This document contains guidance for analyzing statistical data and films. For statistical analysis, it recommends identifying variable levels, calculating descriptive statistics, and testing differences between groups. For film analysis, it advises against simply summarizing plots and instead to analyze themes, images, and how elements work together artistically. It provides tips on developing an argument and using examples to support ideas.
Soc 156 – Sociology of CommunicationReview Sheet – FinalShor.docxwhitneyleman54422
Soc 156 – Sociology of Communication
Review Sheet – Final
Short Essay Questions:
For your final take home exam you will have to answer a total of three of these questions, one from the first five and one from the second five and a third of your choosing from any of the ten.
For your exam you must answer one of this first set of five.
1. What is naturalism and essentialism? Why is it problematic to essentialize gender? How does essentializing gender reproduce and perpetuate stereotypes? What does it mean to say that gender is a social construct that is self-regulated? Further, what does it mean to suggest that gender is “omni-relevant” and that people are “doing gender” constantly? What does this have to do with the concept of performing or communicating gender?
2. According to Mears, how do cultural ideas of race and femininity inform production practices in the fashion modeling market? What are the core differences in representation on the commercial and editorial side of the fashion industry? How does the colorblind ideology (be sure to define the colorblind ideology) reproduce racist practices in the booking of fashion models according to Mears?
3. What is discourse? Why is important to understand the discourses that surround the topic of racism? How, according to Doane, is the discourse on racism connected to the colorblind ideology? (Be sure to define the colorblind ideology.) How is discourse on racism related to the signifying process of “pollution”? How does this process of pollution impact our understanding of racism? Lastly, how would a sociologist describe racism? (Think systemic and institutionalized racism.)
4. What does Goffman mean when he suggests that we perform all of our actions? Further, what does he mean when he suggests we are consistently trying to manage the impressions of those with whom we interact? How can our actions be impacted by the cues delivered to us by our audience? What does this have to do with the concepts of idealization and negative idealization?
5. What does Goffman mean when he suggests that the self is a social construction? What does this have to do with different settings and stages we all perform on? Explain the difference between the front stage and the back stage. Legitimacy and authenticity are connected to congruency; explain what congruency, and its related term defusion, are, how it relates to the front and back stages, and how it can impact an individual’s legitimacy and perceived authenticity.
For your exam must answer one of this second set of five.
6. According to Grindstaff and Murray, what are the societal changes that have helped give rise to reality television? Describe in detail the process with which reality television creates branded affect, serves itself and is intertwined in the emotion economy? Lastly, authenticity is an important prerequisite for reality star success; how is this complicated by the proliferation of agencies that teach you how.
Factory Simulation Paper – Part 2 I know that several of you are.docxmecklenburgstrelitzh
Factory Simulation Paper – Part 2
I know that several of you are concerned about the format and length of this paper since they are not defined. Here are some tips that I have learned about Operations Management that might help.
Two ways that business professionals display information is in a Decision Paper or Information Paper. These are both very similar with the only difference being that the information paper is designed to inform a decision maker and a decision paper is providing the information necessary to make a decision. At this point in your short Operations Manager career I would recommend an Information Paper approach. This way you can specifically inform your boss (the CEO) what you have done to the factory and what the results were.
Here is a typical Information Paper format.
Title Page: With your name, the title of your paper (use something real, not MGT 307), and a date
Body:
Start with an introductory paragraph that informs the boss of what you are trying to tell them.
Bottom Line paragraph that says where you are and what is next. This should be short.
Assumptions paragraph that states any assumptions that you made or will have to make in the future.
Facts and Analysis paragraphs are the meat and potatoes. What did you do? What did you expect to happen? What really did happen? Support these facts with graphics. The graphics can be visual displays of data or tables of data. You choose how best to sell your results. You can do this either as a week by week discussion or by a topic (forecasting, scheduling, etc.).
Summary and conclusion: This set of paragraphs brings all of the highlights to a close.
References – if you need them. I don’t require them unless you specifically call out something that the boss may need to take a deeper look at.
Hopefully this format now allows you to tell your story about the three things that you are being graded on:
1. How did your team’s factory improve over time? Use real metrics to show that your team became more effective and more efficient. 15 points
2. What did your team do and how did it affect your team’s results? Use real metrics to show what happened. 50 points
3. What would you do differently and why? Use information from the book/slides/videos/class to support your comments. 35 points
Good luck!
Factory Process Improvement Update
The First Two Months
Jerry Burch
April 2, 2018
Purpose:
This paper provides information about what we have done over the first two months to improve the factory effectiveness and efficiency. We have several things that have worked well and others that we are still working on.
Bottom Line:
In the first two months we have increased X by Y. We expect this to continue since we still have several projects that are just now beginning to take effect. In particular we anticipate … which will …
Assumptions:
We assumed that continuous improvement processes will be used to improve efficiency and effectiveness without hiring any.
Research
1
Research
Student’s Name
University Affiliation
This is what my professor sent us yesterday through e-mail.
“Your response should look like this:
1. Introduction: Brief description of the study including the purpose and importance of the research question being asked.
Include your response in paragraph form here.
2. What is the null hypothesis? What is the research hypothesis?
Include your response in paragraph form here.
etc.
This format should help you to address EVERY question asked and it helps me in grading.
You should also be defining the statistical test, its requirements, etc and providing an intext citation for this summary. For example: What is a t test? When should it be used? What are the requirements? Why is the particular test appropriate to answer your research hypothesis? “
Research Hypothesis
Students who take breakfast perform better in class that those who don’t (FRAGMENT SENTENCE)
Research question
Does talking breakfast improve the class performance of a student?
Introduction
This research is meant to bring out the relationship between two variables and state how one affects the other. It has a dependent variable as well as independent variables. It should determine whether or not taking breakfast affects the class performance of a student.
Null hypothesis
Talking breakfast does not improve class performance of a student (SPELLING AND PUNCTUATION)
Sampling method
The method used to come up with the sample was the random sampling method. It is because it is unbiased and each unit of the population has an equal chance to be selected and used for the study. There is the assurance that the population will be equally sampled. (REWORD BECAUSE IT IS AWKAWARD)
Sample size
Determination of the right sample size is important because it makes the result of the research more true and reliable. 100 students were selected randomly for the purpose of the research.
Population of interest
The population that was targeted by this research is the entire student body. But since it is not practical to study every unit of the population, the sample was selected to represent the whole population. (YOU CANNOT START THE SENTENCE WITH “BUT” SINCE IT’S A RESEARCH PAPER) The sample size is big enough to represent the real picture depicted by the population under study.
Data analysis
The data was randomly collected from the students in order to create a sample to be used for the study. The data that was collected was analyzed so that to determine if there exists (REWORD) any relationship between breakfast and class performance.
Statistical analysis
This research is to determine the relationship between variables. Correlation refers to how strong two variables are related (Ashley Crossman). (NEEDS TO BE IN PROPER APA FORMAT) Correlation analysis was the most relevant in this case given the fact that the study is to determine the relation between variables. You need to answer this question: Depending on if you are using corr ...
Section 6.26.1) Find the margin of error for the given values of.docxrtodd280
Section 6.2
6.1) Find the margin of error for the given values of c, s, and n.
c=0.8080, s=55, n=21.
6.2) Find the margin of error for the given values of c, s, and n.
c=0.98, s=2.1, n=21.
6.3) Construct the indicated confidence interval for the population mean μ using the t-distribution. Assume the population is normally distributed.
c=0.99, x =13.7 , s=2.0, n=99
The 99% confidence interval using a t-distribution is left parenthesis nothing comma nothing right parenthesis.
6.4) In a random sample of 17people, the mean commute time to work was 31.4 minutes and the standard deviation was 7.3minutes. Assume the population is normally distributed and use a t-distribution to construct a 90% confidence interval for the population mean μ. What is the margin of error of μ? Interpret the results.
6.5) In a random sample of 8 people, the mean commute time to work was 33.5 minutes and the standard deviation was 7.2 minutes. A 90% confidence interval using thet-distribution was calculated to be (28.7,38.3). After researching commute times to work, it was found that the population standard deviation is 9.4minutes. Find the margin of error and construct a 90% confidence interval using the standard normal distribution with the appropriate calculations for a standard deviation that is known. Compare the results.
6.6) The state test scores for 12 randomly selected high school seniors are shown on the right. Complete parts (a) through (c) below. Assume the population is normally distributed.
1428 1222 986
693 720 838
720 741 545
623 1442 942
A) Find the sample mean.
B) Find the standard deviation.
C) A 90% confidence interval for the population mean is ( , ).
1. Summary & Creative elements –costumes (or clothing in a doc) can either enhance the movie or betray its intent. Colors can be vivid and lift the atmosphere or mood in the movie or they can be dull and make it seem depressing. Good sound effects or music enrich the viewing experience while bad ones only destroy everything. Moreover, camera movements and angles also add elements to the story. Take notes of symbols in the story, if any.
1…..2…..3…..4….5
Significance to our class
Make sure you describe instances where the terminology from our readings is shown.
1…..2…..3…..4….5
Make connections between your own research (i.e., your annotated bibliography) and what you observe in the film.
1…..2…..3…..4….5
Describe how your film addresses any of the big questions we looked at the beginning of our class.
1…..2…..3…..4….5
movie review writing tips that may help you:
· Watch the movie twice and take notes of all major and minor details, characters and such on a piece of paper. Don’t rely on your memory only, that way you’d leave out some important details
· Collect the information about the movie through research. Find information about the director, theme, locations, plot, characterization, and other important thi.
BUS 308 Week 2 Lecture 1
Examining Differences - overview
Expected Outcomes
After reading this lecture, the student should be familiar with:
1. The importance of random sampling.
2. The meaning of statistical significance.
3. The basic approach to determining statistical significance.
4. The meaning of the null and alternate hypothesis statements.
5. The hypothesis testing process.
6. The purpose of the F-test and the T-test.
Overview
Last week we collected clues and evidence to help us answer our case question about
males and females getting equal pay for equal work. As we looked at the clues presented by the
salary and comp-ratio measures of pay, things got a bit confusing with results that did not see to
be consistent. We found, among other things, that the male and female compa-ratios were fairly
close together with the female mean being slightly larger. The salary analysis showed a different
view; here we noticed that the averages were apparently quite different with the males, on
average, earning more. Contradictory findings such as this are not all that uncommon when
examining data in the “real world.”
One issue that we could not fully address last week was how meaningful were the
differences? That is, would a different sample have results that might be completely different, or
can we be fairly sure that the observed differences are real and show up in the population as
well? This issue, often referred to as sampling error, deals with the fact that random samples
taken from a population will generally be a bit different than the actual population parameters,
but will be “close” enough to the actual values to be valuable in decision making.
This week, our journey takes us to ways to explore differences, and how significant these
differences are. Just as clues in mysteries are not all equally useful, not all differences are
equally important; and one of the best things statistics will do for us is tell us what differences
we should pay attention to and what we can safely ignore.
Side note; this is a skill that many managers could benefit from. Not all differences in
performances from one period to another are caused by intentional employee actions, some are
due to random variations that employees have no control over. Knowing which differences to
react to would make managers much more effective.
In keeping with our detective theme, this week could be considered the introduction of
the crime scene experts who help detectives interpret what the physical evidence means and how
it can relate to the crime being looked at. We are getting into the support being offered by
experts who interpret details. We need to know how to use these experts to our fullest
advantage. 😊😊
Differences
In general, differences exist in virtually everything we measure that is man-made or
influenced. The underlying issue in statistical analysis is that at times differences are important.
When measu .
BUS 308 Week 2 Lecture 1
Examining Differences - overview
Expected Outcomes
After reading this lecture, the student should be familiar with:
1. The importance of random sampling.
2. The meaning of statistical significance.
3. The basic approach to determining statistical significance.
4. The meaning of the null and alternate hypothesis statements.
5. The hypothesis testing process.
6. The purpose of the F-test and the T-test.
Overview
Last week we collected clues and evidence to help us answer our case question about
males and females getting equal pay for equal work. As we looked at the clues presented by the
salary and comp-ratio measures of pay, things got a bit confusing with results that did not see to
be consistent. We found, among other things, that the male and female compa-ratios were fairly
close together with the female mean being slightly larger. The salary analysis showed a different
view; here we noticed that the averages were apparently quite different with the males, on
average, earning more. Contradictory findings such as this are not all that uncommon when
examining data in the “real world.”
One issue that we could not fully address last week was how meaningful were the
differences? That is, would a different sample have results that might be completely different, or
can we be fairly sure that the observed differences are real and show up in the population as
well? This issue, often referred to as sampling error, deals with the fact that random samples
taken from a population will generally be a bit different than the actual population parameters,
but will be “close” enough to the actual values to be valuable in decision making.
This week, our journey takes us to ways to explore differences, and how significant these
differences are. Just as clues in mysteries are not all equally useful, not all differences are
equally important; and one of the best things statistics will do for us is tell us what differences
we should pay attention to and what we can safely ignore.
Side note; this is a skill that many managers could benefit from. Not all differences in
performances from one period to another are caused by intentional employee actions, some are
due to random variations that employees have no control over. Knowing which differences to
react to would make managers much more effective.
In keeping with our detective theme, this week could be considered the introduction of
the crime scene experts who help detectives interpret what the physical evidence means and how
it can relate to the crime being looked at. We are getting into the support being offered by
experts who interpret details. We need to know how to use these experts to our fullest
advantage. 😊😊
Differences
In general, differences exist in virtually everything we measure that is man-made or
influenced. The underlying issue in statistical analysis is that at times differences are important.
When measu.
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COMM 1001: Week 4 Assignment Worksheet
(Part 1 of your Week 5 Perception Paper)
Directions: Please save the document to your own computer using the naming convention "COMMWK4Assgn+last name+first
initial" as the Submission Title. The file name identifies you and indicates to your instructor that your worksheet is available to grade.
Please fill in the answers in the boxes provided by TYPING in your answers. If you need more space than is provided, the box will
expand as you write. So, no need to worry about space. Do not write your answers in a separate document because your instructor
uses the rubric after each question to grade that section of this worksheet. You may use the rubric as a guide to make sure you
completed that question correctly. Then, please submit this worksheet to the regular Week 4 Assignment submission link in the
classroom.
Section 1. Introduction
Using the directions in the blue part of each box, write an introduction for your week 5 full paper in the boxes below. Be sure to
follow the directions in each box.
First write a sentence (or more if needed) to gradually introduce your reader to the topic of perception. Try to be creative and
original. For instance, you could tell a brief story about how perception played a role in a situation from your own life.
Perception can affect how most of us react to others or give us preconceived notion about others. I tend to base
my perception of other on how I respond to any given situation. The way I was raised, my job duties, and my
personal life all have a great impact on how I perceive others.
The second part of a proper introduction is a thesis or purpose statement. In this worksheet, we will give you
the thesis. In your paper next week, you may choose to use this thesis or write your own. A possible thesis
for this paper would be:
This paper will help me to understand how perception works through doing an analysis of what three observers
declared were their perceptions of a photograph.
Finally, write a sentence or two that previews what your three main points for this paper will be. You have already been given
the three main points. They are:
1) Explanation of the steps of the perception process.
2) Description of your observers and how their background might affect their perception of the world.
3) Analysis of the observers’ descriptions of the photo.
Here is an example of a good preview of these three main points:
In this pa.
Soc 156 – Sociology of CommunicationReview Sheet – FinalShor.docxwhitneyleman54422
Soc 156 – Sociology of Communication
Review Sheet – Final
Short Essay Questions:
For your final take home exam you will have to answer a total of three of these questions, one from the first five and one from the second five and a third of your choosing from any of the ten.
For your exam you must answer one of this first set of five.
1. What is naturalism and essentialism? Why is it problematic to essentialize gender? How does essentializing gender reproduce and perpetuate stereotypes? What does it mean to say that gender is a social construct that is self-regulated? Further, what does it mean to suggest that gender is “omni-relevant” and that people are “doing gender” constantly? What does this have to do with the concept of performing or communicating gender?
2. According to Mears, how do cultural ideas of race and femininity inform production practices in the fashion modeling market? What are the core differences in representation on the commercial and editorial side of the fashion industry? How does the colorblind ideology (be sure to define the colorblind ideology) reproduce racist practices in the booking of fashion models according to Mears?
3. What is discourse? Why is important to understand the discourses that surround the topic of racism? How, according to Doane, is the discourse on racism connected to the colorblind ideology? (Be sure to define the colorblind ideology.) How is discourse on racism related to the signifying process of “pollution”? How does this process of pollution impact our understanding of racism? Lastly, how would a sociologist describe racism? (Think systemic and institutionalized racism.)
4. What does Goffman mean when he suggests that we perform all of our actions? Further, what does he mean when he suggests we are consistently trying to manage the impressions of those with whom we interact? How can our actions be impacted by the cues delivered to us by our audience? What does this have to do with the concepts of idealization and negative idealization?
5. What does Goffman mean when he suggests that the self is a social construction? What does this have to do with different settings and stages we all perform on? Explain the difference between the front stage and the back stage. Legitimacy and authenticity are connected to congruency; explain what congruency, and its related term defusion, are, how it relates to the front and back stages, and how it can impact an individual’s legitimacy and perceived authenticity.
For your exam must answer one of this second set of five.
6. According to Grindstaff and Murray, what are the societal changes that have helped give rise to reality television? Describe in detail the process with which reality television creates branded affect, serves itself and is intertwined in the emotion economy? Lastly, authenticity is an important prerequisite for reality star success; how is this complicated by the proliferation of agencies that teach you how.
Factory Simulation Paper – Part 2 I know that several of you are.docxmecklenburgstrelitzh
Factory Simulation Paper – Part 2
I know that several of you are concerned about the format and length of this paper since they are not defined. Here are some tips that I have learned about Operations Management that might help.
Two ways that business professionals display information is in a Decision Paper or Information Paper. These are both very similar with the only difference being that the information paper is designed to inform a decision maker and a decision paper is providing the information necessary to make a decision. At this point in your short Operations Manager career I would recommend an Information Paper approach. This way you can specifically inform your boss (the CEO) what you have done to the factory and what the results were.
Here is a typical Information Paper format.
Title Page: With your name, the title of your paper (use something real, not MGT 307), and a date
Body:
Start with an introductory paragraph that informs the boss of what you are trying to tell them.
Bottom Line paragraph that says where you are and what is next. This should be short.
Assumptions paragraph that states any assumptions that you made or will have to make in the future.
Facts and Analysis paragraphs are the meat and potatoes. What did you do? What did you expect to happen? What really did happen? Support these facts with graphics. The graphics can be visual displays of data or tables of data. You choose how best to sell your results. You can do this either as a week by week discussion or by a topic (forecasting, scheduling, etc.).
Summary and conclusion: This set of paragraphs brings all of the highlights to a close.
References – if you need them. I don’t require them unless you specifically call out something that the boss may need to take a deeper look at.
Hopefully this format now allows you to tell your story about the three things that you are being graded on:
1. How did your team’s factory improve over time? Use real metrics to show that your team became more effective and more efficient. 15 points
2. What did your team do and how did it affect your team’s results? Use real metrics to show what happened. 50 points
3. What would you do differently and why? Use information from the book/slides/videos/class to support your comments. 35 points
Good luck!
Factory Process Improvement Update
The First Two Months
Jerry Burch
April 2, 2018
Purpose:
This paper provides information about what we have done over the first two months to improve the factory effectiveness and efficiency. We have several things that have worked well and others that we are still working on.
Bottom Line:
In the first two months we have increased X by Y. We expect this to continue since we still have several projects that are just now beginning to take effect. In particular we anticipate … which will …
Assumptions:
We assumed that continuous improvement processes will be used to improve efficiency and effectiveness without hiring any.
Research
1
Research
Student’s Name
University Affiliation
This is what my professor sent us yesterday through e-mail.
“Your response should look like this:
1. Introduction: Brief description of the study including the purpose and importance of the research question being asked.
Include your response in paragraph form here.
2. What is the null hypothesis? What is the research hypothesis?
Include your response in paragraph form here.
etc.
This format should help you to address EVERY question asked and it helps me in grading.
You should also be defining the statistical test, its requirements, etc and providing an intext citation for this summary. For example: What is a t test? When should it be used? What are the requirements? Why is the particular test appropriate to answer your research hypothesis? “
Research Hypothesis
Students who take breakfast perform better in class that those who don’t (FRAGMENT SENTENCE)
Research question
Does talking breakfast improve the class performance of a student?
Introduction
This research is meant to bring out the relationship between two variables and state how one affects the other. It has a dependent variable as well as independent variables. It should determine whether or not taking breakfast affects the class performance of a student.
Null hypothesis
Talking breakfast does not improve class performance of a student (SPELLING AND PUNCTUATION)
Sampling method
The method used to come up with the sample was the random sampling method. It is because it is unbiased and each unit of the population has an equal chance to be selected and used for the study. There is the assurance that the population will be equally sampled. (REWORD BECAUSE IT IS AWKAWARD)
Sample size
Determination of the right sample size is important because it makes the result of the research more true and reliable. 100 students were selected randomly for the purpose of the research.
Population of interest
The population that was targeted by this research is the entire student body. But since it is not practical to study every unit of the population, the sample was selected to represent the whole population. (YOU CANNOT START THE SENTENCE WITH “BUT” SINCE IT’S A RESEARCH PAPER) The sample size is big enough to represent the real picture depicted by the population under study.
Data analysis
The data was randomly collected from the students in order to create a sample to be used for the study. The data that was collected was analyzed so that to determine if there exists (REWORD) any relationship between breakfast and class performance.
Statistical analysis
This research is to determine the relationship between variables. Correlation refers to how strong two variables are related (Ashley Crossman). (NEEDS TO BE IN PROPER APA FORMAT) Correlation analysis was the most relevant in this case given the fact that the study is to determine the relation between variables. You need to answer this question: Depending on if you are using corr ...
Section 6.26.1) Find the margin of error for the given values of.docxrtodd280
Section 6.2
6.1) Find the margin of error for the given values of c, s, and n.
c=0.8080, s=55, n=21.
6.2) Find the margin of error for the given values of c, s, and n.
c=0.98, s=2.1, n=21.
6.3) Construct the indicated confidence interval for the population mean μ using the t-distribution. Assume the population is normally distributed.
c=0.99, x =13.7 , s=2.0, n=99
The 99% confidence interval using a t-distribution is left parenthesis nothing comma nothing right parenthesis.
6.4) In a random sample of 17people, the mean commute time to work was 31.4 minutes and the standard deviation was 7.3minutes. Assume the population is normally distributed and use a t-distribution to construct a 90% confidence interval for the population mean μ. What is the margin of error of μ? Interpret the results.
6.5) In a random sample of 8 people, the mean commute time to work was 33.5 minutes and the standard deviation was 7.2 minutes. A 90% confidence interval using thet-distribution was calculated to be (28.7,38.3). After researching commute times to work, it was found that the population standard deviation is 9.4minutes. Find the margin of error and construct a 90% confidence interval using the standard normal distribution with the appropriate calculations for a standard deviation that is known. Compare the results.
6.6) The state test scores for 12 randomly selected high school seniors are shown on the right. Complete parts (a) through (c) below. Assume the population is normally distributed.
1428 1222 986
693 720 838
720 741 545
623 1442 942
A) Find the sample mean.
B) Find the standard deviation.
C) A 90% confidence interval for the population mean is ( , ).
1. Summary & Creative elements –costumes (or clothing in a doc) can either enhance the movie or betray its intent. Colors can be vivid and lift the atmosphere or mood in the movie or they can be dull and make it seem depressing. Good sound effects or music enrich the viewing experience while bad ones only destroy everything. Moreover, camera movements and angles also add elements to the story. Take notes of symbols in the story, if any.
1…..2…..3…..4….5
Significance to our class
Make sure you describe instances where the terminology from our readings is shown.
1…..2…..3…..4….5
Make connections between your own research (i.e., your annotated bibliography) and what you observe in the film.
1…..2…..3…..4….5
Describe how your film addresses any of the big questions we looked at the beginning of our class.
1…..2…..3…..4….5
movie review writing tips that may help you:
· Watch the movie twice and take notes of all major and minor details, characters and such on a piece of paper. Don’t rely on your memory only, that way you’d leave out some important details
· Collect the information about the movie through research. Find information about the director, theme, locations, plot, characterization, and other important thi.
BUS 308 Week 2 Lecture 1
Examining Differences - overview
Expected Outcomes
After reading this lecture, the student should be familiar with:
1. The importance of random sampling.
2. The meaning of statistical significance.
3. The basic approach to determining statistical significance.
4. The meaning of the null and alternate hypothesis statements.
5. The hypothesis testing process.
6. The purpose of the F-test and the T-test.
Overview
Last week we collected clues and evidence to help us answer our case question about
males and females getting equal pay for equal work. As we looked at the clues presented by the
salary and comp-ratio measures of pay, things got a bit confusing with results that did not see to
be consistent. We found, among other things, that the male and female compa-ratios were fairly
close together with the female mean being slightly larger. The salary analysis showed a different
view; here we noticed that the averages were apparently quite different with the males, on
average, earning more. Contradictory findings such as this are not all that uncommon when
examining data in the “real world.”
One issue that we could not fully address last week was how meaningful were the
differences? That is, would a different sample have results that might be completely different, or
can we be fairly sure that the observed differences are real and show up in the population as
well? This issue, often referred to as sampling error, deals with the fact that random samples
taken from a population will generally be a bit different than the actual population parameters,
but will be “close” enough to the actual values to be valuable in decision making.
This week, our journey takes us to ways to explore differences, and how significant these
differences are. Just as clues in mysteries are not all equally useful, not all differences are
equally important; and one of the best things statistics will do for us is tell us what differences
we should pay attention to and what we can safely ignore.
Side note; this is a skill that many managers could benefit from. Not all differences in
performances from one period to another are caused by intentional employee actions, some are
due to random variations that employees have no control over. Knowing which differences to
react to would make managers much more effective.
In keeping with our detective theme, this week could be considered the introduction of
the crime scene experts who help detectives interpret what the physical evidence means and how
it can relate to the crime being looked at. We are getting into the support being offered by
experts who interpret details. We need to know how to use these experts to our fullest
advantage. 😊😊
Differences
In general, differences exist in virtually everything we measure that is man-made or
influenced. The underlying issue in statistical analysis is that at times differences are important.
When measu .
BUS 308 Week 2 Lecture 1
Examining Differences - overview
Expected Outcomes
After reading this lecture, the student should be familiar with:
1. The importance of random sampling.
2. The meaning of statistical significance.
3. The basic approach to determining statistical significance.
4. The meaning of the null and alternate hypothesis statements.
5. The hypothesis testing process.
6. The purpose of the F-test and the T-test.
Overview
Last week we collected clues and evidence to help us answer our case question about
males and females getting equal pay for equal work. As we looked at the clues presented by the
salary and comp-ratio measures of pay, things got a bit confusing with results that did not see to
be consistent. We found, among other things, that the male and female compa-ratios were fairly
close together with the female mean being slightly larger. The salary analysis showed a different
view; here we noticed that the averages were apparently quite different with the males, on
average, earning more. Contradictory findings such as this are not all that uncommon when
examining data in the “real world.”
One issue that we could not fully address last week was how meaningful were the
differences? That is, would a different sample have results that might be completely different, or
can we be fairly sure that the observed differences are real and show up in the population as
well? This issue, often referred to as sampling error, deals with the fact that random samples
taken from a population will generally be a bit different than the actual population parameters,
but will be “close” enough to the actual values to be valuable in decision making.
This week, our journey takes us to ways to explore differences, and how significant these
differences are. Just as clues in mysteries are not all equally useful, not all differences are
equally important; and one of the best things statistics will do for us is tell us what differences
we should pay attention to and what we can safely ignore.
Side note; this is a skill that many managers could benefit from. Not all differences in
performances from one period to another are caused by intentional employee actions, some are
due to random variations that employees have no control over. Knowing which differences to
react to would make managers much more effective.
In keeping with our detective theme, this week could be considered the introduction of
the crime scene experts who help detectives interpret what the physical evidence means and how
it can relate to the crime being looked at. We are getting into the support being offered by
experts who interpret details. We need to know how to use these experts to our fullest
advantage. 😊😊
Differences
In general, differences exist in virtually everything we measure that is man-made or
influenced. The underlying issue in statistical analysis is that at times differences are important.
When measu.
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COMM 1001: Week 4 Assignment Worksheet
(Part 1 of your Week 5 Perception Paper)
Directions: Please save the document to your own computer using the naming convention "COMMWK4Assgn+last name+first
initial" as the Submission Title. The file name identifies you and indicates to your instructor that your worksheet is available to grade.
Please fill in the answers in the boxes provided by TYPING in your answers. If you need more space than is provided, the box will
expand as you write. So, no need to worry about space. Do not write your answers in a separate document because your instructor
uses the rubric after each question to grade that section of this worksheet. You may use the rubric as a guide to make sure you
completed that question correctly. Then, please submit this worksheet to the regular Week 4 Assignment submission link in the
classroom.
Section 1. Introduction
Using the directions in the blue part of each box, write an introduction for your week 5 full paper in the boxes below. Be sure to
follow the directions in each box.
First write a sentence (or more if needed) to gradually introduce your reader to the topic of perception. Try to be creative and
original. For instance, you could tell a brief story about how perception played a role in a situation from your own life.
Perception can affect how most of us react to others or give us preconceived notion about others. I tend to base
my perception of other on how I respond to any given situation. The way I was raised, my job duties, and my
personal life all have a great impact on how I perceive others.
The second part of a proper introduction is a thesis or purpose statement. In this worksheet, we will give you
the thesis. In your paper next week, you may choose to use this thesis or write your own. A possible thesis
for this paper would be:
This paper will help me to understand how perception works through doing an analysis of what three observers
declared were their perceptions of a photograph.
Finally, write a sentence or two that previews what your three main points for this paper will be. You have already been given
the three main points. They are:
1) Explanation of the steps of the perception process.
2) Description of your observers and how their background might affect their perception of the world.
3) Analysis of the observers’ descriptions of the photo.
Here is an example of a good preview of these three main points:
In this pa.
Please fill the attached Self-Assessment Surveys (TWO) and calcula.docxARIV4
Please fill the attached Self-Assessment Surveys (TWO) and calculate your score according to the instruction after each survey. These are personal assessments and I want you to be as honest as possible, rather than worry about what I am going to think.
1. AM I A DELIBERATE DECISION MAKER?
Indicate to what extent the following statements describe you when you make decisions.
1 = to a very little extent; 2 = to a little extent; 3 = somewhat; 4 = to a large extent; 5 = to a very large extent
1
2
3
4
5
1. I jump into things without thinking.
2. I make rash decisions.
3. I like to act on a whim.
4. I rush into things.
5. I don’t know why I do some of the things I do.
6. I act quickly without thinking.
7. I choose my words with care.
Instructions:
To score the measure, first reverse-code items 1, 2, 3, 4, 5, and 6. So that 1=5, 2=4, 3=3, 4=2, and 5=1. Then compute the sum of the 7 items. Scores will range from 7 to 35.
Interpretation
People differ in how they make decisions. Some people prefer to collect information, carefully weigh alternatives, and then select the best option, while others prefer to make a choice as quickly as possible.
This scale assesses how deliberate you are when making decisions. If you scored at or above 28, you tend to be quite deliberate. If you scored at or below 14, you tend to be rash. Scores between 14 and 27 reveal a more blended style of decision making.
How should decisions be made? The rational model states that individuals should define the problem, identify what criteria are relevant to making the decision and weigh those criteria according to importance, develop alternatives, and finally evaluate and select the best alternative. Though this sounds like an arduous process, research has shown that the rational model tends to result in better decisions.
Interestingly, personality is related to a person’s decision-making style. Individuals who are deliberate and decisive tend to be high in emotional stability and high in conscientiousness, while individuals who are more impulsive tend to be low on these two traits. Thus, while your decision-making style is likely to be somewhat stable, following the rational model should help you to avoid making rash decisions.
2. HOW CREATIVE AM I?
Review the 30 adjectives that follow. Being honest and forthright with your answers, identify only those items that accurately describe you.
1. Affected
2. Capable
3. Cautious
4. Clever
5. Commonplace
6. Confident
7. Conservative
8. Conventional
9. Dissatisfied
10. Egotistical
11. Honest
12. Humorous
13. Individualistic
14. Informal
15. Insightful
16. Intelligent
17. Inventive
18. Mannerly
19. Narrow Interests
20. Original
21. Reflective
22. Resourceful
23. Self-confident
24. Sexy
25. Sincere
26. Snobbish
27. Submissive
28. Suspicious
29. Unconventional
30. Wide Interests
Instructions:
The score was calculated by adding 1 point if you descr.
Please fill the attached Self-Assessment Surveys (TWO) and calcula.docxstilliegeorgiana
Please fill the attached Self-Assessment Surveys (TWO) and calculate your score according to the instruction after each survey. These are personal assessments and I want you to be as honest as possible, rather than worry about what I am going to think.
1. AM I A DELIBERATE DECISION MAKER?
Indicate to what extent the following statements describe you when you make decisions.
1 = to a very little extent; 2 = to a little extent; 3 = somewhat; 4 = to a large extent; 5 = to a very large extent
1
2
3
4
5
1. I jump into things without thinking.
2. I make rash decisions.
3. I like to act on a whim.
4. I rush into things.
5. I don’t know why I do some of the things I do.
6. I act quickly without thinking.
7. I choose my words with care.
Instructions:
To score the measure, first reverse-code items 1, 2, 3, 4, 5, and 6. So that 1=5, 2=4, 3=3, 4=2, and 5=1. Then compute the sum of the 7 items. Scores will range from 7 to 35.
Interpretation
People differ in how they make decisions. Some people prefer to collect information, carefully weigh alternatives, and then select the best option, while others prefer to make a choice as quickly as possible.
This scale assesses how deliberate you are when making decisions. If you scored at or above 28, you tend to be quite deliberate. If you scored at or below 14, you tend to be rash. Scores between 14 and 27 reveal a more blended style of decision making.
How should decisions be made? The rational model states that individuals should define the problem, identify what criteria are relevant to making the decision and weigh those criteria according to importance, develop alternatives, and finally evaluate and select the best alternative. Though this sounds like an arduous process, research has shown that the rational model tends to result in better decisions.
Interestingly, personality is related to a person’s decision-making style. Individuals who are deliberate and decisive tend to be high in emotional stability and high in conscientiousness, while individuals who are more impulsive tend to be low on these two traits. Thus, while your decision-making style is likely to be somewhat stable, following the rational model should help you to avoid making rash decisions.
2. HOW CREATIVE AM I?
Review the 30 adjectives that follow. Being honest and forthright with your answers, identify only those items that accurately describe you.
1. Affected
2. Capable
3. Cautious
4. Clever
5. Commonplace
6. Confident
7. Conservative
8. Conventional
9. Dissatisfied
10. Egotistical
11. Honest
12. Humorous
13. Individualistic
14. Informal
15. Insightful
16. Intelligent
17. Inventive
18. Mannerly
19. Narrow Interests
20. Original
21. Reflective
22. Resourceful
23. Self-confident
24. Sexy
25. Sincere
26. Snobbish
27. Submissive
28. Suspicious
29. Unconventional
30. Wide Interests
Instructions:
The score was calculated by adding 1 point if you descr ...
Essay #3 Social Media EssayHow does social media (and your use .docxSALU18
Essay #3: Social Media Essay
How does social media (and your use of devices which allow you to access social media) affect your life, your relationships with friends and family, your experience and performance at school?
Write an essay in which you describe your experience with social media (either direct experience or what you've observed in friends/family). Discuss the effects of social media, and try to arrive at some conclusions that sum up your thoughts and feelings on this subject.
In your discussion, refer briefly to one or two of the authors we have read on this topic, using a relevant quotation (or paraphrase or summary). Incorporating ideas from these experts lets the reader know that you are a serious person and are aware of what other people are saying about this topic. It's also to give you some points to agree or disagree with. However, the focus of the essay should be on your ideas. This one is about you. The quotations or references to other authors are just to lend you a little credibility. (For examples, look at how Shirky and Pinker refer briefly to other published works.)
This is a reflective essay, not an argumentative essay, so you do not have to try to convince your reader to agree with your point of view. Cruser's "Cropped Out of My Own Fantasy" is probably the best example of the kind of essay you are meant to write, though yours should be more of an academic essay rather than a newspaper article.
For this essay, "social media" can refer to such services as Facebook, Twitter, Instagram, and so on, which are used to communicate and share information, ideas, updates, and life events with friends.
(Not a social media user? If you do not use social media, or if you were previously a user and have given it up, write about that. What is behind your decision not to engage? Did something happen? What is your reasoning? How does your non-use of social media affect your relationships with other people who do use it? Do you feel more or less connected, or differently connected? Explain. What are your thoughts about social media?)
Do not try to say everything you can think of about every kind of social media.
Do not simply summarize your experiences with social media. Choose examples for specific reasons.
Do not simply make a catalogue of "positive effects" and "negative effects."
Think about your experiences and think about what they mean. Use a couple of illustrative examples that you can describe in some detail and explore and explain. You might even choose just one major experience you have had (the time my life was saved/ruined by something that happened on Twitter!) and use that experience as a way into a meaningful exploration and discussion. Again: Cruser's "Cropped Out of My Own Fantasy" is probably the best example of the kind of essay you are meant to write, though yours should be more of an academic essay rather than a newspaper article.
Your grade will be based largely on the quality of your thinking. If you stick wi ...
· Self-Assessment· InterpretationValues and Moral Survey of StLesleyWhitesidefv
· Self-Assessment
· Interpretation
Values and Moral Survey of Students
SCORING AND INTERPRETATION: If trying to rank in order these fifteen values and morals was difficult and you felt that it was somewhat arbitrary; it was. Without the proper context, exact meaning of each, and the problem of one concept being like others, everyone gets frustrated with this exercise. To be sure, this exercise does not tell you what your real morals or values are. Rather, it is a crude representation of what they might be. At the end of the simulation, compare what you ranked as important to what your decisions were. The following are vague descriptions of the fifteen values and morals:
ASSISTANCE: The act of helping or assisting someone or the help supplied.
CANDOR: Freedom from prejudice or malice.
CHARACTER: Someone with moral excellence.
CHARITY: Generosity and helpfulness, especially toward the needy or suffering.
COMPASSION: Sympathetic consciousness of others' distress with a desire to alleviate it.
ENVIRONMENT: Concern about the world's resources (land, water, air).
EXACTING IN TRUTH: Rigid or severe in demands or requirements.
FAIRNESS: Free from bias or injustice; evenhandedness.
HONESTY: Sincerity, frankness, freedom from deceit or fraud.
INTEGRITY: Firm adherence to a code of values; incorruptibility.
PERSEVERANCE: To do or achieve something despite difficulties, failure, or opposition.
SACRIFICE: Surrender of something for the sake of something else.
SERIOUSNESS: Thoughtful in appearance or manner; requiring much thought or work relating to a matter of importance.
TOLERANCE: Sympathy or indulgence for beliefs or practices differing from or conflicting with one's own.
TRUTHFULNESS: Corresponding with reality.
ASSISTANCE Results = 2
Think about a recent action you took with regard to an ethical situation. Was your behavior influenced by your values in the order you have reflected them here?
ASSISTANCE Analytics
What Would You Do? Problem 1
SCORING AND INTERPRETATION: There are no right or wrong answers to these questions, but your answers reveal your moral philosophy.
Moral Philosophy Results = 31
Moral Philosophy Analytics
Ethical Decision-Making Framework Model
Assessment
Summary
Alternatives
Analysis
Application
Action
Notes
Ethical Assessment #1
Ethical Assessment #2
Ethical Assessment #3
Week One Summary
Week Two Summary
Week Three Summary
Week Four Summary
Week Five Summary
Week Six Summary
Week Seven Summary
Week Eight Summary
Instructions: Below (on page 2) is a sample of the template data to assist you in your creative thinking for week one! On the weekly ethics portfolio, you are welcome to submit it along with the week one assignment, however it is not required. It is a note taking template. I highly encourage everyone to submit it each week, as this helps to keep you on track, but again, it is not required. You will use the template note-taking document to ...
35878 Topic Discussion5Number of Pages 1 (Double Spaced).docxrhetttrevannion
35878 Topic: Discussion5
Number of Pages: 1 (Double Spaced)
Number of sources: 1
Writing Style: APA
Type of document: Essay
Academic Level:Master
Category: Psychology
Language Style: English (U.S.)
Order Instructions: Attached
I will attach the instruction
Please follow them carefully
General Business Page 9
Unit 4
Due Wed 12/12
800-1,000 words / these will be turned into slides and added to your key assignment.
Study the following document: Methods for Managing Differences. Assume this communication strategy has been recommended by your employer for mediation when working with potential and existing business clients and partners.
Consider that there are basically two distinct types of cultures. One type is more cooperative, and the other is more competitive. It has been discovered that there are some conflicts occurring between some of the key players who need to come to agreement on specific critical areas of the deal for it to move forward. The top management would really like this deal to happen.
Imagine being in this situation, and create the scenario as you go through the process using the methods approach from above.
· Describe the steps you would take and any considerations along the way.
· How would you use the recommended method when working with individuals who exhibit a generally competitive culture?
· How would you use the recommended method when working with individuals who exhibit a generally cooperative culture?
· Would this cultural factor change the way you apply this method for managing differences? Why or why not? Explain.
Create Section 4 of your Key Assignment presentation: Global Negotiations. Refer to Unit 1 Discussion Board 2 for a description of this section. Submit a draft of your entire presentation for your instructor to review.
Discussion 2: Discuss, elaborate and give example on the topic below. Please use only the reference I attach. Please be careful with grammar and spelling. No running head Please.
Author: Jackson, S.L. (2017). Statistics Plain and Simple (4th ed.): Cengage Learning
Topic
Review this week’s course materials and learning activities, and reflect on your learning so far this week. Respond to one or more of the following prompts in one to two paragraphs:
1. Provide citation and reference to the material(s) you discuss. Describe what you found interesting regarding this topic, and why.
2. Describe how you will apply that learning in your daily life, including your work life.
3. Describe what may be unclear to you, and what you would like to learn.
Reference:
Module 9: The Single-Sample z Test
The z Test: What It Is and What It Does
The Sampling Distribution
The Standard Error of the Mean
Calculations for the One-Tailed z Test
Interpreting the One-Tailed z Test
Calculations for the Two-Tailed z Test
Interpreting the Two-Tailed z Test
Statistical Power
Assumptions and Appropriate Use of the z Test
Confidence Intervals Based on the z Distribution
Review of Key Term.
COMM 1001 Week 4 Assignment Worksheet(Part 1 of your Week 5 P.docxclarebernice
COMM 1001: Week 4 Assignment Worksheet
(Part 1 of your Week 5 Perception Paper)
Directions: Please save the document to your own computer using thenaming convention "COMMWK4Assgn+last name+first initial" as the Submission Title. The file name identifies you and indicates to your instructor that your worksheet is available to grade. Please fill in the answers in the boxes provided by TYPING in your answers. If you need more space than is provided, the box will expand as you write. So, no need to worry about space. Do not write your answers in a separate document because your instructor uses the rubric after each question to grade that section of this worksheet. You may use the rubric as a guide to make sure you completed that question correctly. Then, please submit this worksheet to the regular Week 4 Assignment submission link in the classroom.
Section 1. Introduction
Using the directions in the blue part of each box, write an introduction for your week 5 full paper in the boxes below. Be sure to follow the directions in each box.
First write a sentence (or more if needed) to gradually introduce your reader to the topic of perception. Try to be creative and original. For instance, you could tell a brief story about how perception played a role in a situation from your own life.
Perception is to know and understand something through the ability to hear, see, or become aware of something through the senses. It can influence how most people react towards others. My perception is mainly based on how a person sitting down on grass can be perceived through the sense of sight. This paper will guide me to know how perception works through analyzing what three observers stated in their perceptions of a photograph.
The second part of a proper introduction is a thesis or purpose statement. In this worksheet, we will give you
the thesis. In your paper next week, you may choose to use this thesis or write your own. A possible thesis
for this paper would be:
This paper will help me to understand how perception works through doing an analysis of what three observers
declared were their perceptions of a photograph.
Finally, write a sentence or two that previews what your three main points for this paper will be. You have already been given the three main points. They are:
1) Explanation of the steps of the perception process.
2) Description of your observers and how their background might affect their perception of the world.
3) Analysis of the observers’ descriptions of the photo.
Here is an example of a good preview of these three main points:
In this paper, first I will explain the three step process of perception; second, I will provide a description of my three observers complete with an explanation as to how their backgrounds might affect their perceptions of the world. Finally, I will delve deeper into what these observers had to say about the photograph I showed them by analyzing their perceptions.
In space below, write a preview ...
COMM 1001 Week 4 Assignment Worksheet(Part 1 of your Week 5 P.docxmonicafrancis71118
COMM 1001: Week 4 Assignment Worksheet
(Part 1 of your Week 5 Perception Paper)
Directions: Please save the document to your own computer using thenaming convention "COMMWK4Assgn+last name+first initial" as the Submission Title. The file name identifies you and indicates to your instructor that your worksheet is available to grade. Please fill in the answers in the boxes provided by TYPING in your answers. If you need more space than is provided, the box will expand as you write. So, no need to worry about space. Do not write your answers in a separate document because your instructor uses the rubric after each question to grade that section of this worksheet. You may use the rubric as a guide to make sure you completed that question correctly. Then, please submit this worksheet to the regular Week 4 Assignment submission link in the classroom.
Section 1. Introduction
Using the directions in the blue part of each box, write an introduction for your week 5 full paper in the boxes below. Be sure to follow the directions in each box.
First write a sentence (or more if needed) to gradually introduce your reader to the topic of perception. Try to be creative and original. For instance, you could tell a brief story about how perception played a role in a situation from your own life.
Perception is to know and understand something through the ability to hear, see, or become aware of something through the senses. It can influence how most people react towards others. My perception is mainly based on how a person sitting down on grass can be perceived through the sense of sight. This paper will guide me to know how perception works through analyzing what three observers stated in their perceptions of a photograph.
The second part of a proper introduction is a thesis or purpose statement. In this worksheet, we will give you
the thesis. In your paper next week, you may choose to use this thesis or write your own. A possible thesis
for this paper would be:
This paper will help me to understand how perception works through doing an analysis of what three observers
declared were their perceptions of a photograph.
Finally, write a sentence or two that previews what your three main points for this paper will be. You have already been given the three main points. They are:
1) Explanation of the steps of the perception process.
2) Description of your observers and how their background might affect their perception of the world.
3) Analysis of the observers’ descriptions of the photo.
Here is an example of a good preview of these three main points:
In this paper, first I will explain the three step process of perception; second, I will provide a description of my three observers complete with an explanation as to how their backgrounds might affect their perceptions of the world. Finally, I will delve deeper into what these observers had to say about the photograph I showed them by analyzing their perceptions.
In space below, write a preview .
Introduction to experimental designsPH2600 2019Neil O’TatianaMajor22
Introduction to experimental
designs
PH2600 2019
Neil O’Connell
Learning outcomes
By the end of the lecture students should
be able to:
Describe basic common experimental
study designs
Consider some of the biases that
attempt we control for
Describe the basic purpose and
structure of a systematic review
A bottom line
The choice of design should
arise from the research
question - not the other way
around.
Experimental design - definition
In which one (or more) variable(s) is
manipulated and the effect of this
manipulation is observed in other
variables.
It aims to control all other variables.
It allows us to infer causality
Causality
If there is change to A does a change
in B result?
◦ Cause must precede the effect
◦ The cause and effect must co-vary
◦ If the cause does not occur then neither
does the effect
Inferring causation - problems
Confounding
Regression to the mean
Natural recovery
Placebo/ non-specific effects
Hawthorne Effect (Observer)
Rosenthal Effect (Experimenter
expectancy)
Time itself is a
confounder
Se
ve
ri
ty
Time
Se
ve
ri
ty
Time
Control group
By including a group who undergo the
same conditions (except…) as the
experimental group we control for
numerous possible confounders
For within-subjects designs this might
be a control condition
Blinding
Why conceal the identity of
the experimental condition?
◦ A function of placebo groups -
‘sham’ interventions
◦ Single blind
◦ Double-blind
◦ Triple Blind
◦ What confounders might
blinding control for?
Who can
we blind
in trials
of PT?
Group designs – within or
between subjects
Within Group design
One group of
participants receives
all experimental
conditions (including
control)
Offers paired data
Between-Group
Design
Different groups
receive the different
experimental
conditions
Offers unpaired data
Designs
Randomised controlled experiment.
Parallel, cross-over, factorial
Controlled experiment
Quasi experimental study
Single group pre-test post-test
design
Group before
Same group
after
IN
T
ERV
EN
T
IO
N
Time series design
IN
T
ER
V
EN
T
IO
N
measure measure
Basic parallel experimental design (pre
test-post test)
Experimental
group
INTERVENTION
CONTROL Follow up
Follow up
Control group
Pre
test
Post test
SAME
POPULATION
TAKE
BASELINE
MEASURES
INTERVENTION
CONTROL FOLLOW UP
FOLLOW UP
Pre
test
Post test
How to ensure the groups are
the same?
Matching groups
Or
Use the same group for the different
conditions
Or
Randomisation
RANDOMISATION
The beauty of randomisation
It solves all your problems (maybe)!
In NRS you can only control for known
confounders
Successful randomisation controls for
all
Even imbalances at baseline occur at
random and are unsystematic biases.
RA Fisher (1935)
“Randomisation
relieves the
experimenter from the
anxiety of considering
and estimating ...
BUS308 – Week 1 Lecture 2 Describing Data Expected Out.docxcurwenmichaela
BUS308 – Week 1 Lecture 2
Describing Data
Expected Outcomes
After reading this lecture, the student should be familiar with:
1. Basic descriptive statistics for data location
2. Basic descriptive statistics for data consistency
3. Basic descriptive statistics for data position
4. Basic approaches for describing likelihood
5. Difference between descriptive and inferential statistics
What this lecture covers
This lecture focuses on describing data and how these descriptions can be used in an
analysis. It also introduces and defines some specific descriptive statistical tools and results.
Even if we never become a data detective or do statistical tests, we will be exposed and
bombarded with statistics and statistical outcomes. We need to understand what they are telling
us and how they help uncover what the data means on the “crime,” AKA research question/issue.
How we obtain these results will be covered in lecture 1-3.
Detecting
In our favorite detective shows, starting out always seems difficult. They have a crime,
but no real clues or suspects, no idea of what happened, no “theory of the crime,” etc. Much as
we are at this point with our question on equal pay for equal work.
The process followed is remarkably similar across the different shows. First, a case or
situation presents itself. The heroes start by understanding the background of the situation and
those involved. They move on to collecting clues and following hints, some of which do not pan
out to be helpful. They then start to build relationships between and among clues and facts,
tossing out ideas that seemed good but lead to dead-ends or non-helpful insights (false leads,
etc.). Finally, a conclusion is reached and the initial question of “who done it” is solved.
Data analysis, and specifically statistical analysis, is done quite the same way as we will
see.
Descriptive Statistics
Week 1 Clues
We are interested in whether or not males and females are paid the same for doing equal
work. So, how do we go about answering this question? The “victim” in this question could be
considered the difference in pay between males and females, specifically when they are doing
equal work. An initial examination (Doc, was it murder or an accident?) involves obtaining
basic information to see if we even have cause to worry.
The first action in any analysis involves collecting the data. This generally involves
conducting a random sample from the population of employees so that we have a manageable
data set to operate from. In this case, our sample, presented in Lecture 1, gave us 25 males and
25 females spread throughout the company. A quick look at the sample by HR provided us with
assurance that the group looked representative of the company workforce we are concerned with
as a whole. Now we can confidently collect clues to see if we should be concerned or not.
As with any detective, the first issue is to understand the.
You clearly understand the concepts of this assignment. You’ve don.docxjeffevans62972
You clearly understand the concepts of this assignment. You’ve done an excellent job answering the problems correctly. You’ve demonstrated a clear understanding of stats and their application to this assignment. You read your diagrams and explained the results correctly, and your formulaic work at the end is right on target. You have also written a very clean, narrative document.
Be sure to look at the formatting of your sources. Be sure to always use credible sources to back your work. This is so important when it comes to academic and scholarly work. Please see my comments throughout the paper. That’s really where the advice ends regarding things you should work on, because you have demonstrated you have no problems with the content.
Knowing these concepts, and progressing even more toward an academic writing style, will help you as you move forward personally and professionally. Being able to translate numbers into a sharp narrative document will make you a go-to person in the workplace, and it will provide confidence in everything you do. Good work on this assignment.
Chapter Seven
Problem 1) Look at the scatterplot below. Does it demonstrate a positive or negative correlation? Why?
Are there any outliers? What are they?
The scatterplot is an example of a positive correlation, the outlier in the scatterplot is 6.00. A ; “Outliners are a set of data, a value so far removed from other values in the distribution that its presence cannot be attributed to the random combination of chance causes” (http://www.statcan.gc.ca/,2013)scatterplot is considered positive when the point runs from the lower left to the upper right such as the circles shown on the example
.
Problem 2) Look at the scatterplot below. Does it demonstrate a positive or negative correlation? Why?
Are there any outliers? What are they?
The scatter plot is the opposite of example one, it is actually a negative correlation
because the points run from the upper left to the lower right. As with example one there is an outer liner which is 6.00 as well, it does not fall within line with the other points.
Problem 3) The following data come from your book, problem 26 on page 298. Here is the data:
Mean daily calories Infant Mortality Rate (per 1,000 births)
1523 154
3495 6
1941 114
2678 24
1610 107
3443 6
1640 153
3362 7
3429 44
2671 7
For the above data construct a scatterplot using SPSS or Excel (Follow instructions on page 324 of your textbook). What does the scatterplot show? Can you determine a type of relationship? Are there any outliers that you can see?
Mean daily calories
Infant Mortality Rate
(per 1,000 births)
1523
154
3495
6
1941
114
2678
24
1610
107
3443
6
1640
153
3362
7
3429
44
2671
7
Infant Mortality Rate (per 1,000 births)
0
20
40
60
80
100
120
140
160
180
020004000
Infant Mortality
Rate (per 1,000
births)
The scatter plot demonstrates that there is a significant reverence b.
Correlation Analysis Paper
Self Analysis Example
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ENGL 101
Essay 1: Narrative Argument Grading Rubric
Essential Requirements for Grading:
The next two columns refer to the deviation scores squared, since summing up deviation scores always equals zero and is of no use when determining variance.
Again, can you complete the cells for the last two employees?
Finally, the last column refers to the product of multiplying the deviation of X by the deviation of Y shown here as xy. Again, fill in the last two rows to check your understanding.
Now we are ready to calculate our correlation, r.
The following general categories indicate a quick way of interpreting a calculated r value (+ or –):
· 0.0 to 0.2: very weak to negligible correlation
· 0.2 to 0.4: weak, low correlation (not very significant)
· 0.4 to 0.7: moderate correlation
· 0.7 to 0.9: strong, high correlation
· 0.9 to 1.0: very strong correlation
Using the above guide the consulting psychologist would advise the human resource manager to conclude there is a weak relationship between years of employment and pay raises, and that the relationship is positive.
Caution must be exercised at this juncture as the relationship determined cannot as of yet be considered statistically significant.
Knowing that the situation included 10 individuals (N = 10: number of participants and degrees of freedom equal N – 2: two variables) to determine whether the results are statistically significant one must go to a "r" value table and determine what value is needed for statistical significance. In this case where the degrees of freedom are 8 (N – 2), the required r value at the probability level of 0.05 is +/- 0.549. The obtained r value in this case of + 0.36 is less than the required r value; therefore, the relationship is not statistically significant at the 0.05 probability level. Further, the consulting psychologist can conclude as well that the null hypothesis is accepted, and the relationship was most likely due to chance factors.
The same data cited above can be placed in the raw score formula, and the exact same r value will result. Note: Should an r value exceed +/– 1.0, the conclusion drawn is simply that a mathematical mistake has been made in the calculation process.
Post a behavioral research situation that could use a Pearson coefficient research study and a chi square research study. Present the rationale for each selection. Be very specific in your presentation.
Format in APA style. 2 pages
Writing Components:
Organization: Introduction, Thesis, Transitions, Conclusion
Usage and Mechanics: Grammar, Spelling, Sentence structure
APA Elements: Attribution, Paraphrasing, Quotations
Style: Audience, Word Choice
100
Total 100
EDUCATION
2
Education: What does it mean to be Well-educated?
Thesis statement
To be well-educated means to attain the highest level of education for that individual
.
Abstrac ...
3Type your name hereType your three-letter and -number cours.docxlorainedeserre
3
Type your name here
Type your three-letter and -number course code here
The date goes here
Type instructor’s name here
Your Title Goes Here
This is an electronic template for papers written in GCU style. The purpose of the template is to help you follow the basic writing expectations for beginning your coursework at GCU. Margins are set at 1 inch for top, bottom, left, and right. The first line of each paragraph is indented a half inch (0.5"). The line spacing is double throughout the paper, even on the reference page. One space after punctuation is used at the end of a sentence. The font style used in this template is Times New Roman. The font size is 12 point. When you are ready to write, and after having read these instructions completely, you can delete these directions and start typing. The formatting should stay the same. If you have any questions, please consult with your instructor.
Citations are used to reference material from another source. When paraphrasing material from another source (such as a book, journal, website), include the author’s last name and the publication year in parentheses.When directly quoting material word-for-word from another source, use quotation marks and include the page number after the author’s last name and year.
Using citations to give credit to others whose ideas or words you have used is an essential requirement to avoid issues of plagiarism. Just as you would never steal someone else’s car, you should not steal his or her words either. To avoid potential problems, always be sure to cite your sources. Cite by referring to the author’s last name, the year of publication in parentheses at the end of the sentence, such as (George & Mallery, 2016), and page numbers if you are using word-for-word materials. For example, “The developments of the World War II years firmly established the probability sample survey as a tool for describing population characteristics, beliefs, and attitudes” (Heeringa, West, & Berglund, 2017, p. 3).
The reference list should appear at the end of a paper (see the next page). It provides the information necessary for a reader to locate and retrieve any source you cite in the body of the paper. Each source you cite in the paper must appear in your reference list; likewise, each entry in the reference list must be cited in your text. A sample reference page is included below; this page includes examples (George & Mallery, 2016; Heeringa et al., 2017; Smith et al., 2018; “USA swimming,” 2018; Yu, Johnson, Deutsch, & Varga, 2018) of how to format different reference types (e.g., books, journal articles, and a website). For additional examples, see the GCU Style Guide.
References
George, D., & Mallery, P. (2016). IBM SPSS statistics 23 step by step: A simple guide and reference. New York, NY: Routledge.
Heeringa, S. G., West, B. T., & Berglund, P. A. (2017). Applied survey data analysis (2nd ed.). New York, NY: Chapman & Hall/CRC Press.
Smith, P. D., Martin, B., Chewning, B., ...
Project 2: Research Paper Compendium
Choose what you consider to be a monster or monstrosity –
literal
figurative (ideology, practice)
historical
cryptozoology
Examples:
mythology
invention
Vlad Tepes
Joseph Stalin
Pablo Escobar
Nazis
Biological Weapons
Assault Rifles
Adolf Hitler
the Ku Klux Klan
Dylan Roof
Griselda Blanco
Aileen Wuornos
Fred & Rosemary West
Mark Twitchell
Jeffrey Dahmer
Long Island Serial Killer
Jack the Ripper
Jim Jones/Jonestown
Bigfoot
Loch Ness Monster
the Hydra
Slender Man
Michael Myers
Ed Gein
Freddy Krueger
Slavery
Human Trafficking
the Drug Trade
Drug Addiction
Rwandan Genocide
Pol Pot’s Khmer Rouge
Aurora shooting
Sandy Hook
Lizzie Borden
Saddam Hussein
Heaven’s Gate Cult
Baba Yaga
the Holocaust
Balkan Genocide
the list goes on…
Write an 8 to 9 page research paper in which you are the expert on this monster/monstrosity. Both your paper and your expert presentation will reflect the biography/origin; timeline of actions/atrocities; cultural/societal impact; how this subject is depicted/sensationalized through various writings/the media (stories, biographies, scholarly articles, comics, graphic novels, poems, movies, interviews, folklore/fairy tails, television shows, et cetera); and why this monster/monstrosity has meaning to you. The paper must also include
7-8 annotated bibliography entries (I have attatched a document to show what it is).
Jamal Sampson's paper has to focus on the two monsters listed:
Saddam Hussein
Osama Bin Laden
.
Project 1 Interview Essay Conduct a brief interview with an Asian.docxdessiechisomjj4
Project 1: Interview Essay
Conduct a brief interview with an Asian immigrant to ask about their immigration story and push-pull factors. This can last 5-15 minutes. Then, write a 2 paragraphs on the DB.
You do
not
have to include the person’s real name! Immigration status is a sensitive topic, so please understand if someone does not want to be interviewed. Students have interviewed friends, family members, people in their community, and other students.
Project 1: Prompt
1.
Brief facts:
Around what age did they immigrate? How old are they now (in my 30s is acceptable)? What push-pull factors led them to immigrate to the U.S.? (You may have to explain what push-pull factors are.)
2. Add your own comments/perspective and perhaps even your own immigration story. What aspects of their story did you find interesting or surprising? What aspects were familiar to you?
Example:
I conducted a 10 minute interview with my neighbor "Dr. Villanueva" who immigrated to the U.S. over 45 years ago at the age of 26. I asked him about his push and pull factors. What reasons did he have for leaving his home country and why did he choose the U.S. as his new home? He stated that he wanted to leave the Philippines for a better life and more opportunities. He had grown up as the youngest of nine children and was very poor, but was able to study medicine and become a medical doctor specializing in ophthalmology. He heard that the U.S. was encouraging medical professionals to work there especially if they were fluent in English. According to our reading "Filipinos in America," (Lee 2015) the Philippines was a colony of the U.S. from 1898-1945 and English was taught in the education system (Lee, p. 90). Plus, many Filipinos then and still today dream about immigrating to the United States to improve their educational and financial opportunities. Dr. Villanueva came to the U.S. after the 1965 Immigration and Nationality Act abolished national quotas but limited immigration from Asia to educated professionals. When I asked if he felt that he experienced discrimination, Dr. Villanueva said yes, many times, but overall he is glad that he immigrated because his children had so many more opportunities in the U.S. Often, people still think that he is a foreigner or can't speak English. There have been a few occasions that people directed racial slurs at him, but he has not experienced any physical harm.
Dr. Villanueva seems to fit much of the data on Asian Americans that we studied in this class. However, I noticed some ways that he did not. For example, {etc....} Dr. Villanueva's story is much different than my grandparents' story who immigrated from __ and did not have college degrees when they arrived. [ADD YOUR PERSONAL REFLECTIONS ON THE INTERVIEW.]
.
Project 1 Scenario There is a Top Secret intelligence report.docxdessiechisomjj4
Project 1:
Scenario
: There is a Top Secret intelligence report that a terrorist organization based in the Middle East is planning to plant a dirty bomb in the inner harbor of major American city in the next 48 hours. The report has not been officially released or the classification reduced. You (the student) are the Chief of Police of this major metro city and do not have a security clearance at this time. The inner harbor is a major tourist attraction, a major shipping port and home to many international shipping companies, trade zones and military and federal government facilities.
You have heard the report exists but have not seen it. As the Police Chief of (you choose the city e.g. Baltimore, New York, Miami, Los Angeles, San Diego, Seattle etc) you have many questions about the report and many different agencies you will want to coordinate with. You will identify the real Homeland Security, LE and Intelligence organizations within the jurisdiction of the city you have chosen.
Requirement:
Write a minimum 1000 word paper (double space, 12 Font, New Times Roman) explaining how you would deal with this yet unseen report.
What actions would you take upon hearing of this report?
What Federal, state, local or government agencies would want to contact?
What questions would you want to ask about this report?
If it were true who would you want to share it with? Can you share it? What factors (e.g. legal, operational, public safety) might impede sharing this information?
Address
at least ten
of the concepts listed below within your paper:
Dissemination
Differentiate between intelligence and information
Intelligence products
Strategic versus tactical intelligence
Information sharing
Jurisdiction
Security classifications
Public safety
Intelligence roles
Federal versus local, state, and/or tribal
Target identification
Media/Hollywood portrayals
Database security/security of data
Value of intelligence
Domain awareness
Intelligence gap
Collection plans
Reliability, viability, and validity
Security clearances
.
Project #1 Personal Reflection (10)Consider an opinion that you .docxdessiechisomjj4
Project #1: Personal Reflection (10%)
Consider an opinion that you hold dearly. Write a brief reflection on the genealogy of your opinion. This can include personal experience, upbringing, social influence, media analysis, philosophy, anything that’s helped you form your opinion.
Purpose: I want you to start thinking about your process as a thinker. We can’t improve our processes in the future without understanding what we’ve done in the past.
Length: 1-3 pages
Format: MLA, 12 point Times New Roman font, 1 inch margins
.
Project 1 Chinese Dialect Exploration and InterviewYou will nee.docxdessiechisomjj4
Project 1: Chinese Dialect Exploration and Interview
You will need to cite references whenever you get the information from an article or from some online resources. In the written report, you need to include the following:
Title: An Exploration of [Dialect Name (spoken
where
)]
1.
Introduction
Introduce the geography of the dialect and which particular dialect variant you are focusing on. Give basic introduction about how many people are using this dialect and its current situation. Provide a map to indicate the dialectal grouping and the location of the speakers of the dialect.
2.
Linguistic Features of [Dialect Name (spoken
where
)]
Explore the following topics and introduce the
differences between this dialect and Standard Chinese (Mandarin)
in an organized and systematic way.
·
Syllable structure
·
Initial consonants
·
Finals (Rhymes)
·
Medials
·
Basic tones
·
Tone changes (optional: you get additional points if you explore this one)
·
Lexical or syntactic differences
To be able to do this section, you need to find resources online or from the library that reliably analyzed a dialect and systematically introduces this dialect or a dialect closely related to it. At the end of this linguistic description, summarize the speech features of speakers of this dialect when s/he uses Standard Chinese. What features do you expect a speaker of this dialect may carry into Standard Chinese? Are the differences going to be drastic enough to be detectable?
3.
Method:
In this section, you introduce the linguistic and social background of your interviewee(s).
1.
Informant Background:
Personal profile (gender, age, relevant linguistic and educational history, family background) [Have your interviewee fill out a linguistic background form provided by Prof. Lin]
2.
Setting (time and location of the interview, how was it documented?)
4.
Findings: Sociolinguistic aspect of the dialect according to the interview
You will present the interview results in an organized way. You should discuss the following issues related to the dialect:
·
What is the status of the particular dialect in relation to Mandarin? Discuss the issues related to diglossia (high versus low varieties). What are the social functions of the dialects? When do people use them and when do they not use them but opt for other languages and dialects? Compare the different uses of different dialects or speech variants.
·
Ask your interviewee his or her experiences with “accents”. How do people sound if they have accents? Do people using the dialects carry a special accent speaking Mandarin? How are people with accents perceived? Are there social stigma, attitudes, and identity issues associated with the dialect? How are people speaking this dialect usually perceived? Why do you think there are these social meanings that go with the accented speech?
·
How has this dialect changed in recent years, which may be associated with the above social political properties?
5.
Online.
Project 1 (1-2 pages)What are the employee workplace rights mand.docxdessiechisomjj4
Project 1 (1-2 pages)
What are the employee workplace rights mandated by U.S. Federal law?
Briefly discuss at least two controversial issues concerning workplace rights (other than monitoring e-mail). Provide real-life examples to illustrate your answer.
In addition, discuss the issue of workplace privacy. Specifically, do employees have the right to expect privacy in their e-mail conversations, or do companies have a right and/or responsibility to monitor e-mail?
Project 2 (1-2 pages)
Draft a performance action plan for a company to follow when providing discipline in response to complaints of sexual harassment. Use the Library or other Web resources if needed.
Please submit your assignment.
.
More Related Content
Similar to Statistical Calculations 5Statistical Calculations.docx
Please fill the attached Self-Assessment Surveys (TWO) and calcula.docxARIV4
Please fill the attached Self-Assessment Surveys (TWO) and calculate your score according to the instruction after each survey. These are personal assessments and I want you to be as honest as possible, rather than worry about what I am going to think.
1. AM I A DELIBERATE DECISION MAKER?
Indicate to what extent the following statements describe you when you make decisions.
1 = to a very little extent; 2 = to a little extent; 3 = somewhat; 4 = to a large extent; 5 = to a very large extent
1
2
3
4
5
1. I jump into things without thinking.
2. I make rash decisions.
3. I like to act on a whim.
4. I rush into things.
5. I don’t know why I do some of the things I do.
6. I act quickly without thinking.
7. I choose my words with care.
Instructions:
To score the measure, first reverse-code items 1, 2, 3, 4, 5, and 6. So that 1=5, 2=4, 3=3, 4=2, and 5=1. Then compute the sum of the 7 items. Scores will range from 7 to 35.
Interpretation
People differ in how they make decisions. Some people prefer to collect information, carefully weigh alternatives, and then select the best option, while others prefer to make a choice as quickly as possible.
This scale assesses how deliberate you are when making decisions. If you scored at or above 28, you tend to be quite deliberate. If you scored at or below 14, you tend to be rash. Scores between 14 and 27 reveal a more blended style of decision making.
How should decisions be made? The rational model states that individuals should define the problem, identify what criteria are relevant to making the decision and weigh those criteria according to importance, develop alternatives, and finally evaluate and select the best alternative. Though this sounds like an arduous process, research has shown that the rational model tends to result in better decisions.
Interestingly, personality is related to a person’s decision-making style. Individuals who are deliberate and decisive tend to be high in emotional stability and high in conscientiousness, while individuals who are more impulsive tend to be low on these two traits. Thus, while your decision-making style is likely to be somewhat stable, following the rational model should help you to avoid making rash decisions.
2. HOW CREATIVE AM I?
Review the 30 adjectives that follow. Being honest and forthright with your answers, identify only those items that accurately describe you.
1. Affected
2. Capable
3. Cautious
4. Clever
5. Commonplace
6. Confident
7. Conservative
8. Conventional
9. Dissatisfied
10. Egotistical
11. Honest
12. Humorous
13. Individualistic
14. Informal
15. Insightful
16. Intelligent
17. Inventive
18. Mannerly
19. Narrow Interests
20. Original
21. Reflective
22. Resourceful
23. Self-confident
24. Sexy
25. Sincere
26. Snobbish
27. Submissive
28. Suspicious
29. Unconventional
30. Wide Interests
Instructions:
The score was calculated by adding 1 point if you descr.
Please fill the attached Self-Assessment Surveys (TWO) and calcula.docxstilliegeorgiana
Please fill the attached Self-Assessment Surveys (TWO) and calculate your score according to the instruction after each survey. These are personal assessments and I want you to be as honest as possible, rather than worry about what I am going to think.
1. AM I A DELIBERATE DECISION MAKER?
Indicate to what extent the following statements describe you when you make decisions.
1 = to a very little extent; 2 = to a little extent; 3 = somewhat; 4 = to a large extent; 5 = to a very large extent
1
2
3
4
5
1. I jump into things without thinking.
2. I make rash decisions.
3. I like to act on a whim.
4. I rush into things.
5. I don’t know why I do some of the things I do.
6. I act quickly without thinking.
7. I choose my words with care.
Instructions:
To score the measure, first reverse-code items 1, 2, 3, 4, 5, and 6. So that 1=5, 2=4, 3=3, 4=2, and 5=1. Then compute the sum of the 7 items. Scores will range from 7 to 35.
Interpretation
People differ in how they make decisions. Some people prefer to collect information, carefully weigh alternatives, and then select the best option, while others prefer to make a choice as quickly as possible.
This scale assesses how deliberate you are when making decisions. If you scored at or above 28, you tend to be quite deliberate. If you scored at or below 14, you tend to be rash. Scores between 14 and 27 reveal a more blended style of decision making.
How should decisions be made? The rational model states that individuals should define the problem, identify what criteria are relevant to making the decision and weigh those criteria according to importance, develop alternatives, and finally evaluate and select the best alternative. Though this sounds like an arduous process, research has shown that the rational model tends to result in better decisions.
Interestingly, personality is related to a person’s decision-making style. Individuals who are deliberate and decisive tend to be high in emotional stability and high in conscientiousness, while individuals who are more impulsive tend to be low on these two traits. Thus, while your decision-making style is likely to be somewhat stable, following the rational model should help you to avoid making rash decisions.
2. HOW CREATIVE AM I?
Review the 30 adjectives that follow. Being honest and forthright with your answers, identify only those items that accurately describe you.
1. Affected
2. Capable
3. Cautious
4. Clever
5. Commonplace
6. Confident
7. Conservative
8. Conventional
9. Dissatisfied
10. Egotistical
11. Honest
12. Humorous
13. Individualistic
14. Informal
15. Insightful
16. Intelligent
17. Inventive
18. Mannerly
19. Narrow Interests
20. Original
21. Reflective
22. Resourceful
23. Self-confident
24. Sexy
25. Sincere
26. Snobbish
27. Submissive
28. Suspicious
29. Unconventional
30. Wide Interests
Instructions:
The score was calculated by adding 1 point if you descr ...
Essay #3 Social Media EssayHow does social media (and your use .docxSALU18
Essay #3: Social Media Essay
How does social media (and your use of devices which allow you to access social media) affect your life, your relationships with friends and family, your experience and performance at school?
Write an essay in which you describe your experience with social media (either direct experience or what you've observed in friends/family). Discuss the effects of social media, and try to arrive at some conclusions that sum up your thoughts and feelings on this subject.
In your discussion, refer briefly to one or two of the authors we have read on this topic, using a relevant quotation (or paraphrase or summary). Incorporating ideas from these experts lets the reader know that you are a serious person and are aware of what other people are saying about this topic. It's also to give you some points to agree or disagree with. However, the focus of the essay should be on your ideas. This one is about you. The quotations or references to other authors are just to lend you a little credibility. (For examples, look at how Shirky and Pinker refer briefly to other published works.)
This is a reflective essay, not an argumentative essay, so you do not have to try to convince your reader to agree with your point of view. Cruser's "Cropped Out of My Own Fantasy" is probably the best example of the kind of essay you are meant to write, though yours should be more of an academic essay rather than a newspaper article.
For this essay, "social media" can refer to such services as Facebook, Twitter, Instagram, and so on, which are used to communicate and share information, ideas, updates, and life events with friends.
(Not a social media user? If you do not use social media, or if you were previously a user and have given it up, write about that. What is behind your decision not to engage? Did something happen? What is your reasoning? How does your non-use of social media affect your relationships with other people who do use it? Do you feel more or less connected, or differently connected? Explain. What are your thoughts about social media?)
Do not try to say everything you can think of about every kind of social media.
Do not simply summarize your experiences with social media. Choose examples for specific reasons.
Do not simply make a catalogue of "positive effects" and "negative effects."
Think about your experiences and think about what they mean. Use a couple of illustrative examples that you can describe in some detail and explore and explain. You might even choose just one major experience you have had (the time my life was saved/ruined by something that happened on Twitter!) and use that experience as a way into a meaningful exploration and discussion. Again: Cruser's "Cropped Out of My Own Fantasy" is probably the best example of the kind of essay you are meant to write, though yours should be more of an academic essay rather than a newspaper article.
Your grade will be based largely on the quality of your thinking. If you stick wi ...
· Self-Assessment· InterpretationValues and Moral Survey of StLesleyWhitesidefv
· Self-Assessment
· Interpretation
Values and Moral Survey of Students
SCORING AND INTERPRETATION: If trying to rank in order these fifteen values and morals was difficult and you felt that it was somewhat arbitrary; it was. Without the proper context, exact meaning of each, and the problem of one concept being like others, everyone gets frustrated with this exercise. To be sure, this exercise does not tell you what your real morals or values are. Rather, it is a crude representation of what they might be. At the end of the simulation, compare what you ranked as important to what your decisions were. The following are vague descriptions of the fifteen values and morals:
ASSISTANCE: The act of helping or assisting someone or the help supplied.
CANDOR: Freedom from prejudice or malice.
CHARACTER: Someone with moral excellence.
CHARITY: Generosity and helpfulness, especially toward the needy or suffering.
COMPASSION: Sympathetic consciousness of others' distress with a desire to alleviate it.
ENVIRONMENT: Concern about the world's resources (land, water, air).
EXACTING IN TRUTH: Rigid or severe in demands or requirements.
FAIRNESS: Free from bias or injustice; evenhandedness.
HONESTY: Sincerity, frankness, freedom from deceit or fraud.
INTEGRITY: Firm adherence to a code of values; incorruptibility.
PERSEVERANCE: To do or achieve something despite difficulties, failure, or opposition.
SACRIFICE: Surrender of something for the sake of something else.
SERIOUSNESS: Thoughtful in appearance or manner; requiring much thought or work relating to a matter of importance.
TOLERANCE: Sympathy or indulgence for beliefs or practices differing from or conflicting with one's own.
TRUTHFULNESS: Corresponding with reality.
ASSISTANCE Results = 2
Think about a recent action you took with regard to an ethical situation. Was your behavior influenced by your values in the order you have reflected them here?
ASSISTANCE Analytics
What Would You Do? Problem 1
SCORING AND INTERPRETATION: There are no right or wrong answers to these questions, but your answers reveal your moral philosophy.
Moral Philosophy Results = 31
Moral Philosophy Analytics
Ethical Decision-Making Framework Model
Assessment
Summary
Alternatives
Analysis
Application
Action
Notes
Ethical Assessment #1
Ethical Assessment #2
Ethical Assessment #3
Week One Summary
Week Two Summary
Week Three Summary
Week Four Summary
Week Five Summary
Week Six Summary
Week Seven Summary
Week Eight Summary
Instructions: Below (on page 2) is a sample of the template data to assist you in your creative thinking for week one! On the weekly ethics portfolio, you are welcome to submit it along with the week one assignment, however it is not required. It is a note taking template. I highly encourage everyone to submit it each week, as this helps to keep you on track, but again, it is not required. You will use the template note-taking document to ...
35878 Topic Discussion5Number of Pages 1 (Double Spaced).docxrhetttrevannion
35878 Topic: Discussion5
Number of Pages: 1 (Double Spaced)
Number of sources: 1
Writing Style: APA
Type of document: Essay
Academic Level:Master
Category: Psychology
Language Style: English (U.S.)
Order Instructions: Attached
I will attach the instruction
Please follow them carefully
General Business Page 9
Unit 4
Due Wed 12/12
800-1,000 words / these will be turned into slides and added to your key assignment.
Study the following document: Methods for Managing Differences. Assume this communication strategy has been recommended by your employer for mediation when working with potential and existing business clients and partners.
Consider that there are basically two distinct types of cultures. One type is more cooperative, and the other is more competitive. It has been discovered that there are some conflicts occurring between some of the key players who need to come to agreement on specific critical areas of the deal for it to move forward. The top management would really like this deal to happen.
Imagine being in this situation, and create the scenario as you go through the process using the methods approach from above.
· Describe the steps you would take and any considerations along the way.
· How would you use the recommended method when working with individuals who exhibit a generally competitive culture?
· How would you use the recommended method when working with individuals who exhibit a generally cooperative culture?
· Would this cultural factor change the way you apply this method for managing differences? Why or why not? Explain.
Create Section 4 of your Key Assignment presentation: Global Negotiations. Refer to Unit 1 Discussion Board 2 for a description of this section. Submit a draft of your entire presentation for your instructor to review.
Discussion 2: Discuss, elaborate and give example on the topic below. Please use only the reference I attach. Please be careful with grammar and spelling. No running head Please.
Author: Jackson, S.L. (2017). Statistics Plain and Simple (4th ed.): Cengage Learning
Topic
Review this week’s course materials and learning activities, and reflect on your learning so far this week. Respond to one or more of the following prompts in one to two paragraphs:
1. Provide citation and reference to the material(s) you discuss. Describe what you found interesting regarding this topic, and why.
2. Describe how you will apply that learning in your daily life, including your work life.
3. Describe what may be unclear to you, and what you would like to learn.
Reference:
Module 9: The Single-Sample z Test
The z Test: What It Is and What It Does
The Sampling Distribution
The Standard Error of the Mean
Calculations for the One-Tailed z Test
Interpreting the One-Tailed z Test
Calculations for the Two-Tailed z Test
Interpreting the Two-Tailed z Test
Statistical Power
Assumptions and Appropriate Use of the z Test
Confidence Intervals Based on the z Distribution
Review of Key Term.
COMM 1001 Week 4 Assignment Worksheet(Part 1 of your Week 5 P.docxclarebernice
COMM 1001: Week 4 Assignment Worksheet
(Part 1 of your Week 5 Perception Paper)
Directions: Please save the document to your own computer using thenaming convention "COMMWK4Assgn+last name+first initial" as the Submission Title. The file name identifies you and indicates to your instructor that your worksheet is available to grade. Please fill in the answers in the boxes provided by TYPING in your answers. If you need more space than is provided, the box will expand as you write. So, no need to worry about space. Do not write your answers in a separate document because your instructor uses the rubric after each question to grade that section of this worksheet. You may use the rubric as a guide to make sure you completed that question correctly. Then, please submit this worksheet to the regular Week 4 Assignment submission link in the classroom.
Section 1. Introduction
Using the directions in the blue part of each box, write an introduction for your week 5 full paper in the boxes below. Be sure to follow the directions in each box.
First write a sentence (or more if needed) to gradually introduce your reader to the topic of perception. Try to be creative and original. For instance, you could tell a brief story about how perception played a role in a situation from your own life.
Perception is to know and understand something through the ability to hear, see, or become aware of something through the senses. It can influence how most people react towards others. My perception is mainly based on how a person sitting down on grass can be perceived through the sense of sight. This paper will guide me to know how perception works through analyzing what three observers stated in their perceptions of a photograph.
The second part of a proper introduction is a thesis or purpose statement. In this worksheet, we will give you
the thesis. In your paper next week, you may choose to use this thesis or write your own. A possible thesis
for this paper would be:
This paper will help me to understand how perception works through doing an analysis of what three observers
declared were their perceptions of a photograph.
Finally, write a sentence or two that previews what your three main points for this paper will be. You have already been given the three main points. They are:
1) Explanation of the steps of the perception process.
2) Description of your observers and how their background might affect their perception of the world.
3) Analysis of the observers’ descriptions of the photo.
Here is an example of a good preview of these three main points:
In this paper, first I will explain the three step process of perception; second, I will provide a description of my three observers complete with an explanation as to how their backgrounds might affect their perceptions of the world. Finally, I will delve deeper into what these observers had to say about the photograph I showed them by analyzing their perceptions.
In space below, write a preview ...
COMM 1001 Week 4 Assignment Worksheet(Part 1 of your Week 5 P.docxmonicafrancis71118
COMM 1001: Week 4 Assignment Worksheet
(Part 1 of your Week 5 Perception Paper)
Directions: Please save the document to your own computer using thenaming convention "COMMWK4Assgn+last name+first initial" as the Submission Title. The file name identifies you and indicates to your instructor that your worksheet is available to grade. Please fill in the answers in the boxes provided by TYPING in your answers. If you need more space than is provided, the box will expand as you write. So, no need to worry about space. Do not write your answers in a separate document because your instructor uses the rubric after each question to grade that section of this worksheet. You may use the rubric as a guide to make sure you completed that question correctly. Then, please submit this worksheet to the regular Week 4 Assignment submission link in the classroom.
Section 1. Introduction
Using the directions in the blue part of each box, write an introduction for your week 5 full paper in the boxes below. Be sure to follow the directions in each box.
First write a sentence (or more if needed) to gradually introduce your reader to the topic of perception. Try to be creative and original. For instance, you could tell a brief story about how perception played a role in a situation from your own life.
Perception is to know and understand something through the ability to hear, see, or become aware of something through the senses. It can influence how most people react towards others. My perception is mainly based on how a person sitting down on grass can be perceived through the sense of sight. This paper will guide me to know how perception works through analyzing what three observers stated in their perceptions of a photograph.
The second part of a proper introduction is a thesis or purpose statement. In this worksheet, we will give you
the thesis. In your paper next week, you may choose to use this thesis or write your own. A possible thesis
for this paper would be:
This paper will help me to understand how perception works through doing an analysis of what three observers
declared were their perceptions of a photograph.
Finally, write a sentence or two that previews what your three main points for this paper will be. You have already been given the three main points. They are:
1) Explanation of the steps of the perception process.
2) Description of your observers and how their background might affect their perception of the world.
3) Analysis of the observers’ descriptions of the photo.
Here is an example of a good preview of these three main points:
In this paper, first I will explain the three step process of perception; second, I will provide a description of my three observers complete with an explanation as to how their backgrounds might affect their perceptions of the world. Finally, I will delve deeper into what these observers had to say about the photograph I showed them by analyzing their perceptions.
In space below, write a preview .
Introduction to experimental designsPH2600 2019Neil O’TatianaMajor22
Introduction to experimental
designs
PH2600 2019
Neil O’Connell
Learning outcomes
By the end of the lecture students should
be able to:
Describe basic common experimental
study designs
Consider some of the biases that
attempt we control for
Describe the basic purpose and
structure of a systematic review
A bottom line
The choice of design should
arise from the research
question - not the other way
around.
Experimental design - definition
In which one (or more) variable(s) is
manipulated and the effect of this
manipulation is observed in other
variables.
It aims to control all other variables.
It allows us to infer causality
Causality
If there is change to A does a change
in B result?
◦ Cause must precede the effect
◦ The cause and effect must co-vary
◦ If the cause does not occur then neither
does the effect
Inferring causation - problems
Confounding
Regression to the mean
Natural recovery
Placebo/ non-specific effects
Hawthorne Effect (Observer)
Rosenthal Effect (Experimenter
expectancy)
Time itself is a
confounder
Se
ve
ri
ty
Time
Se
ve
ri
ty
Time
Control group
By including a group who undergo the
same conditions (except…) as the
experimental group we control for
numerous possible confounders
For within-subjects designs this might
be a control condition
Blinding
Why conceal the identity of
the experimental condition?
◦ A function of placebo groups -
‘sham’ interventions
◦ Single blind
◦ Double-blind
◦ Triple Blind
◦ What confounders might
blinding control for?
Who can
we blind
in trials
of PT?
Group designs – within or
between subjects
Within Group design
One group of
participants receives
all experimental
conditions (including
control)
Offers paired data
Between-Group
Design
Different groups
receive the different
experimental
conditions
Offers unpaired data
Designs
Randomised controlled experiment.
Parallel, cross-over, factorial
Controlled experiment
Quasi experimental study
Single group pre-test post-test
design
Group before
Same group
after
IN
T
ERV
EN
T
IO
N
Time series design
IN
T
ER
V
EN
T
IO
N
measure measure
Basic parallel experimental design (pre
test-post test)
Experimental
group
INTERVENTION
CONTROL Follow up
Follow up
Control group
Pre
test
Post test
SAME
POPULATION
TAKE
BASELINE
MEASURES
INTERVENTION
CONTROL FOLLOW UP
FOLLOW UP
Pre
test
Post test
How to ensure the groups are
the same?
Matching groups
Or
Use the same group for the different
conditions
Or
Randomisation
RANDOMISATION
The beauty of randomisation
It solves all your problems (maybe)!
In NRS you can only control for known
confounders
Successful randomisation controls for
all
Even imbalances at baseline occur at
random and are unsystematic biases.
RA Fisher (1935)
“Randomisation
relieves the
experimenter from the
anxiety of considering
and estimating ...
BUS308 – Week 1 Lecture 2 Describing Data Expected Out.docxcurwenmichaela
BUS308 – Week 1 Lecture 2
Describing Data
Expected Outcomes
After reading this lecture, the student should be familiar with:
1. Basic descriptive statistics for data location
2. Basic descriptive statistics for data consistency
3. Basic descriptive statistics for data position
4. Basic approaches for describing likelihood
5. Difference between descriptive and inferential statistics
What this lecture covers
This lecture focuses on describing data and how these descriptions can be used in an
analysis. It also introduces and defines some specific descriptive statistical tools and results.
Even if we never become a data detective or do statistical tests, we will be exposed and
bombarded with statistics and statistical outcomes. We need to understand what they are telling
us and how they help uncover what the data means on the “crime,” AKA research question/issue.
How we obtain these results will be covered in lecture 1-3.
Detecting
In our favorite detective shows, starting out always seems difficult. They have a crime,
but no real clues or suspects, no idea of what happened, no “theory of the crime,” etc. Much as
we are at this point with our question on equal pay for equal work.
The process followed is remarkably similar across the different shows. First, a case or
situation presents itself. The heroes start by understanding the background of the situation and
those involved. They move on to collecting clues and following hints, some of which do not pan
out to be helpful. They then start to build relationships between and among clues and facts,
tossing out ideas that seemed good but lead to dead-ends or non-helpful insights (false leads,
etc.). Finally, a conclusion is reached and the initial question of “who done it” is solved.
Data analysis, and specifically statistical analysis, is done quite the same way as we will
see.
Descriptive Statistics
Week 1 Clues
We are interested in whether or not males and females are paid the same for doing equal
work. So, how do we go about answering this question? The “victim” in this question could be
considered the difference in pay between males and females, specifically when they are doing
equal work. An initial examination (Doc, was it murder or an accident?) involves obtaining
basic information to see if we even have cause to worry.
The first action in any analysis involves collecting the data. This generally involves
conducting a random sample from the population of employees so that we have a manageable
data set to operate from. In this case, our sample, presented in Lecture 1, gave us 25 males and
25 females spread throughout the company. A quick look at the sample by HR provided us with
assurance that the group looked representative of the company workforce we are concerned with
as a whole. Now we can confidently collect clues to see if we should be concerned or not.
As with any detective, the first issue is to understand the.
You clearly understand the concepts of this assignment. You’ve don.docxjeffevans62972
You clearly understand the concepts of this assignment. You’ve done an excellent job answering the problems correctly. You’ve demonstrated a clear understanding of stats and their application to this assignment. You read your diagrams and explained the results correctly, and your formulaic work at the end is right on target. You have also written a very clean, narrative document.
Be sure to look at the formatting of your sources. Be sure to always use credible sources to back your work. This is so important when it comes to academic and scholarly work. Please see my comments throughout the paper. That’s really where the advice ends regarding things you should work on, because you have demonstrated you have no problems with the content.
Knowing these concepts, and progressing even more toward an academic writing style, will help you as you move forward personally and professionally. Being able to translate numbers into a sharp narrative document will make you a go-to person in the workplace, and it will provide confidence in everything you do. Good work on this assignment.
Chapter Seven
Problem 1) Look at the scatterplot below. Does it demonstrate a positive or negative correlation? Why?
Are there any outliers? What are they?
The scatterplot is an example of a positive correlation, the outlier in the scatterplot is 6.00. A ; “Outliners are a set of data, a value so far removed from other values in the distribution that its presence cannot be attributed to the random combination of chance causes” (http://www.statcan.gc.ca/,2013)scatterplot is considered positive when the point runs from the lower left to the upper right such as the circles shown on the example
.
Problem 2) Look at the scatterplot below. Does it demonstrate a positive or negative correlation? Why?
Are there any outliers? What are they?
The scatter plot is the opposite of example one, it is actually a negative correlation
because the points run from the upper left to the lower right. As with example one there is an outer liner which is 6.00 as well, it does not fall within line with the other points.
Problem 3) The following data come from your book, problem 26 on page 298. Here is the data:
Mean daily calories Infant Mortality Rate (per 1,000 births)
1523 154
3495 6
1941 114
2678 24
1610 107
3443 6
1640 153
3362 7
3429 44
2671 7
For the above data construct a scatterplot using SPSS or Excel (Follow instructions on page 324 of your textbook). What does the scatterplot show? Can you determine a type of relationship? Are there any outliers that you can see?
Mean daily calories
Infant Mortality Rate
(per 1,000 births)
1523
154
3495
6
1941
114
2678
24
1610
107
3443
6
1640
153
3362
7
3429
44
2671
7
Infant Mortality Rate (per 1,000 births)
0
20
40
60
80
100
120
140
160
180
020004000
Infant Mortality
Rate (per 1,000
births)
The scatter plot demonstrates that there is a significant reverence b.
Correlation Analysis Paper
Self Analysis Example
Essay On Survey Analysis
Textual Analysis Essay example
Art Analysis Essay
Examples Of Semiotic Analysis
Examples Of Discourse Analysis
Organizational Analysis Essay examples
Examples Of Thematic Analysis
Dream Analysis Essay
Marketing Analysis Essay
Genre Analysis Example
Introductory Paragraph Analysis
Presentation Analysis Essay examples
Essay on Self-Analysis
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Examples Of Genre Analysis
ENGL 101
Essay 1: Narrative Argument Grading Rubric
Essential Requirements for Grading:
The next two columns refer to the deviation scores squared, since summing up deviation scores always equals zero and is of no use when determining variance.
Again, can you complete the cells for the last two employees?
Finally, the last column refers to the product of multiplying the deviation of X by the deviation of Y shown here as xy. Again, fill in the last two rows to check your understanding.
Now we are ready to calculate our correlation, r.
The following general categories indicate a quick way of interpreting a calculated r value (+ or –):
· 0.0 to 0.2: very weak to negligible correlation
· 0.2 to 0.4: weak, low correlation (not very significant)
· 0.4 to 0.7: moderate correlation
· 0.7 to 0.9: strong, high correlation
· 0.9 to 1.0: very strong correlation
Using the above guide the consulting psychologist would advise the human resource manager to conclude there is a weak relationship between years of employment and pay raises, and that the relationship is positive.
Caution must be exercised at this juncture as the relationship determined cannot as of yet be considered statistically significant.
Knowing that the situation included 10 individuals (N = 10: number of participants and degrees of freedom equal N – 2: two variables) to determine whether the results are statistically significant one must go to a "r" value table and determine what value is needed for statistical significance. In this case where the degrees of freedom are 8 (N – 2), the required r value at the probability level of 0.05 is +/- 0.549. The obtained r value in this case of + 0.36 is less than the required r value; therefore, the relationship is not statistically significant at the 0.05 probability level. Further, the consulting psychologist can conclude as well that the null hypothesis is accepted, and the relationship was most likely due to chance factors.
The same data cited above can be placed in the raw score formula, and the exact same r value will result. Note: Should an r value exceed +/– 1.0, the conclusion drawn is simply that a mathematical mistake has been made in the calculation process.
Post a behavioral research situation that could use a Pearson coefficient research study and a chi square research study. Present the rationale for each selection. Be very specific in your presentation.
Format in APA style. 2 pages
Writing Components:
Organization: Introduction, Thesis, Transitions, Conclusion
Usage and Mechanics: Grammar, Spelling, Sentence structure
APA Elements: Attribution, Paraphrasing, Quotations
Style: Audience, Word Choice
100
Total 100
EDUCATION
2
Education: What does it mean to be Well-educated?
Thesis statement
To be well-educated means to attain the highest level of education for that individual
.
Abstrac ...
3Type your name hereType your three-letter and -number cours.docxlorainedeserre
3
Type your name here
Type your three-letter and -number course code here
The date goes here
Type instructor’s name here
Your Title Goes Here
This is an electronic template for papers written in GCU style. The purpose of the template is to help you follow the basic writing expectations for beginning your coursework at GCU. Margins are set at 1 inch for top, bottom, left, and right. The first line of each paragraph is indented a half inch (0.5"). The line spacing is double throughout the paper, even on the reference page. One space after punctuation is used at the end of a sentence. The font style used in this template is Times New Roman. The font size is 12 point. When you are ready to write, and after having read these instructions completely, you can delete these directions and start typing. The formatting should stay the same. If you have any questions, please consult with your instructor.
Citations are used to reference material from another source. When paraphrasing material from another source (such as a book, journal, website), include the author’s last name and the publication year in parentheses.When directly quoting material word-for-word from another source, use quotation marks and include the page number after the author’s last name and year.
Using citations to give credit to others whose ideas or words you have used is an essential requirement to avoid issues of plagiarism. Just as you would never steal someone else’s car, you should not steal his or her words either. To avoid potential problems, always be sure to cite your sources. Cite by referring to the author’s last name, the year of publication in parentheses at the end of the sentence, such as (George & Mallery, 2016), and page numbers if you are using word-for-word materials. For example, “The developments of the World War II years firmly established the probability sample survey as a tool for describing population characteristics, beliefs, and attitudes” (Heeringa, West, & Berglund, 2017, p. 3).
The reference list should appear at the end of a paper (see the next page). It provides the information necessary for a reader to locate and retrieve any source you cite in the body of the paper. Each source you cite in the paper must appear in your reference list; likewise, each entry in the reference list must be cited in your text. A sample reference page is included below; this page includes examples (George & Mallery, 2016; Heeringa et al., 2017; Smith et al., 2018; “USA swimming,” 2018; Yu, Johnson, Deutsch, & Varga, 2018) of how to format different reference types (e.g., books, journal articles, and a website). For additional examples, see the GCU Style Guide.
References
George, D., & Mallery, P. (2016). IBM SPSS statistics 23 step by step: A simple guide and reference. New York, NY: Routledge.
Heeringa, S. G., West, B. T., & Berglund, P. A. (2017). Applied survey data analysis (2nd ed.). New York, NY: Chapman & Hall/CRC Press.
Smith, P. D., Martin, B., Chewning, B., ...
Similar to Statistical Calculations 5Statistical Calculations.docx (16)
Project 2: Research Paper Compendium
Choose what you consider to be a monster or monstrosity –
literal
figurative (ideology, practice)
historical
cryptozoology
Examples:
mythology
invention
Vlad Tepes
Joseph Stalin
Pablo Escobar
Nazis
Biological Weapons
Assault Rifles
Adolf Hitler
the Ku Klux Klan
Dylan Roof
Griselda Blanco
Aileen Wuornos
Fred & Rosemary West
Mark Twitchell
Jeffrey Dahmer
Long Island Serial Killer
Jack the Ripper
Jim Jones/Jonestown
Bigfoot
Loch Ness Monster
the Hydra
Slender Man
Michael Myers
Ed Gein
Freddy Krueger
Slavery
Human Trafficking
the Drug Trade
Drug Addiction
Rwandan Genocide
Pol Pot’s Khmer Rouge
Aurora shooting
Sandy Hook
Lizzie Borden
Saddam Hussein
Heaven’s Gate Cult
Baba Yaga
the Holocaust
Balkan Genocide
the list goes on…
Write an 8 to 9 page research paper in which you are the expert on this monster/monstrosity. Both your paper and your expert presentation will reflect the biography/origin; timeline of actions/atrocities; cultural/societal impact; how this subject is depicted/sensationalized through various writings/the media (stories, biographies, scholarly articles, comics, graphic novels, poems, movies, interviews, folklore/fairy tails, television shows, et cetera); and why this monster/monstrosity has meaning to you. The paper must also include
7-8 annotated bibliography entries (I have attatched a document to show what it is).
Jamal Sampson's paper has to focus on the two monsters listed:
Saddam Hussein
Osama Bin Laden
.
Project 1 Interview Essay Conduct a brief interview with an Asian.docxdessiechisomjj4
Project 1: Interview Essay
Conduct a brief interview with an Asian immigrant to ask about their immigration story and push-pull factors. This can last 5-15 minutes. Then, write a 2 paragraphs on the DB.
You do
not
have to include the person’s real name! Immigration status is a sensitive topic, so please understand if someone does not want to be interviewed. Students have interviewed friends, family members, people in their community, and other students.
Project 1: Prompt
1.
Brief facts:
Around what age did they immigrate? How old are they now (in my 30s is acceptable)? What push-pull factors led them to immigrate to the U.S.? (You may have to explain what push-pull factors are.)
2. Add your own comments/perspective and perhaps even your own immigration story. What aspects of their story did you find interesting or surprising? What aspects were familiar to you?
Example:
I conducted a 10 minute interview with my neighbor "Dr. Villanueva" who immigrated to the U.S. over 45 years ago at the age of 26. I asked him about his push and pull factors. What reasons did he have for leaving his home country and why did he choose the U.S. as his new home? He stated that he wanted to leave the Philippines for a better life and more opportunities. He had grown up as the youngest of nine children and was very poor, but was able to study medicine and become a medical doctor specializing in ophthalmology. He heard that the U.S. was encouraging medical professionals to work there especially if they were fluent in English. According to our reading "Filipinos in America," (Lee 2015) the Philippines was a colony of the U.S. from 1898-1945 and English was taught in the education system (Lee, p. 90). Plus, many Filipinos then and still today dream about immigrating to the United States to improve their educational and financial opportunities. Dr. Villanueva came to the U.S. after the 1965 Immigration and Nationality Act abolished national quotas but limited immigration from Asia to educated professionals. When I asked if he felt that he experienced discrimination, Dr. Villanueva said yes, many times, but overall he is glad that he immigrated because his children had so many more opportunities in the U.S. Often, people still think that he is a foreigner or can't speak English. There have been a few occasions that people directed racial slurs at him, but he has not experienced any physical harm.
Dr. Villanueva seems to fit much of the data on Asian Americans that we studied in this class. However, I noticed some ways that he did not. For example, {etc....} Dr. Villanueva's story is much different than my grandparents' story who immigrated from __ and did not have college degrees when they arrived. [ADD YOUR PERSONAL REFLECTIONS ON THE INTERVIEW.]
.
Project 1 Scenario There is a Top Secret intelligence report.docxdessiechisomjj4
Project 1:
Scenario
: There is a Top Secret intelligence report that a terrorist organization based in the Middle East is planning to plant a dirty bomb in the inner harbor of major American city in the next 48 hours. The report has not been officially released or the classification reduced. You (the student) are the Chief of Police of this major metro city and do not have a security clearance at this time. The inner harbor is a major tourist attraction, a major shipping port and home to many international shipping companies, trade zones and military and federal government facilities.
You have heard the report exists but have not seen it. As the Police Chief of (you choose the city e.g. Baltimore, New York, Miami, Los Angeles, San Diego, Seattle etc) you have many questions about the report and many different agencies you will want to coordinate with. You will identify the real Homeland Security, LE and Intelligence organizations within the jurisdiction of the city you have chosen.
Requirement:
Write a minimum 1000 word paper (double space, 12 Font, New Times Roman) explaining how you would deal with this yet unseen report.
What actions would you take upon hearing of this report?
What Federal, state, local or government agencies would want to contact?
What questions would you want to ask about this report?
If it were true who would you want to share it with? Can you share it? What factors (e.g. legal, operational, public safety) might impede sharing this information?
Address
at least ten
of the concepts listed below within your paper:
Dissemination
Differentiate between intelligence and information
Intelligence products
Strategic versus tactical intelligence
Information sharing
Jurisdiction
Security classifications
Public safety
Intelligence roles
Federal versus local, state, and/or tribal
Target identification
Media/Hollywood portrayals
Database security/security of data
Value of intelligence
Domain awareness
Intelligence gap
Collection plans
Reliability, viability, and validity
Security clearances
.
Project #1 Personal Reflection (10)Consider an opinion that you .docxdessiechisomjj4
Project #1: Personal Reflection (10%)
Consider an opinion that you hold dearly. Write a brief reflection on the genealogy of your opinion. This can include personal experience, upbringing, social influence, media analysis, philosophy, anything that’s helped you form your opinion.
Purpose: I want you to start thinking about your process as a thinker. We can’t improve our processes in the future without understanding what we’ve done in the past.
Length: 1-3 pages
Format: MLA, 12 point Times New Roman font, 1 inch margins
.
Project 1 Chinese Dialect Exploration and InterviewYou will nee.docxdessiechisomjj4
Project 1: Chinese Dialect Exploration and Interview
You will need to cite references whenever you get the information from an article or from some online resources. In the written report, you need to include the following:
Title: An Exploration of [Dialect Name (spoken
where
)]
1.
Introduction
Introduce the geography of the dialect and which particular dialect variant you are focusing on. Give basic introduction about how many people are using this dialect and its current situation. Provide a map to indicate the dialectal grouping and the location of the speakers of the dialect.
2.
Linguistic Features of [Dialect Name (spoken
where
)]
Explore the following topics and introduce the
differences between this dialect and Standard Chinese (Mandarin)
in an organized and systematic way.
·
Syllable structure
·
Initial consonants
·
Finals (Rhymes)
·
Medials
·
Basic tones
·
Tone changes (optional: you get additional points if you explore this one)
·
Lexical or syntactic differences
To be able to do this section, you need to find resources online or from the library that reliably analyzed a dialect and systematically introduces this dialect or a dialect closely related to it. At the end of this linguistic description, summarize the speech features of speakers of this dialect when s/he uses Standard Chinese. What features do you expect a speaker of this dialect may carry into Standard Chinese? Are the differences going to be drastic enough to be detectable?
3.
Method:
In this section, you introduce the linguistic and social background of your interviewee(s).
1.
Informant Background:
Personal profile (gender, age, relevant linguistic and educational history, family background) [Have your interviewee fill out a linguistic background form provided by Prof. Lin]
2.
Setting (time and location of the interview, how was it documented?)
4.
Findings: Sociolinguistic aspect of the dialect according to the interview
You will present the interview results in an organized way. You should discuss the following issues related to the dialect:
·
What is the status of the particular dialect in relation to Mandarin? Discuss the issues related to diglossia (high versus low varieties). What are the social functions of the dialects? When do people use them and when do they not use them but opt for other languages and dialects? Compare the different uses of different dialects or speech variants.
·
Ask your interviewee his or her experiences with “accents”. How do people sound if they have accents? Do people using the dialects carry a special accent speaking Mandarin? How are people with accents perceived? Are there social stigma, attitudes, and identity issues associated with the dialect? How are people speaking this dialect usually perceived? Why do you think there are these social meanings that go with the accented speech?
·
How has this dialect changed in recent years, which may be associated with the above social political properties?
5.
Online.
Project 1 (1-2 pages)What are the employee workplace rights mand.docxdessiechisomjj4
Project 1 (1-2 pages)
What are the employee workplace rights mandated by U.S. Federal law?
Briefly discuss at least two controversial issues concerning workplace rights (other than monitoring e-mail). Provide real-life examples to illustrate your answer.
In addition, discuss the issue of workplace privacy. Specifically, do employees have the right to expect privacy in their e-mail conversations, or do companies have a right and/or responsibility to monitor e-mail?
Project 2 (1-2 pages)
Draft a performance action plan for a company to follow when providing discipline in response to complaints of sexual harassment. Use the Library or other Web resources if needed.
Please submit your assignment.
.
PROGRAM 1 Favorite Show!Write an HLA Assembly program that displa.docxdessiechisomjj4
PROGRAM 1: Favorite Show!
Write an HLA Assembly program that displays your favorite television show on screen in large letters. There should be no input, only output. For example, I really like The X-Files, so my output would look like this:
All this output should be generated by just five
stdout.put
statements.
.
Program must have these things Format currency, total pieces & e.docxdessiechisomjj4
Program must have these things
Format currency, total pieces & exit or ok button to go back; comments; tooltips;
Piecework C
Modify Piecework B to a multi-form project, adding a Splash form and a Summary form. Be sure to
retain your Piecework B program as you will need it later. Add a slogan and logo that the user can
display or hide independently, based on toggling and
displaying a checkmark in the menu choices; program
should start with slogan and logo being displayed and the
menu items checked. Add program version number, a
graphic, and an OK button to About box; About box should
display as modal. Splash should display project name,
programmer name, and a graphic. Change the Summary
data from a message box to its own form (also modal).
.
Professors Comments1) Only the three body paragraphs were require.docxdessiechisomjj4
Professors Comments:
1) Only the three body paragraphs were required. The introduction and the conclusion were not to be included in the Unit 6 paper. They should be saved for the Unit 8 paper when the thesis will be moved to the end of the introduction.
2) You paper is already over the length limit, so nothing else can be added. Some parts could be deleted, for example: "
Samimi and Jenatabadi (2014), point out that" and "
In another article, Sandbrook and Güven (2014) asserted that
." Those phrases add nothing to the paper and are distracting. You would have to explain who they are, so eliminate that phrase and others like it.
3) Keep in mind that your paper is not a literature review. It is an essay in which you are to explain your topic clearly and concisely. Also keep in mind that your topic is one that is difficult to understand and you are not writing for economists or for those with Ph.D.'s. Write in a manner that your average reader can comprehend. Explain concepts clearly in non-jargon type language. Clarity is your goal.
4) The Federal Reserve Bank information at the end of the introduction is not cited.
5) Bullet points should not be used in this paper. Everything should be integrated into the paragraphs using transitions.
6) Subtitles should not be used. This is a short paper, 2 - 2 1/2 pages double spaced, and they are not needed.
7) What does this mean: "
Globalization makes it possible for huge organizations to comprehend economies of scale
"?
8) Do not use the word "we."
9) Since you are discussing globalization, you must explain which country you are discussing. For example, when you say "federal policy," do you mean the United States?
My draft of paper:
Thesis statement:
Globalization has influenced practically every facet regarding today’s lifestyles.
Globalization
Globalization
refers to the action or process of global incorporation as a result of the interchange associated with world perspectives, goods, concepts, as well as other facets of tradition.
Improvements in transportation (like the steam train engine, steamship, aircraft engine, as well as container ships) in addition to telecommunications infrastructure (such as the development of the telegraph along with its contemporary progeny, the world wide web as well as cellular phones) happen to be significant aspects of globalization. Therefore, it creates new interdependence associated with monetary as well as social functions.
Samimi and Jenatabadi (2014), point out that a
lthough a lot of scholars place the beginnings connected with globalization within contemporary days. Some trace its heritage a long time before the Western Age regarding Discovery as well as voyages towards the New World, others even to the 3rd centuries BC
(Samimi, & Jenatabadi, 2014)
.
Large-scale globalization started out in the 1820s. Back in the Nineteenth millennium as well as in the
early
Twentieth century, the connection of the globe's financial system.
Program EssayPlease answer essay prompt in a separate 1-page file..docxdessiechisomjj4
Program Essay
Please answer essay prompt in a separate 1-page file. Responses should be double-spaced, 11 point font or greater with 1-inch margins.
Based on what you’ve learned about the NYU communicative sciences and disorders master’s program through your application process, please name two faculty members whose research or fieldwork you are most interested in and why.
Ist
• Voice and Voice Disorders
• Neurogenic Communicative Disorders
• Dysphagia
Professor Celia Stewart is a tenured Associate Professor in the Department of Communicative Sciences and Disorders at NYU: Steinhardt School of Culture, Education, and Human Development. She provides classes in Voice Disorders, Interdisciplinary Habilitation of the Speaking Voice, Multicultural and Professional Issues, and Motor Speech Disorders. She maintains a small private practice that specializes in care of the professional voice, transgender voice modification, neurogenic voice disorders, and dysphagia. She has published in the areas of spasmodic dysphonia, transgender voice, dysphagia, Parkinson’s disease, and Huntington’s disease.
2nd
• Perception of linguistic and talker information in speech
• Relationship between talker processing, working memory, and linguistic processing
• Development of talker processing in children with both typical and impaired language development.
Susannah Levi is an Associate Professor in the Department of Communicative Sciences and Disorders. She examines how information about a speaker affects language processing. Her past research has looked at whether people sound the same when speaking different languages and whether being familiar with a speaker’s voice in one language, helps a listener understand that speaker in a different language. Her current work expands on this to examine whether children, like adults, also show a processing benefit when listening to familiar talkers. She is also exploring whether language processing can be improved for children with language disorders using speaker familiarity.
Dr. Levi received her doctorate from the Department of Linguistics at the University of Washington, completed a postdoctoral research position in the Department of Brain and Psychological Sciences at Indiana University. Prior to coming to NYU, she taught at the University of Michigan. She is currently the Director of the Undergraduate Program in the Department of Communicative Sciences and Disorders.
.
Program Computing Project 4 builds upon CP3 to develop a program to .docxdessiechisomjj4
Program Computing Project 4 builds upon CP3 to develop a program to perform truss analysis. A truss consists of straight, slender bars pinned together at their end points. Truss members are considered to be two force, axial members. Thus, the force caused by each truss member - and the internal force in each member - acts only along it’s axis. In other words, the direction of each member force is known and only the magnitudes must be determined. To analyze a truss we study the forces acting at each individual pin joint. This is known as the Method of Joints. We will call each pin joint a node and the slender bars connecting the nodes will be called members. The previous project computed a unit vector to describe the vector direction of every member of a truss structure. To analyze the structure a few other key inputs must be included like the support reactions and external loads applied to the structure. With all of this information, you will need to make the correct changes to the provided planar (2-D) truss template program to be able to analyze a space (3-D) truss. What you need to do For a planar truss, every node has 2 degrees of freedom, the e1 and e2 directions. Therefore, for every planar truss problem, the total number of degrees of freedom (DOF) in the structure is equal to 2 times the number of nodes. We will consider the first degree of freedom for each node as the component acting in the e1 direction. So for any given node, i, the corresponding degree of freedom is (2·i)-1. For the same node, i, the corresponding value for the second degree of freedom, the component in the e2 direction, is 2-i. This numbering notation can be modified for a space truss. The difference with the space truss is that every node has 3 degrees of freedom, one degree for each of the e1, e2 and e3 directions. The degree of freedom indices are extremely crucial in understanding how to set up the matrices for the truss analysis. For this computing project, you will first need to understand the planar truss program and the inputs that are needed for that program. The first input is the spatial coordinates (x, y, z) of the nodal locations for a truss. It is convenient to label each node with a unique number (also known as the “node number”). Each row of the nodal coordinate array should contain the x and y coordinates of the node. We will use the matrix name of “x” for all nodal coordinates. Please note that “nNode” is an integer value that corresponds to the number of nodes in the truss and must be adjusted for every new truss problem. For Node 1 this matrix array input looks like: x(1,:) = [0,0]; Once the coordinates of the nodes are in the program, you will need to input how those nodes are connected by the members of the truss. In order to describe how the members connect the nodes you will also need to label each member with a “member number”. This connectivity array should contain only the nodes that are joined by a member, with each row containing firs.
Project 1 Resource Research and ReviewNo directly quoted material.docxdessiechisomjj4
Project 1: Resource Research and Review
No directly quoted material may be used in this project paper. Resources should be summarized or paraphrased with appropriate in-text and Resource page citations.
Project 1 is designed to help prepare you for the final project at the end of the semester. You will notice that, for your final project in this course, you will be asked to trace a crime or criminal incident through the adult criminal justice system, from initial arrest to the eventual return to the community following incarceration. As you work on the final project, you will encounter numerous decision points or stages in the system. Project 1 will assist you in preparing for your final project by introducing you to topic research. You may then use the results of this project to support your final project paper.
Project 1 Assignment:
Using the designated topic listed below (see, Topics), you will search the UMUC Library Services databases and the Internet for resource material that explains, clarifies, critiques, etc. the topic.
1. Your Resource Research and Review project must contain four (4) outside sources (not instructional material for this course), at least two of which must come from the UMUC Library data base.
2. Locate books, periodicals, and documents that may contain useful information and ideas on your topic. You may conduct your research with the assistance of a UMUC librarian, reviewing your own personal materials on the topic, using the Internet, visiting an actual library, etc. and reviewing the available items. Then, choose those works that provide a variety of perspectives on your topic.
Note: You can connect to Library Services by using the Library link under RESOURCES in the Classroom task bar, or link directly to the UMUC Library Guide to Criminal Justice Resources link in CONTENT
3. Type the reference “citation” information for the book, article, or document using the American Psychological Association (APA) formatting standards. (There are links to APA format standards under Library Services.)
4. Each reference is to be followed by the annotation. The purpose of the annotation is to inform the reader of the relevance, accuracy, and quality of the sources cited. Creating an annotated bibliography calls for a variety of intellectual skills: concise exposition, succinct analysis, and informed library research.
5. Write a concise annotation (150 words) for each reference that summarizes the central theme and scope of the book, article, or document. This must include:
a) briefly, in your own words, describe the content of the article
b) compares or contrasts the work with at least one other article in your research review
The topic: Issues with evidence (DNA, eyewitness testimonies, direct vs. circumstantial, etc.)
Format
The project paper should begin with an introductory paragraph and end with a concluding paragraph
Each annotation should contain approximately 150 words
Double space, 12 pt. font, 1” margins
Cover pa.
Professionalism Assignment I would like for you to put together yo.docxdessiechisomjj4
Professionalism Assignment
I would like for you to put together your current resume or update one that you have previously created. Refer to the attached curriculum vitae as an example to assist with the completion of this assignment. A curriculum vitae, or CV, is typically a longer version of a resume which includes conference and journal publications, research, and awards. CVs are usually 2-3 pages, compared to a resume which should usually be limited to a single page. Since most of you will not have publication or conference presentations at this point in your academic career, please leave that section out and submit a more traditional single page resume.
Education
M.S. Electrical and Computer Engineering, 2012
University of Louisville, Louisville, KY
B.S. Electrical Engineering, 2008
Western Kentucky University, Bowling Green, KY
Experience
Engineering Technician, 2014-Current
Engineering, Manufacturing, and Commercialization Center
Applied Physics Institute
Western Kentucky University
Instructor, 2014 - Current
Electrical Engineering Program
Department of Engineering
Western Kentucky University
Grosscurth PhD Fellow, 2012-2014
Department of Electrical and Computer Engineering
J.B. Speed School of Engineering
University of Louisville
Graduate Research Assistant, 2011-2012
Department of Electrical and Computer Engineering
J.B. Speed School of Engineering
University of Louisville
Electrical Engineer, 2009-2012
Applied Physics Institute
Western Kentucky University
Research Associate, 2008-2009
Applied Physics Institute
Western Kentucky University
Research Assistant, 2005-2008
Applied Physics Institute
Western Kentucky University
Publications
Craig Dickson, Stuart Foster,
Kyle Moss
, Anoop Paidipally, Jonathan Quiton, William Ray, and Phillip Womble,
Stochastic Modeling for Automatic Response Technology with Applications to Climate and Energy,
at the 8
th
Kentucky Entrepreneurship and Innovation Conference, Louisville, KY, June 2012
Jeffrey L. Hieb, James H. Graham, Nathan Armentrout, and
Kyle Moss
,
Security Pre-Processor for Industrial Control Systems,
at the 8
th
Kentucky Entrepreneurship and Innovation Conference, Louisville, KY, June 2012
Jeffery Hieb, James Graham, Jacob Schreiver,
Kyle Moss,
Security Preprocessor for Industrial Control Networks,
at the 7
th
International Conference on Information-Warfare and Security, Seattle, Washington, March 2012
Kyle Moss,
Phillip Womble, Alexander Barzilov, Jon Paschal, Jeremy Board,
Wireless Orthogonal Sensor Networks for Homeland Security
at 2007 IEEE Conference on Technologies for Homeland Security, Woburn, MA, May 2007
Barzilov, P. Womble, I. Novikov, J. Paschal, Jeremy Board, and
Kyle Moss
,
Network of Wireless Gamma Ray Sensors for Radiological Detection and Identification
at the SPIE Defense and Security Symposium, Orlando, FL, April 2007
Alexander Barzilov, Jeremy Board, .
Professor Drebins Executive MBA students were recently discussing t.docxdessiechisomjj4
Professor Drebin's Executive MBA students were recently discussing the benefits of a chart of accounts. Following is a transcript of the discussion. Most of the comments were correct, but two students were off base. Assume the role of Professor Drebin, and identify the two students whose statements are incorrect. Record your answer in Blackboard.
.
Professional Legal Issues with Medical and Nursing Professionals .docxdessiechisomjj4
"Professional Legal Issues with Medical and Nursing Professionals" Please respond to the following:
* From the scenario, analyze the different and overlapping general roles of physicians and nurses as they apply to professional credentialing and subsequent patient safety and satisfaction. Determine the major ways in which these overlapping roles may help play a part in health professional credentialing processes and conduct, and identify and analyze the ethical role these influences play in health care.
Analyze the major professional roles played by physicians and nurses as they apply to physicians’ conduct in the medical arena and to nurses in the role of adjuncts to physicians. Evaluate the degree and quality of care that physicians, nurses, and medical technologists provide in their primary roles, including, but not limited to, patient safety and satisfaction as required in 21st Century U.S. hospitals.
.
Prof Washington, ScenarioHere is another assignment I need help wi.docxdessiechisomjj4
Prof Washington, Scenario
Here is another assignment I need help with. I know the scenario is the same as before but now we need to come up with the project management plan. The Scenario is
You have been asked to be the project manager for the development of an information technology (IT) project. The system to be developed will allow a large company to coordinate and maintain records of the professional development of its employees. The company has over 30,000 employees who are located in four sites: Florida, Colorado, Illinois, and Texas. The system needs to allow employees to locate and schedule professional development activities that are relevant to their positions. Sophisticated search capabilities are required, and the ability to add scheduled events to the employees’ calendars is desired. The system needs to support social networking to allow employees to determine who is attending conferences and events. This will promote fostering relationships and ensure coverage of conferences that are considered of high importance.
Once an activity has been completed, employees will use the system to submit the documentation. The system should support notifications to management personnel whenever their direct reports have submitted documentation. The system should also notify employees if their deadline to complete professional development requirements is approaching and is not yet satisfied.
Project Scope Management Plan
For the given scenario, create a project scope management plan that will detail how the project scope will be defined, managed, and controlled to prevent scope creep. The plan may also include how the scope will be communicated to all stakeholders.
Project Scope
After you have the project scope management plan developed, define the project scope.
.
Prof James Kelvin onlyIts just this one and simple question 1.docxdessiechisomjj4
Prof James Kelvin only
It's just this one and simple question
1. This week we begin focusing on PowerPoint. When you create a PowerPoint presentation, there are many elements included such as: theme, transitions, images, font, color, content layout, etc. List and explain four guidelines you learned about how to create a successful PowerPoint presentation. Additionally, describe some common mistakes that are made when PowerPoint presentations are created.
.
Product life cycle for album and single . sales vs time ( 2 pa.docxdessiechisomjj4
Product life cycle for album and single .
sales vs time ( 2 pages not less with chart for each album and singles
Album
introduction,
growth
, maturity
, decline .
Singles
introduction,
growth
, maturity
, decline
.
Produce the following components as the final draft of your health p.docxdessiechisomjj4
Produce the following components as the final draft of your health promotion program written proposal;
1. Introduction to the Program project.
2. Epidemiological and Needs Assessments Summary
3. Risk Factors, Goals, Objectives and Educational Plans
4. Marketing Plans and Proposed Budget
5. Evaluation Plans
6. Leadership Needs and Collaborative Strategies
.
Produce a preparedness proposal the will recommend specific steps th.docxdessiechisomjj4
Produce a preparedness proposal the will recommend specific steps that could potentially reduce (mitigate) the loss of life and property resulting from you climate impact or natural hazard. The proposal should target a specific person, agency, municipality or organization responsible for emergency mitigation efforts. Seven sections should be labelled as indicated in bold and address the following:
Specifically Identify and state who is the intended audience for your proposal (Target audience)
Identify and describe the climate impact or natural hazard (Hazard)
Identify and explain the risk associated with your specific geographic location (Location)
Describe the atmospheric and geologic conditions or processes that give rise to the impact or hazard (Earth processes)
Describe ways in which human and environmental processes contribute to the impact or hazard (Human processes)
Discuss past impact/hazard events and mitigation or communication policies and their effectiveness (Past events/policies)
Recommend ethically and socially responsible ways to improve current mitigation and communication policies (Proposal)
Make sure and answer according to the bolded labels (Target audience, Hazard, etc.) Responses should be brief, except for your Proposal recommendation. If you have completed the Milestones as directed the majority of this information should already exist!
1. The preparedness proposal should focus on COMMUNICATING the science information to the target audience
2. The proposal MUST include at least two data sources supporting your recommendations and be represented in a graphical format
3. The proposal must be double spaced, size 12 font
4. The proposal must list references/citations where appropriate
1.5-2page.
China Gansu
mudslides. Read mileston I write fist. here will have the information you need use in that paper.
.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
The Art Pastor's Guide to Sabbath | Steve ThomasonSteve Thomason
What is the purpose of the Sabbath Law in the Torah. It is interesting to compare how the context of the law shifts from Exodus to Deuteronomy. Who gets to rest, and why?
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
1. Statistical Calculations 5
Statistical Calculations
Jeffree Doerflinger
Instructor: Margarita Rovira
August 22, 2014
1. For assistance with these calculations, see the Recommended
Resources for Week One. Data, even numerically code
variables, can be one of 4 levels – nominal, ordinal, interval, or
ratio. It is important to identify which level a variable is, as
this impacts the kind of analysis we can do with the data. For
example, descriptive statistics such as means can only be done
on interval or ratio level data. Please list, under each label, the
variables in our data set that belong in each group.
Nominal
ID
Gender
Gender 1
Ordinal
Degree
Grade
Midpoint
Interval
Salary
Age
Performance Rating
Service
2. Ratio
Compa
Raise
1. The first step in analyzing data sets is to find some summary
descriptive statistics for key variables. For salary, compa, age,
Performance Rating, and Service; find the mean and standard
deviation for 3 groups: overall sample, Females, and Males.
You can use either the Data Analysis Descriptive Statistics tool
or the Fx = average and = stdev functions. Note: Place data to
the right, if you use Descriptive statistics, place that to the right
as well:
Salary
Overall = 45
Male = 52
Female = 38
Compa
Overall = 1.062
Male = 1.056
Female = 1.069
Age
Overall = 35.72
Male = 38.92
Female = 32.52
Performance rating
Overall = 85.9
Male = 87.6
Female = 84.2
Service
Overall = 8.96
Male = 10
Female = 7.92
3. Standard Deviations
Salary
Overall = 19.20140301
Male = 17.77638883
Female = 18.29389698
Compa
Overall = 0.07682507
Male = 0.083789061
Female = 0.070344699
Age
Overall = 8.251258407
Male = 8.38609961
Female = 8.251258407
Performance rating
Overall = 11.41472375
Male = 8.674675786
Female = 13.59227722
Service
Overall = 5.717713258
Male = 6.357410374
Female = 4.906798005
2. What is the probability for a:
a. Randomly selected person being a male in grade E?
= 25/49*12/49
= 0.1249
b. Randomly selected male being in grade E?
= 10/25
4. = 0.4
c. Why are the results different?
When we look at the first instance it would show that a person
would be randomly chosen from the total population, and I look
at the second the individual or person is chosen randomly from
a group of only males.
4. For each group (overall, females, and males) find::
a. The value that cuts off the top 1/3 salary in each group.
Overall = 58
Male = 62
Female = 41
b. The z score for each value.
z = (X - μ) / σ
Overall = (58-45)/19.20 = 0.67708
Male = (62-52)/17.77 = 0.5627
Female = (41-38)/18.29 = 0.1640
c. The normal curve probability of exceeding this score.
Overall = 0.2332
Male = 0.2147
Female = 0.1332
d. What is the empirical probability of being at or exceeding
this salary value?
Overall = 0.2525
Male = 0.2214
Female = 0.1564
e. The score that cuts off the top 1/3 compa in each group.
Overall = 1.096
Male = 1.086
Female = 1.096
f. The z score for each value.
z = (X - μ) / σ
Overall = (1.096-1.062)/0.07683 = 0.4425
Male = (1.086-1.056)/0.08386 = 0.3577
5. Female = (1.096-1.069)/0.07034 = 0.3838
g. The normal curve probability of exceeding this score.
Overall = 0.5675
Male = 0.6523
Female = 0.6277
h. What is the empirical probability of being at or exceeding
this salary value?
Overall = 0.5573
Male = 0.6372
Female = 0.5986
i. How do you interpret the relationship between the data sets?
What do they mean about our equal pay for equal work
question?
The data indicates a higher pay ratio for the male employees
than the females. The overall comp scores relationship to the
individual is drawn that individuals pay that is not proportional
to the work rates. It is evident that the salaries vary for the
different work performance rate. Equal pay for equal work
relationship does not exist. Equal Pay Conclusions
j. What conclusions can you make about the issue of male and
female pay equality? Are all of the results consistent?
The results show non-equality on male and female pays for
equal work. The results from the analysis on pay because of
performance are not consistent.
k. What is the difference between the salary and compa
measures of pay?
The compa measures depend completely on the grade. The
higher your pay, the higher the resulting compa value will be in
the similar pay grades.
l. Conclusions from looking at salary results
There is no equality and consistency in the pay associated with
performance rate, it would show that equal pay for equal work
does not again exist. There is no clear trend on salaries from the
data
m. Conclusions from looking at compa results
Compa results are dependent on the job grade and the salaries of
6. the individuals.
n. Do both salary measures show the same results?
The salary results do not show the same trend or results
o. Can we make any conclusions about equal pay for equal work
yet?
From the data there is no trend for equal pay for equal work, so
yes.
How to Analyze a Film
Borrowed heavily from Russell Johnson, Department of Theatre,
Wright State University.
1. Assume that your reader is intelligent and has seen the film
you are writing about.
2. Write in present tense. The film is a living work of art. Do
not discuss the film, the plot, the characters, etc., in the past
tense.
3. Avoid the slice-of-life approach. This presumes that the film
is not a work of art, but is real life, and then discusses the
characters as if they really existed. Often, this approach leads to
one of the most common errors for people starting to write
about film: because you presume the film is “real life,” you
start criticizing components of the film as not being realistic.
These kinds of comments are not relevant, generally, because
films are works of art which don’t always function realistically.
4. Never merely summarize the plot. This is often the worst
thing you can do. Beware of going on for several paragraphs or
pages summarizing the plot, occasionally disguising the
summary by throwing in a comment like “thus revealing an
insight.” Remember that analysis is very different from plot
synopsis, and if all you are doing is retelling the story, you are
accomplishing virtually nothing.
7. 5. Never ignore the images. Remember, don’t write your paper
as if you were working from a plot summary or the novel on
which the film may be based. The best kind of film analysis
strikes a good balance between the literary components of the
film and the visual components of the film -- showing how all
the elements, in a unified way, function.
6. Avoid writing your own personal response or prejudice. In
other words, don’t give a personal “movie review” or your
opinion of the quality of the film. Remember to analyze the
themes and images. When you analyze something, you are
trying to explain how it works, what it means, and how and why
it’s constructed the way it is. Do not use a newspaper or
magazine review as your model for writing. The evaluative
approach is not acceptable for an analytical paper. As such, you
should be very wary of using words like “great,” “thrilling,”
“interesting,” “boring,” etc.
7. Avoid the “Introduction/Conclusion” problem. This is when
you take almost one whole page to tell me what your are going
to do, write two pages, and then take one more page of
conclusion to tell me what you did. In general, avoid these
statements of purpose. For the most part, in a short analytical
paper, all you need to do is do it. Get into and out of the
argument and analysis as simply and directly as possible. You
should, of course, use examples to support your ideas.
8. Avoid putting all the ideas you didn’t know what do with into
one long, complicated paragraph.
9. Avoid the corollary to Rule #8: putting one idea in each
paragraph, with each paragraph being only one or two sentences
long and no real development of the idea.
10. Avoid sweeping generalizations, such as “A good movie
must always …” There are always exceptions.
11. Avoid the “Everything but the Kitchen Sink” approach. This
8. is when you mention every possible theme and image you can
think of, but you develop none of them thoroughly. Remember
to develop your argument and fine tune it. Always back up your
ideas and contentions with specific examples for support.
Ways to Approach the Film Analysis
1. You can try to “unlock” the film, by providing its “key” –
what is the film really about? How is the film organized
visually? What are its themes? What was the director trying to
say?
2. You can find a cultural theme or themes in the film and
compare/contrast these cultural concepts to the concepts you are
familiar with in the United States. Show how these themes are
developed throughout the film.
3. You can take one specific subject and show the relevance of
that subject to the film and the director’s sensibility (i.e., the
use of color, camera movement, relationship to contemporary
politics, treatment of women, and so forth).
GIVE ONLY A BRIEF (1 PARAGRAPH) SUMMARY OF THE
READINGS AND FILMS.Then, analyze and react. Tell me how
the readings and films are affecting your thinking. What ideas
do these readings spark? How do these readings and films fit
into the overall theme of the course? What comparisons can you
make between the readings/films and your own experiences?
What comparisons can you make between the readings and the
films we view? What are some of the cultural values addressed
in the readings and films?
DataSee comments at the right of the data
set.IDSalaryCompaMidpointAgePerformance
RatingServiceGenderRaiseDegreeGender1Grade8231.000233290
9. 915.80FAThe ongoing question that the weekly assignments
will focus on is: Are males and females paid the same for equal
work (under the Equal Pay Act)?
10220.956233080714.70FANote: to simplfy the analysis, we
will assume that jobs within each grade comprise equal
work.11231.00023411001914.80FA14241.04323329012160FAT
he column labels in the table
mean:15241.043233280814.90FAID – Employee sample number
Salary – Salary in thousands 23231.000233665613.31FAAge
– Age in yearsPerformance Rating – Appraisal rating
(Employee evaluation score)26241.043232295216.21FAService
– Years of service (rounded)Gender: 0 = male, 1 = female
31241.043232960413.90FAMidpoint – salary grade midpoint
Raise – percent of last raise35241.043232390415.31FAGrade –
job/pay gradeDegree (0= BSBA 1 =
MS)36231.000232775314.31FAGender1 (Male or
Female)Compa - salary divided by
midpoint37220.956232295216.21FA42241.0432332100815.70F
A3341.096313075513.60FB18361.1613131801115.61FB20341.0
963144701614.81FB39351.129312790615.51FB7411.025403210
0815.70FC13421.0504030100214.71FC22571.187484865613.80
FD24501.041483075913.81FD45551.145483695815.20FD17691
.2105727553130FE48651.1405734901115.31FE28751.11967449
5914.41FF43771.1496742952015.51FF19241.043233285104.61
MA25241.0432341704040MA40251.086232490206.30MA2270.
870315280703.90MB32280.903312595405.60MB34280.903312
680204.91MB16471.175404490405.70MC27401.000403580703.
91MC41431.075402580504.30MC5470.9794836901605.71MD3
0491.0204845901804.30MD1581.017573485805.70ME4661.157
57421001605.51ME12601.0525752952204.50ME33641.1225735
90905.51ME38560.9825745951104.50ME44601.0525745901605
.21ME46651.1405739752003.91ME47621.087573795505.51ME
49601.0525741952106.60ME50661.1575738801204.60ME6761.
1346736701204.51MF9771.149674910010041MF21761.134674
3951306.31MF29721.074675295505.40MF
Week 1Week 1.Measurement and Description - chapters 1 and
10. 21Measurement issues. Data, even numerically coded variables,
can be one of 4 levels - nominal, ordinal, interval, or ratio. It is
important to identify which level a variable is, asthis impact the
kind of analysis we can do with the data. For example,
descriptive statistics such as means can only be done on interval
or ratio level data.Please list under each label, the variables in
our data set that belong in each
group.NominalOrdinalIntervalRatiob.For each variable that you
did not call ratio, why did you make that decision?2The first
step in analyzing data sets is to find some summary descriptive
statistics for key variables.For salary, compa, age, performance
rating, and service; find the mean, standard deviation, and range
for 3 groups: overall sample, Females, and Males.You can use
either the Data Analysis Descriptive Statistics tool or the Fx
=average and =stdev functions. (the range must be found using
the difference between the =max and =min functions with Fx)
functions.Note: Place data to the right, if you use Descriptive
statistics, place that to the right as well.SalaryCompaAgePerf.
Rat.ServiceOverallMeanStandard
DeviationRangeFemaleMeanStandard
DeviationRangeMaleMeanStandard DeviationRange3What is the
probability for a:Probabilitya. Randomly selected person
being a male in grade E?b. Randomly selected male being in
grade E? Note part b is the same as given a male, what is
probabilty of being in grade E?c. Why are the results
different?4For each group (overall, females, and males)
find:OverallFemaleMalea.The value that cuts off the top 1/3
salary in each group.b.The z score for each value:c.The normal
curve probability of exceeding this score:d.What is the
empirical probability of being at or exceeding this salary
value?e.The value that cuts off the top 1/3 compa in each
group.f.The z score for each value:g.The normal curve
probability of exceeding this score:h.What is the empirical
probability of being at or exceeding this compa value?i.How do
you interpret the relationship between the data sets? What do
they mean about our equal pay for equal work question?5.
11. What conclusions can you make about the issue of male and
female pay equality? Are all of the results consistent? What is
the difference between the sal and compa measures of
pay?Conclusions from looking at salary results:Conclusions
from looking at compa results:Do both salary measures show
the same results?Can we make any conclusions about equal pay
for equal work yet?
Week 2Week 2Testing meansQ3In questions 2 and 3, be sure to
include the null and alternate hypotheses you will be testing.
HoFemaleMaleIn the first 3 questions use alpha = 0.05 in
making your decisions on rejecting or not rejecting the null
hypothesis.45341.01745410.8701Below are 2 one-sample t-tests
comparing male and female average salaries to the overall
sample mean. 45231.157(Note: a one-sample t-test in Excel can
be performed by selecting the 2-sample unequal variance t-test
and making the second variable = Ho value -- see column
S)45220.979Based on our sample, how do you interpret the
results and what do these results suggest about the population
means for male and female average
salaries?45231.134MalesFemales45421.149Ho: Mean salary =
45Ho: Mean salary = 4545241.052Ha: Mean salary =/= 45Ha:
Mean salary =/= 4545241.17545691.043Note: While the results
both below are actually from Excel's t-Test: Two-Sample
Assuming Unequal Variances, 45361.134having no variance in
the Ho variable makes the calculations default to the one-
sample t-test outcome - we are tricking Excel into doing a one
sample test for
us.45341.043MaleHoFemaleHo45571.000Mean5245Mean38454
5231.074Variance3160Variance334.6666666667045501.020Obs
ervations2525Observations252545240.903Hypothesized Mean
Difference0Hypothesized Mean
Difference045751.122df24df2445240.903t Stat1.9689038266t
Stat-1.913206357345240.982P(T<=t) one-
tail0.0303078503P(T<=t) one-tail0.033862118445231.086t
Critical one-tail1.7108820799t Critical one-
tail1.710882079945221.075P(T<=t) two-
12. tail0.0606157006P(T<=t) two-tail0.067724236945351.052t
Critical two-tail2.0638985616t Critical two-
tail2.063898561645241.140Conclusion: Do not reject Ho; mean
equals 45Conclusion: Do not reject Ho; mean equals
4545771.087Is this a 1 or 2 tail test?Is this a 1 or 2 tail test?-
why?- why?P-value is:P-value is:45551.052Is P-value > 0.05?Is
P-value > 0.05?45651.157Why do we not reject Ho?Why do we
not reject Ho?Interpretation:I would not dismiss H0, for an
alpha of 0.05, there is no distinction function between mean pay
and 45.2Based on our sample data set, perform a 2-sample t-test
to see if the population male and female average salaries could
be equal to each other.(Since we have not yet covered testing
for variance equality, assume the data sets have statistically
equal variances.)t = (X1-X2)/(Sx1+x2) (52-38) / sqrt(316/25 +
334.667/25)t=2.744Ho mean1-mean2 =0Ha: mean1 - mean 2 =/=
0P-value is:2.744Is P-value < 0.05?NoReject or do not reject
Ho:RejectIf the null hypothesis was rejected, what is the effect
size value:Meaning of effect size measure:Effect size = True
value - Hypothesized value0Interpretation:I can dismiss my null
hypothesis because the results are significant at .05.b.Since the
one and two tail t-test results provided different outcomes,
which is the proper/correct apporach to comparing salary
equality? Why?The two tail t-test would be fitting if the fact
that Two-sample hypothesis testing is factual investigation
intended to test if there is a distinction between two methods
from two separate populations.3Based on our sample data set,
can the male and female compas in the population be equal to
each other? (Another 2-sample t-test.)t = (X1-X2)/(Sx1-
x2)(1.05624-1.0687)/ sqrt(.0070207641/25+.0049477156/25)-
0.01246/sqrt (.000280830564+.000197908624)-
0.01246/.021880t=-.56947the critical 2 tail number at .05
significance is 2.064What is the p-value:0.56947Is P-value <
0.05?YesReject or do not reject Ho:AcceptedIf the null
hypothesis was rejected, what is the effect size value:-Meaning
of effect size measure:- Interpretation: I can accept the null
hypothesis that male and female compas can be equal to each
13. other.4Since performance is often a factor in pay levels, is the
average Performance Rating the same for both genders?Ho
mean1-mean2 =0Ha: mean1 - mean 2 =/= 0Test to use:T-
Test1.0521.145What is the p-value:1.6245Is P-value <
0.05?NODo we REJ or Not reject the null?RejectIf the null
hypothesis was rejected, what is the effect size value:-Meaning
of effect size measure:-Interpretation:For average Performance
Rating is the same for both genders. This makes me want to
know if the rate of variation is growing larger or smaller over
time. This would let me know if the gap is improving or not.5If
the salary and compa mean tests in questions 2 and 3 provide
different results about male and female salary equality, which
would be more appropriate to use in answering the question
about salary equity? Why?What are your conclusions about
equal pay at this point?I would believe that the compensation
mean test is referring to 2 it would seem that this would also be
the better decision given the two tests. I feel that the compa
estimation takes the pay review out of the mathematical
statement by isolating the compensation by the midpoint.My
conclusion is that there is not enough data to focus on the data
equity.
Week 3Week 3At this point we know the following about male
and female salaries.a.Male and female overall average salaries
are not equal in the population.b.Male and female overall
average compas are equal in the population, but males are a bit
more spread out.c.The male and female salary range are almost
the same, as is their age and service.d. Average performance
ratings per gender are equal.Let's look at some other factors that
might influence pay - education(degree) and performance
ratings.1Last week, we found that average performance ratings
do not differ between males and females in the population.Now
we need to see if they differ among the grades. Is the average
performace rating the same for all grades?(Assume variances
are equal across the grades for this ANOVA.)ABCDEFNull
Hypothesis:Alt. Hypothesis:Place B17 in Outcome range
box.Interpretation:What is the p-value:Is P-value < 0.05?Do we
14. REJ or Not reject the null?If the null hypothesis was rejected,
what is the effect size value (eta squared):Meaning of effect
size measure:What does that decision mean in terms of our
equal pay question:2While it appears that average salaries per
each grade differ, we need to test this assumption. Is the
average salary the same for each of the grade levels? (Assume
equal variance, and use the analysis toolpak function ANOVA.)
Use the input table to the right to list salaries under each grade
level.Null Hypothesis:Alt. Hypothesis:ABCDEFPlace B55 in
Outcome range box.What is the p-value:Is P-value < 0.05?Do
you reject or not reject the null hypothesis:If the null
hypothesis was rejected, what is the effect size value (eta
squared):Meaning of effect size measure:Interpretation:3The
table and analysis below demonstrate a 2-way ANOVA with
replication. Please interpret the results.BAMAHo: Average
compas by gender are equalMale1.0171.157Ha: Average compas
by gender are not equal0.8700.979Ho: Average compas are
equal for each degree1.0521.134Ho: Average compas are not
equal for each degree1.1751.149Ho: Interaction is not
significant1.0431.043Ha: Interaction is
significant1.0741.1341.0201.000Perform
analysis:0.9031.1220.9820.903Anova: Two-Factor With
Replication1.0861.0521.0751.140SUMMARYBAMATotal1.052
1.087MaleFemale1.0961.050Count1212241.0251.161Sum12.349
12.925.2491.0001.096Average1.02908333331.0751.0520416667
0.9561.000Variance0.0066864470.00651981820.00686604171.0
001.0411.0431.043Female1.0431.119Count1212241.2101.043Su
m12.79112.78725.5781.1871.000Average1.06591666671.06558
333331.065751.0430.956Variance0.0061024470.00421281060.0
049334131.0431.1291.1451.149TotalCount2424Sum25.1425.68
7Average1.04751.0702916667Variance0.00647034780.0051561
286ANOVASource of VariationSSdfMSFP-valueF
critSample0.002255020810.00225502080.38348211710.5389389
5074.0617064601 (This is the row variable or
gender.)Columns0.006233520810.00623352081.06005396090.3
0882956334.0617064601 (This is the column variable or
15. Degree.)Interaction0.006417187510.00641718751.09128776640
.30189150624.0617064601Within0.25873675440.0058803807To
tal0.273642479247Interpretation:For Ho: Average compas by
gender are equalHa: Average compas by gender are not
equalWhat is the p-value:Is P-value < 0.05?Do you reject or not
reject the null hypothesis:If the null hypothesis was rejected,
what is the effect size value (eta squared):Meaning of effect
size measure:For Ho: Average salaries are equal for all grades
Ha: Average salaries are not equal for all gradesWhat is the p-
value:Is P-value < 0.05?Do you reject or not reject the null
hypothesis:If the null hypothesis was rejected, what is the
effect size value (eta squared):Meaning of effect size
measure:For: Ho: Interaction is not significantHa: Interaction is
significantWhat is the p-value:Do you reject or not reject the
null hypothesis:If the null hypothesis was rejected, what is the
effect size value (eta squared):Meaning of effect size
measure:What do these decisions mean in terms of our equal
pay question:4Many companies consider the grade midpoint to
be the "market rate" - what is needed to hire a new
employee.MidpointSalaryDoes the company, on average, pay its
existing employees at or above the market rate?Null
Hypothesis:Alt. Hypothesis:Statistical test to use:Place the
cursor in B160 for correl.What is the p-value:Is P-value <
0.05?Do we REJ or Not reject the null?If the null hypothesis
was rejected, what is the effect size value:Since the effect size
was not discussed in this chapter, we do not have a formula for
it - it differs from the non-paired t.Meaning of effect size
measure:NAInterpretation:5. Using the results up thru this
week, what are your conclusions about gender equal pay for
equal work at this point?
Week 4Week 4Confidence Intervals and Chi Square (Chs 11 -
12)For questions 3 and 4 below, be sure to list the null and
alternate hypothesis statements. Use .05 for your significance
level in making your decisions.For full credit, you need to also
show the statistical outcomes - either the Excel test result or the
calculations you performed.1Using our sample data, construct a
16. 95% confidence interval for the population's mean salary for
each gender. Interpret the results. How do they compare with
the findings in the week 2 one sample t-test outcomes (Question
1)?MeanSt error t valueLow to HighMalesFemales<Reminder:
standard error is the sample standard deviation divided by the
square root of the sample size.>Interpretation:2Using our
sample data, construct a 95% confidence interval for the mean
salary difference between the genders in the population. How
does this compare to the findings in week 2, question
2?DifferenceSt Err.T valueLow to HighYes/NoCan the means be
equal?Why?How does this compare to the week 2, question 2
result (2 sampe t-test)?a.Why is using a two sample tool (t-test,
confidence interval) a better choice than using 2 one-sample
techniques when comparing two samples?3We found last week
that the degrees compa values within the population. do not
impact compa rates. This does not mean that degrees are
distributed evenly across the grades and genders.Do males and
females have athe same distribution of degrees by grade?(Note:
while technically the sample size might not be large enough to
perform this test, ignore this limitation for this exercise.)What
are the hypothesis statements:Ho: Ha:Note: You can either use
the Excel Chi-related functions or do the calculations
manually.Data input tables - graduate degrees by gender and
grade levelOBSERVEDA BCDEFTotalDo manual calculations
per cell here (if desired)M GradA BCDEFFem GradM GradMale
UndFem GradFemale UndMale UndFemale UndSum
=EXPECTEDM GradFor this exercise - ignore the requirement
for a correctionFem Gradfor expected values less than 5.Male
UndFemale UndInterpretation:What is the value of the chi
square statistic: What is the p-value associated with this value:
Is the p-value <0.05?Do you reject or not reject the null
hypothesis: If you rejected the null, what is the Cramer's V
correlation:What does this correlation mean?What does this
decision mean for our equal pay question: 4Based on our sample
data, can we conclude that males and females are distributed
across grades in a similar patternwithin the population?What are
17. the hypothesis statements:Ho: Ha:Do manual calculations per
cell here (if desired)A BCDEFA BCDEFOBS COUNT -
mMOBS COUNT - fFSum = EXPECTEDWhat is the value of
the chi square statistic: What is the p-value associated with this
value: Is the p-value <0.05?Do you reject or not reject the null
hypothesis: If you rejected the null, what is the Phi
correlation:What does this correlation mean?What does this
decision mean for our equal pay question: 5. How do you
interpret these results in light of our question about equal pay
for equal work?
Week 5Week 5 Correlation and Regression1. Create a
correlation table for the variables in our data set. (Use analysis
ToolPak or StatPlus:mac LE function Correlation.)a. Reviewing
the data levels from week 1, what variables can be used in a
Pearson's Correlation table (which is what Excel produces)?b.
Place table here (C8 in Output range box):c.Using r =
approximately .28 as the signicant r value (at p = 0.05) for a
correlation between 50 values, what variables aresignificantly
related to Salary?To compa?d.Looking at the above correlations
- both significant or not - are there any surprises -by that I mean
any relationships you expected to be meaningful and are not and
vice-versa?e.Does this help us answer our equal pay for equal
work question?2Below is a regression analysis for salary being
predicted/explained by the other variables in our sample
(Midpoint, age, performance rating, service, gender, and degree
variables. (Note: since salary and compa are different ways of
expressing an employee’s salary, we do not want to have both
used in the same regression.)Plase interpret the findings.Ho:
The regression equation is not significant.Ha: The regression
equation is significant.Ho: The regression coefficient for each
variable is not significant Note: technically we have one for
each input variable.Ha: The regression coefficient for each
variable is significant Listing it this way to save
space.SalSUMMARY OUTPUTRegression StatisticsMultiple
R0.9915590747R Square0.9831893985Adjusted R
Square0.9808437332Standard
18. Error2.6575925726Observations50ANOVAdfSSMSFSignificanc
e
FRegression617762.29967387432960.383278979419.151611129
41.8121523852609E-
36Residual43303.70032612577.062798282Total4918066Coeffic
ientsStandard Errort StatP-valueLower 95%Upper 95%Lower
95.0%Upper 95.0%Intercept-1.74962121233.6183676583-
0.48353881570.6311664899-9.04675504275.547512618-
9.04675504275.547512618Midpoint1.21670105050.0319023509
38.13828811638.66416336978111E-
351.15236382831.28103827271.15236382831.2810382727Age-
0.00462801020.065197212-0.07098478760.9437389875-
0.13611071910.1268546987-
0.13611071910.1268546987Performace Rating-
0.05659644050.0344950678-1.64071109710.1081531819-
0.12616237470.0129694936-
0.12616237470.0129694936Service-
0.04250035730.0843369821-0.50393500330.6168793519-
0.21258209120.1275813765-
0.21258209120.1275813765Gender2.4203372120.86084431762.
81158528040.00739661880.6842791924.1563952320.68427919
24.156395232Degree0.27553341430.79980230480.34450190090
.732148119-1.33742165471.8884884833-
1.33742165471.8884884833Note: since Gender and Degree are
expressed as 0 and 1, they are considered dummy variables and
can be used in a multiple regression equation.Interpretation:For
the Regression as a whole:What is the value of the F statistic:
What is the p-value associated with this value: Is the p-value
<0.05?Do you reject or not reject the null hypothesis: What
does this decision mean for our equal pay question: For each of
the coefficients:InterceptMidpointAgePerf.
Rat.ServiceGenderDegreeWhat is the coefficient's p-value for
each of the variables: Is the p-value < 0.05?Do you reject or not
reject each null hypothesis: What are the coefficients for the
significant variables?Using only the significant variables, what
is the equation?Salary =Is gender a significant factor in
19. salary:If so, who gets paid more with all other things being
equal?How do we know? 3Perform a regression analysis using
compa as the dependent variable and the same
independentvariables as used in question 2. Show the result,
and interpret your findings by answering the same
questions.Note: be sure to include the appropriate hypothesis
statements.Regression hypothesesHo:Ha:Coefficient hypotheses
(one to stand for all the separate variables)Ho:Ha:Put C94 in
output range boxInterpretation:For the Regression as a
whole:What is the value of the F statistic: What is the p-value
associated with this value: Is the p-value < 0.05?Do you reject
or not reject the null hypothesis: What does this decision mean
for our equal pay question: For each of the coefficients:
InterceptMidpointAgePerf. Rat.ServiceGenderDegreeWhat is
the coefficient's p-value for each of the variables: Is the p-value
< 0.05?Do you reject or not reject each null hypothesis: What
are the coefficients for the significant variables?Using only the
significant variables, what is the equation?Compa = Is gender a
significant factor in compa:If so, who gets paid more with all
other things being equal?How do we know? 4Based on all of
your results to date, do we have an answer to the question of are
males and females paid equally for equal work?If so, which
gender gets paid more? How do we know?Which is the best
variable to use in analyzing pay practices - salary or compa?
Why?What is most interesting or surprising about the results we
got doing the analysis during the last 5 weeks?5Why did the
single factor tests and analysis (such as t and single factor
ANOVA tests on salary equality) not provide a complete answer
to our salary equality question?What outcomes in your life or
work might benefit from a multiple regression examination
rather than a simpler one variable test?
DataSee comments at the right of the data
set.IDSalaryCompaMidpointAgePerformance
RatingServiceGenderRaiseDegreeGender1Grade8231.000233290
915.80FAThe ongoing question that the weekly assignments
20. will focus on is: Are males and females paid the same for equal
work (under the Equal Pay Act)?
10220.956233080714.70FANote: to simplfy the analysis, we
will assume that jobs within each grade comprise equal
work.11231.00023411001914.80FA14241.04323329012160FAT
he column labels in the table
mean:15241.043233280814.90FAID – Employee sample number
Salary – Salary in thousands 23231.000233665613.31FAAge
– Age in yearsPerformance Rating – Appraisal rating
(Employee evaluation score)26241.043232295216.21FAService
– Years of service (rounded)Gender: 0 = male, 1 = female
31241.043232960413.90FAMidpoint – salary grade midpoint
Raise – percent of last raise35241.043232390415.31FAGrade –
job/pay gradeDegree (0= BSBA 1 =
MS)36231.000232775314.31FAGender1 (Male or
Female)Compa - salary divided by
midpoint37220.956232295216.21FA42241.0432332100815.70F
A19241.043233285104.61MA25241.0432341704040MA40251.0
86232490206.30MA3341.096313075513.60FB18361.161313180
1115.61FB20341.0963144701614.81FB39351.129312790615.51
FB2270.870315280703.90MB32280.903312595405.60MB34280
.903312680204.91MB7411.0254032100815.70FC13421.050403
0100214.71FC16471.175404490405.70MC27401.000403580703
.91MC41431.075402580504.30MC22571.187484865613.80FD2
4501.041483075913.81FD45551.145483695815.20FD5470.9794
836901605.71MD30491.0204845901804.30MD17691.21057275
53130FE48651.1405734901115.31FE1581.017573485805.70ME
4661.15757421001605.51ME12601.0525752952204.50ME33641
.122573590905.51ME38560.9825745951104.50ME44601.05257
45901605.21ME46651.1405739752003.91ME47621.0875737955
05.51ME49601.0525741952106.60ME50661.1575738801204.60
ME28751.119674495914.41FF43771.1496742952015.51FF6761.
1346736701204.51MF9771.149674910010041MF21761.134674
3951306.31MF29721.074675295505.40MF
Week 1Week 1.Measurement and Description - chapters 1 and
21Measurement issues. Data, even numerically coded variables,
21. can be one of 4 levels - nominal, ordinal, interval, or ratio. It is
important to identify which level a variable is, asthis impact the
kind of analysis we can do with the data. For example,
descriptive statistics such as means can only be done on interval
or ratio level data.Please list under each label, the variables in
our data set that belong in each
group.NominalOrdinalIntervalRatiob.For each variable that you
did not call ratio, why did you make that decision?2The first
step in analyzing data sets is to find some summary descriptive
statistics for key variables.For salary, compa, age, performance
rating, and service; find the mean, standard deviation, and range
for 3 groups: overall sample, Females, and Males.You can use
either the Data Analysis Descriptive Statistics tool or the Fx
=average and =stdev functions. (the range must be found using
the difference between the =max and =min functions with Fx)
functions.Note: Place data to the right, if you use Descriptive
statistics, place that to the right as well.SalaryCompaAgePerf.
Rat.ServiceOverallMeanStandard
DeviationRangeFemaleMeanStandard
DeviationRangeMaleMeanStandard DeviationRange3What is the
probability for a:Probabilitya. Randomly selected person
being a male in grade E?b. Randomly selected male being in
grade E? Note part b is the same as given a male, what is
probabilty of being in grade E?c. Why are the results
different?4For each group (overall, females, and males)
find:OverallFemaleMalea.The value that cuts off the top 1/3
salary in each group.b.The z score for each value:c.The normal
curve probability of exceeding this score:d.What is the
empirical probability of being at or exceeding this salary
value?e.The value that cuts off the top 1/3 compa in each
group.f.The z score for each value:g.The normal curve
probability of exceeding this score:h.What is the empirical
probability of being at or exceeding this compa value?i.How do
you interpret the relationship between the data sets? What do
they mean about our equal pay for equal work question?5.
What conclusions can you make about the issue of male and
22. female pay equality? Are all of the results consistent? What is
the difference between the sal and compa measures of
pay?Conclusions from looking at salary results:Conclusions
from looking at compa results:Do both salary measures show
the same results?Can we make any conclusions about equal pay
for equal work yet?
Week 2 Week 2Testing meansQ3In questions 2 and 3, be sure to
include the null and alternate hypotheses you will be testing.
HoFemaleMaleFemaleIn the first 3 questions use alpha = 0.05 in
making your decisions on rejecting or not rejecting the null
hypothesis.45341.0171.09645410.8701.0251Below are 2 one-
sample t-tests comparing male and female average salaries to
the overall sample mean. 45231.1571.000(Note: a one-sample
t-test in Excel can be performed by selecting the 2-sample
unequal variance t-test and making the second variable = Ho
value -- see column S)45220.9790.956Based on our sample, how
do you interpret the results and what do these results suggest
about the population means for male and female average
salaries?45231.1341.000MalesFemales45421.1491.050Ho: Mean
salary = 45Ho: Mean salary = 4545241.0521.043Ha: Mean
salary =/= 45Ha: Mean salary =/=
4545241.1751.04345691.0431.210Note: While the results both
below are actually from Excel's t-Test: Two-Sample Assuming
Unequal Variances, 45361.1341.161having no variance in the
Ho variable makes the calculations default to the one-sample t-
test outcome - we are tricking Excel into doing a one sample
test for
us.45341.0431.096MaleHoFemaleHo45571.0001.187Mean5245
Mean384545231.0741.000Variance3160Variance334.666666666
7045501.0201.041Observations2525Observations252545240.90
31.043Hypothesized Mean Difference0Hypothesized Mean
Difference045751.1221.119df24df2445240.9031.043t
Stat1.9689038266t Stat-1.913206357345240.9821.043P(T<=t)
one-tail0.0303078503P(T<=t) one-
tail0.033862118445231.0861.000t Critical one-
tail1.7108820799t Critical one-
23. tail1.710882079945221.0750.956P(T<=t) two-
tail0.0606157006P(T<=t) two-
tail0.067724236945351.0521.129t Critical two-
tail2.0638985616t Critical two-
tail2.063898561645241.1401.043Conclusion: Do not reject Ho;
mean equals 45Conclusion: Do not reject Ho; mean equals
4545771.0871.149Is this a 1 or 2 tail test?Is this a 1 or 2 tail
test?- why?- why?P-value is:P-value is:45551.0521.145Is P-
value > 0.05?Is P-value > 0.05?45651.1571.140Why do we not
reject Ho?Why do we not reject Ho?Interpretation:2Based on
our sample data set, perform a 2-sample t-test to see if the
population male and female average salaries could be equal to
each other.(Since we have not yet covered testing for variance
equality, assume the data sets have statistically equal
variances.)Ho: Ha: Test to use:Place B43 in Outcome range
box.P-value is:Is P-value < 0.05?Reject or do not reject Ho:If
the null hypothesis was rejected, what is the effect size
value:Meaning of effect size measure:Interpretation:b.Since the
one and two tail t-test results provided different outcomes,
which is the proper/correct apporach to comparing salary
equality? Why?3Based on our sample data set, can the male and
female compas in the population be equal to each other?
(Another 2-sample t-test.)Ho:Ha:Statistical test to use:Place
B75 in Outcome range box.What is the p-value:Is P-value <
0.05?Reject or do not reject Ho:If the null hypothesis was
rejected, what is the effect size value:Meaning of effect size
measure: Interpretation: 4Since performance is often a factor in
pay levels, is the average Performance Rating the same for both
genders?Ho:Ha:Test to use:Place B106 in Outcome range
box.What is the p-value:Is P-value < 0.05?Do we REJ or Not
reject the null?If the null hypothesis was rejected, what is the
effect size value:Meaning of effect size
measure:Interpretation:5If the salary and compa mean tests in
questions 2 and 3 provide different results about male and
female salary equality, which would be more appropriate to
use in answering the question about salary equity? Why?What
24. are your conclusions about equal pay at this point?
Week 3Week 3At this point we know the following about male
and female salaries.a.Male and female overall average salaries
are not equal in the population.b.Male and female overall
average compas are equal in the population, but males are a bit
more spread out.c.The male and female salary range are almost
the same, as is their age and service.d. Average performance
ratings per gender are equal.Let's look at some other factors that
might influence pay - education(degree) and performance
ratings.1Last week, we found that average performance ratings
do not differ between males and females in the population.Now
we need to see if they differ among the grades. Is the average
performace rating the same for all grades?(Assume variances
are equal across the grades for this
ANOVA.)ABCDEF9075100655595Null Hypothesis:performance
rating average is the same for all grades8080100759095Alt.
Hypothesis:perfomance rating is not the same for all
grades1007090958570Place B17 in Outcome range
box.90908090100100Anova: Single
Factor80808090959565959095SUMMARY958095GroupsCountS
umAverageVariance6090Column
115126584.3333333333153.09523809529075Column
2757081.428571428672.6190476197595Column
35450901009595Column 4541583157.510080Column
512104587.0833333333152.083333333385Column
6655091.6666666667116.66666666677090ANOVASource of
VariationSSdfMSFP-valueF critBetween
Groups519.20238095245103.84047619050.77898535580.57021
547742.4270401198Within
Groups5865.297619047644133.3022186147Total6384.549Interp
retation:What is the p-value:0.5702154774Is P-value <
0.05?noDo we REJ or Not reject the null?acceptIf the null
hypothesis was rejected, what is the effect size value (eta
squared):0.0813223245Meaning of effect size measure:The
effect is moderateWhat does that decision mean in terms of our
equal pay question:It would seem that despite the pay, the
25. employees are putting in equal efforts despite the pay and even
the grades2While it appears that average salaries per each grade
differ, we need to test this assumption. Is the average salary the
same for each of the grade levels? (Assume equal variance, and
use the analysis toolpak function ANOVA.) Use the input table
to the right to list salaries under each grade level.Null
Hypothesis:Average salaries are the same for all gradesAlt.
Hypothesis:Average salaries are not the same for all
gradesABCDEF233441576975223642506577Place B55 in
Outcome range box.233447555876Anova: Single
Factor243540476677242743496076SUMMARY23286472Groups
CountSumAverageVariance242856Column
11535323.53333333330.69523809522460Column
2722231.714285714314.90476190482465Column
3521342.67.32362Column 4525851.617.82260Column
51275162.583333333314.81060606062466Column
6645375.53.5242425ANOVASource of VariationSSdfMSFP-
valueF critBetween
Groups17686.021428571453537.2042857143409.59411996921.
03856156092.4270401198Within
Groups379.9785714286448.6358766234Total1806649What is
the p-value:1.0385615609Is P-value < 0.05?yesDo you reject or
not reject the null hypothesis:RejectIf the null hypothesis was
rejected, what is the effect size value (eta
squared):0.0210328004Meaning of effect size
measure:SignificantInterpretation:The salaries are not the same
for all grades, our null hypothesis is true3The table and analysis
below demonstrate a 2-way ANOVA with replication. Please
interpret the results.BAMAHo: Average compas by gender are
equalMale1.0171.157Ha: Average compas by gender are not
equal0.8700.979Ho: Average compas are equal for each
degree1.0521.134Ho: Average compas are not equal for each
degree1.1751.149Ho: Interaction is not
significant1.0431.043Ha: Interaction is
significant1.0741.1341.0201.000Perform
analysis:0.9031.1220.9820.903Anova: Two-Factor With
27. employee.MidpointSalaryDoes the company, on average, pay its
existing employees at or above the market
rate?232323222323Null Hypothesis:The company pays above
the market rate2324Alt. Hypothesis:The company does not pay
above market rate23242323Statistical test to
use:Correlation23242324Place the cursor in B160 for
correl.2324Column 1Column 22323Column 112322Column
20.98897178271232423242324232531343136313431353127312
831284041404240474040What is the p-value:Not Known4043Is
P-value < 0.05?Not Known4857Do we REJ or Not reject the
null?Not Known4850If the null hypothesis was rejected, what
is the effect size value:Since the effect size was not discussed
in this chapter, we do not have a formula for it - it differs from
the non-paired t.4855Meaning of effect size measure:Not
Known48474849Interpretation:The variables are postively
correlated at 0.98 factor5769576557585. Using the results up
thru this week, what are your conclusions about gender equal
pay for equal work at this point?5766The company does not pay
equally as per gender or as per the grades despite the fact that
all the perfomance ratings are averagely
equal576057645756576057655762576057666775677767766777
67766772
Week 4Week 4Confidence Intervals and Chi Square (Chs 11 -
12)For questions 3 and 4 below, be sure to list the null and
alternate hypothesis statements. Use .05 for your significance
level in making your decisions.For full credit, you need to also
show the statistical outcomes - either the Excel test result or the
calculations you performed.1Using our sample data, construct a
95% confidence interval for the population's mean salary for
each gender. Interpret the results. How do they compare with
the findings in the week 2 one sample t-test outcomes (Question
1)?MeanSt error t valueLow to HighMalesFemales<Reminder:
standard error is the sample standard deviation divided by the
square root of the sample size.>Interpretation:2Using our
sample data, construct a 95% confidence interval for the mean
salary difference between the genders in the population. How
28. does this compare to the findings in week 2, question
2?DifferenceSt Err.T valueLow to HighYes/NoCan the means be
equal?Why?How does this compare to the week 2, question 2
result (2 sampe t-test)?a.Why is using a two sample tool (t-test,
confidence interval) a better choice than using 2 one-sample
techniques when comparing two samples?3We found last week
that the degrees compa values within the population. do not
impact compa rates. This does not mean that degrees are
distributed evenly across the grades and genders.Do males and
females have athe same distribution of degrees by grade?(Note:
while technically the sample size might not be large enough to
perform this test, ignore this limitation for this exercise.)What
are the hypothesis statements:Ho: Ha:Note: You can either use
the Excel Chi-related functions or do the calculations
manually.Data input tables - graduate degrees by gender and
grade levelOBSERVEDA BCDEFTotalDo manual calculations
per cell here (if desired)M GradA BCDEFFem GradM GradMale
UndFem GradFemale UndMale UndFemale UndSum
=EXPECTEDM GradFor this exercise - ignore the requirement
for a correctionFem Gradfor expected values less than 5.Male
UndFemale UndInterpretation:What is the value of the chi
square statistic: What is the p-value associated with this value:
Is the p-value <0.05?Do you reject or not reject the null
hypothesis: If you rejected the null, what is the Cramer's V
correlation:What does this correlation mean?What does this
decision mean for our equal pay question: 4Based on our sample
data, can we conclude that males and females are distributed
across grades in a similar patternwithin the population?What are
the hypothesis statements:Ho: Ha:Do manual calculations per
cell here (if desired)A BCDEFA BCDEFOBS COUNT -
mMOBS COUNT - fFSum = EXPECTEDWhat is the value of
the chi square statistic: What is the p-value associated with this
value: Is the p-value <0.05?Do you reject or not reject the null
hypothesis: If you rejected the null, what is the Phi
correlation:What does this correlation mean?What does this
decision mean for our equal pay question: 5. How do you
29. interpret these results in light of our question about equal pay
for equal work?
Week 5Week 5 Correlation and Regression1. Create a
correlation table for the variables in our data set. (Use analysis
ToolPak or StatPlus:mac LE function Correlation.)a. Reviewing
the data levels from week 1, what variables can be used in a
Pearson's Correlation table (which is what Excel produces)?b.
Place table here (C8 in Output range box):c.Using r =
approximately .28 as the signicant r value (at p = 0.05) for a
correlation between 50 values, what variables aresignificantly
related to Salary?To compa?d.Looking at the above correlations
- both significant or not - are there any surprises -by that I mean
any relationships you expected to be meaningful and are not and
vice-versa?e.Does this help us answer our equal pay for equal
work question?2Below is a regression analysis for salary being
predicted/explained by the other variables in our sample
(Midpoint, age, performance rating, service, gender, and degree
variables. (Note: since salary and compa are different ways of
expressing an employee’s salary, we do not want to have both
used in the same regression.)Plase interpret the findings.Ho:
The regression equation is not significant.Ha: The regression
equation is significant.Ho: The regression coefficient for each
variable is not significant Note: technically we have one for
each input variable.Ha: The regression coefficient for each
variable is significant Listing it this way to save
space.SalSUMMARY OUTPUTRegression StatisticsMultiple
R0.9915590747R Square0.9831893985Adjusted R
Square0.9808437332Standard
Error2.6575925726Observations50ANOVAdfSSMSFSignificanc
e
FRegression617762.29967387432960.383278979419.151611129
41.8121523852609E-
36Residual43303.70032612577.062798282Total4918066Coeffic
ientsStandard Errort StatP-valueLower 95%Upper 95%Lower
95.0%Upper 95.0%Intercept-1.74962121233.6183676583-
0.48353881570.6311664899-9.04675504275.547512618-
31. output range boxInterpretation:For the Regression as a
whole:What is the value of the F statistic: What is the p-value
associated with this value: Is the p-value < 0.05?Do you reject
or not reject the null hypothesis: What does this decision mean
for our equal pay question: For each of the coefficients:
InterceptMidpointAgePerf. Rat.ServiceGenderDegreeWhat is
the coefficient's p-value for each of the variables: Is the p-value
< 0.05?Do you reject or not reject each null hypothesis: What
are the coefficients for the significant variables?Using only the
significant variables, what is the equation?Compa = Is gender a
significant factor in compa:If so, who gets paid more with all
other things being equal?How do we know? 4Based on all of
your results to date, do we have an answer to the question of are
males and females paid equally for equal work?If so, which
gender gets paid more? How do we know?Which is the best
variable to use in analyzing pay practices - salary or compa?
Why?What is most interesting or surprising about the results we
got doing the analysis during the last 5 weeks?5Why did the
single factor tests and analysis (such as t and single factor
ANOVA tests on salary equality) not provide a complete answer
to our salary equality question?What outcomes in your life or
work might benefit from a multiple regression examination
rather than a simpler one variable test?
DataSee comments at the right of the data
set.IDSalaryCompaMidpointAgePerformance
RatingServiceGenderRaiseDegreeGender1Grade8231.000233290
915.80FAThe ongoing question that the weekly assignments
will focus on is: Are males and females paid the same for equal
work (under the Equal Pay Act)?
10220.956233080714.70FANote: to simplfy the analysis, we
will assume that jobs within each grade comprise equal
work.11231.00023411001914.80FA14241.04323329012160FAT
he column labels in the table
mean:15241.043233280814.90FAID – Employee sample number
Salary – Salary in thousands 23231.000233665613.31FAAge
32. – Age in yearsPerformance Rating – Appraisal rating
(Employee evaluation score)26241.043232295216.21FAService
– Years of service (rounded)Gender: 0 = male, 1 = female
31241.043232960413.90FAMidpoint – salary grade midpoint
Raise – percent of last raise35241.043232390415.31FAGrade –
job/pay gradeDegree (0= BSBA 1 =
MS)36231.000232775314.31FAGender1 (Male or
Female)Compa - salary divided by
midpoint37220.956232295216.21FA42241.0432332100815.70F
A3341.096313075513.60FB18361.1613131801115.61FB20341.0
963144701614.81FB39351.129312790615.51FB7411.025403210
0815.70FC13421.0504030100214.71FC22571.187484865613.80
FD24501.041483075913.81FD45551.145483695815.20FD17691
.2105727553130FE48651.1405734901115.31FE28751.11967449
5914.41FF43771.1496742952015.51FF19241.043233285104.61
MA25241.0432341704040MA40251.086232490206.30MA2270.
870315280703.90MB32280.903312595405.60MB34280.903312
680204.91MB16471.175404490405.70MC27401.000403580703.
91MC41431.075402580504.30MC5470.9794836901605.71MD3
0491.0204845901804.30MD1581.017573485805.70ME4661.157
57421001605.51ME12601.0525752952204.50ME33641.1225735
90905.51ME38560.9825745951104.50ME44601.0525745901605
.21ME46651.1405739752003.91ME47621.087573795505.51ME
49601.0525741952106.60ME50661.1575738801204.60ME6761.
1346736701204.51MF9771.149674910010041MF21761.134674
3951306.31MF29721.074675295505.40MF
Week 4Week 4Confidence Intervals and Chi Square (Chs 11 -
12)For questions 3 and 4 below, be sure to list the null and
alternate hypothesis statements. Use .05 for your significance
level in making your decisions.For full credit, you need to also
show the statistical outcomes - either the Excel test result or the
calculations you performed.1Using our sample data, construct a
95% confidence interval for the population's mean salary for
each gender. Interpret the results. How do they compare with
the findings in the week 2 one sample t-test outcomes (Question
1)?MeanSt error t valueLow to
33. HighMales523.55527776692.063898547344.6659.34Females383
.65877939572.063898547330.4545.55<Reminder: standard error
is the sample standard deviation divided by the square root of
the sample size.>Interpretation:If repeated observations are
taken, the mean salary of male employees is expected to lie
within 44.66 to 59.34 thousands about 95% of the time. If
repeated observations are taken, the mean salary of female
employees is expected to lie within 30.45 to 45.55 thousands
about 95% of the time. As per our previous findings in week 2
one sample t-test outcomes, the mean salary of the male
employees is 52 thousands and there is no evidence to suggest
that the mean salary of the male employees is significantly
different from the mean salary of the population, which is 45
thousand. The mean salary of the female employees is 38
thousands and there is no evidence to suggest that the mean
salary of the female employees is significantly different from
the mean salary of the popululation.The 95% confidence
intervals for the salaries of the male and the female employees
both contain the population mean salary of 45 thousand. This is
in accordance with our previous findings that the mean salary of
the male and female employees are not significantly different
from the mean salary of the popululation.2Using our sample
data, construct a 95% confidence interval for the mean salary
difference between the genders in the population. How does
this compare to the findings in week 2, question 2?DifferenceSt
Err.T valueLow to
High145.10163372532.01063472193.742478093524.257521906
5Yes/NoCan the means be equal?NoWhy?The confidence
interval for the difference of means does not contain 0. How
does this compare to the week 2, question 2 result (2 sampe t-
test)?As the confidence interval for the population's mean salary
difference for male and female employees does not include 0,
we can conclude that there is a significant difference between
the means at 95% level of confidence. This is in accordance
with our previous findings of week 2 two sample t-test outcome
that the mean salary of the male employees is significantly
34. different from the mean salary of the female employees, at 95%
level of confidence.a.Why is using a two sample tool (t-test,
confidence interval) a better choice than using 2 one-sample
techniques when comparing two samples?It reduces the number
of errors due to approximations. Females3We found last week
that the degrees compa values within the population.Count of
DegreeColumn Labels do not impact compa rates. This does not
mean that degrees are distributed evenly across the grades and
genders.Row LabelsABCDEFGrand TotalDo males and females
have athe same distribution of degrees by
grade?07112112(Note: while technically the sample size might
not be large enough to perform this test, ignore this limitation
for this exercise.)153111213Grand Total124232225What are the
hypothesis statements:Ho: Males and females have the same
distribution of degrees by grade.Ha:Males and females do not
have the same distribution of degrees by grade.Count of
DegreeColumn LabelsNote: You can either use the Excel Chi-
related functions or do the calculations manually.Row
LabelsABCDEFGrand TotalData input tables - graduate degrees
by gender and grade level022215113OBSERVEDA
BCDEFTotalDo manual calculations per cell here (if
desired)111115312M Grad11115312A BCDEFGrand
Total333210425Fem Grad53111213M
Grad1.87780.27520.03330.03331.56061.6900Male
Und22215113Fem
Grad0.31030.76510.06920.06921.44050.1241Female
Und71121012Male
Und0.92560.01780.37690.06921.13280.2010Total1575512650Fe
male Und3.21110.27520.03330.53331.22721.4400Sum
=17.6923076923EXPECTEDM Grad3.61.681.21.22.881.44For
this exercise - ignore the requirement for a correctionFem
Grad3.91.821.31.33.121.56for expected values less than 5.Male
Und3.91.821.31.33.121.56Female
Und3.61.681.21.22.881.44Interpretation:What is the value of
the chi square statistic: 17.6923076923What is the p-value
associated with this value: 0.2791871758Is the p-value
35. <0.05?NoDo you reject or not reject the null hypothesis: We do
not reject the null hypothesis.If you rejected the null, what is
the Cramer's V correlation:Not ApplicableWhat does this
correlation mean?Not ApplicableWhat does this decision mean
for our equal pay question: There is no evidence to suggest that
male and female employees have different distribution of
degrees by grade, at significance level of 0.05.4Based on our
sample data, can we conclude that males and females are
distributed across grades in a similar patternwithin the
population?What are the hypothesis statements:Count of
Gender1Column LabelsHo: Males and females have same
distribution across grades. Row LabelsABCDEFGrand
TotalHa:Males and females have different distribution across
grades. F124232225M333210425Do manual calculations per
cell here (if desired)Grand Total1575512650A BCDEFTotalA
BCDEOBS COUNT -
m333210425M2.70000.07140.10000.10002.6667OBS COUNT -
f124232225F2.70000.07140.10000.10002.6667Total1575512650
Sum = 11.2762EXPECTED7.53.52.52.5637.53.52.52.563What is
the value of the chi square statistic: 11.2762What is the p-value
associated with this value: 0.0461707397Is the p-value
<0.05?YesDo you reject or not reject the null hypothesis: We
reject the null hypothesis.If you rejected the null, what is the
Phi correlation:0.96937224What does this correlation mean?The
extent of relationship between gender and grades is strong.What
does this decision mean for our equal pay question: Males and
females have different distribution across grades, and this might
be accountable for the difference in the mean salaries of males
and females.5. How do you interpret these results in light of
our question about equal pay for equal work?The mean salaries
of males are significantly higher that the mean salaries of
females, at significance level of 0.05. The distribution of males
and females across grades are also significantly different, at
significance level of 0.05. This difference might be the
underlying factor for the difference in the mean salaries of
males and females.
36. DataSee comments at the right of the data
set.IDSalaryCompaMidpointAgePerformance
RatingServiceGenderRaiseDegreeGender1Grade8231.000233290
915.80FAThe ongoing question that the weekly assignments
will focus on is: Are males and females paid the same for equal
work (under the Equal Pay Act)?
10220.956233080714.70FANote: to simplfy the analysis, we
will assume that jobs within each grade comprise equal
work.11231.00023411001914.80FA14241.04323329012160FAT
he column labels in the table
mean:15241.043233280814.90FAID – Employee sample number
Salary – Salary in thousands 23231.000233665613.31FAAge
– Age in yearsPerformance Rating – Appraisal rating
(Employee evaluation score)26241.043232295216.21FAService
– Years of service (rounded)Gender: 0 = male, 1 = female
31241.043232960413.90FAMidpoint – salary grade midpoint
Raise – percent of last raise35241.043232390415.31FAGrade –
job/pay gradeDegree (0= BSBA 1 =
MS)36231.000232775314.31FAGender1 (Male or
Female)Compa - salary divided by
midpoint37220.956232295216.21FA42241.0432332100815.70F
A3341.096313075513.60FB18361.1613131801115.61FB20341.0
963144701614.81FB39351.129312790615.51FB7411.025403210
0815.70FC13421.0504030100214.71FC22571.187484865613.80
FD24501.041483075913.81FD45551.145483695815.20FD17691
.2105727553130FE48651.1405734901115.31FE28751.11967449
5914.41FF43771.1496742952015.51FF19241.043233285104.61
MA25241.0432341704040MA40251.086232490206.30MA2270.
870315280703.90MB32280.903312595405.60MB34280.903312
680204.91MB16471.175404490405.70MC27401.000403580703.
91MC41431.075402580504.30MC5470.9794836901605.71MD3
0491.0204845901804.30MD1581.017573485805.70ME4661.157
57421001605.51ME12601.0525752952204.50ME33641.1225735
90905.51ME38560.9825745951104.50ME44601.0525745901605
.21ME46651.1405739752003.91ME47621.087573795505.51ME
37. 49601.0525741952106.60ME50661.1575738801204.60ME6761.
1346736701204.51MF9771.149674910010041MF21761.134674
3951306.31MF29721.074675295505.40MF
Week 1Week 1.Measurement and Description - chapters 1 and
21Measurement issues. Data, even numerically coded variables,
can be one of 4 levels - nominal, ordinal, interval, or ratio. It is
important to identify which level a variable is, asthis impact the
kind of analysis we can do with the data. For example,
descriptive statistics such as means can only be done on interval
or ratio level data.Please list under each label, the variables in
our data set that belong in each
group.NominalOrdinalIntervalRatiob.For each variable that you
did not call ratio, why did you make that decision?2The first
step in analyzing data sets is to find some summary descriptive
statistics for key variables.For salary, compa, age, performance
rating, and service; find the mean, standard deviation, and range
for 3 groups: overall sample, Females, and Males.You can use
either the Data Analysis Descriptive Statistics tool or the Fx
=average and =stdev functions. (the range must be found using
the difference between the =max and =min functions with Fx)
functions.Note: Place data to the right, if you use Descriptive
statistics, place that to the right as well.SalaryCompaAgePerf.
Rat.ServiceOverallMeanStandard
DeviationRangeFemaleMeanStandard
DeviationRangeMaleMeanStandard DeviationRange3What is the
probability for a:Probabilitya. Randomly selected person
being a male in grade E?b. Randomly selected male being in
grade E? Note part b is the same as given a male, what is
probabilty of being in grade E?c. Why are the results
different?4For each group (overall, females, and males)
find:OverallFemaleMalea.The value that cuts off the top 1/3
salary in each group.b.The z score for each value:c.The normal
curve probability of exceeding this score:d.What is the
empirical probability of being at or exceeding this salary
value?e.The value that cuts off the top 1/3 compa in each
group.f.The z score for each value:g.The normal curve
38. probability of exceeding this score:h.What is the empirical
probability of being at or exceeding this compa value?i.How do
you interpret the relationship between the data sets? What do
they mean about our equal pay for equal work question?5.
What conclusions can you make about the issue of male and
female pay equality? Are all of the results consistent? What is
the difference between the sal and compa measures of
pay?Conclusions from looking at salary results:Conclusions
from looking at compa results:Do both salary measures show
the same results?Can we make any conclusions about equal pay
for equal work yet?
Week 2 Week 2Testing meansQ3In questions 2 and 3, be sure to
include the null and alternate hypotheses you will be testing.
HoFemaleMaleFemaleIn the first 3 questions use alpha = 0.05 in
making your decisions on rejecting or not rejecting the null
hypothesis.45341.0171.09645410.8701.0251Below are 2 one-
sample t-tests comparing male and female average salaries to
the overall sample mean. 45231.1571.000(Note: a one-sample
t-test in Excel can be performed by selecting the 2-sample
unequal variance t-test and making the second variable = Ho
value -- see column S)45220.9790.956Based on our sample, how
do you interpret the results and what do these results suggest
about the population means for male and female average
salaries?45231.1341.000MalesFemales45421.1491.050Ho: Mean
salary = 45Ho: Mean salary = 4545241.0521.043Ha: Mean
salary =/= 45Ha: Mean salary =/=
4545241.1751.04345691.0431.210Note: While the results both
below are actually from Excel's t-Test: Two-Sample Assuming
Unequal Variances, 45361.1341.161having no variance in the
Ho variable makes the calculations default to the one-sample t-
test outcome - we are tricking Excel into doing a one sample
test for
us.45341.0431.096MaleHoFemaleHo45571.0001.187Mean5245
Mean384545231.0741.000Variance3160Variance334.666666666
7045501.0201.041Observations2525Observations252545240.90
31.043Hypothesized Mean Difference0Hypothesized Mean
39. Difference045751.1221.119df24df2445240.9031.043t
Stat1.9689038266t Stat-1.913206357345240.9821.043P(T<=t)
one-tail0.0303078503P(T<=t) one-
tail0.033862118445231.0861.000t Critical one-
tail1.7108820799t Critical one-
tail1.710882079945221.0750.956P(T<=t) two-
tail0.0606157006P(T<=t) two-
tail0.067724236945351.0521.129t Critical two-
tail2.0638985616t Critical two-
tail2.063898561645241.1401.043Conclusion: Do not reject Ho;
mean equals 45Conclusion: Do not reject Ho; mean equals
4545771.0871.149Is this a 1 or 2 tail test?Is this a 1 or 2 tail
test?- why?- why?P-value is:P-value is:45551.0521.145Is P-
value > 0.05?Is P-value > 0.05?45651.1571.140Why do we not
reject Ho?Why do we not reject Ho?Interpretation:2Based on
our sample data set, perform a 2-sample t-test to see if the
population male and female average salaries could be equal to
each other.(Since we have not yet covered testing for variance
equality, assume the data sets have statistically equal
variances.)Ho: Ha: Test to use:Place B43 in Outcome range
box.P-value is:Is P-value < 0.05?Reject or do not reject Ho:If
the null hypothesis was rejected, what is the effect size
value:Meaning of effect size measure:Interpretation:b.Since the
one and two tail t-test results provided different outcomes,
which is the proper/correct apporach to comparing salary
equality? Why?3Based on our sample data set, can the male and
female compas in the population be equal to each other?
(Another 2-sample t-test.)Ho:Ha:Statistical test to use:Place
B75 in Outcome range box.What is the p-value:Is P-value <
0.05?Reject or do not reject Ho:If the null hypothesis was
rejected, what is the effect size value:Meaning of effect size
measure: Interpretation: 4Since performance is often a factor in
pay levels, is the average Performance Rating the same for both
genders?Ho:Ha:Test to use:Place B106 in Outcome range
box.What is the p-value:Is P-value < 0.05?Do we REJ or Not
reject the null?If the null hypothesis was rejected, what is the
40. effect size value:Meaning of effect size
measure:Interpretation:5If the salary and compa mean tests in
questions 2 and 3 provide different results about male and
female salary equality, which would be more appropriate to
use in answering the question about salary equity? Why?What
are your conclusions about equal pay at this point?
Week 3Week 3At this point we know the following about male
and female salaries.a.Male and female overall average salaries
are not equal in the population.b.Male and female overall
average compas are equal in the population, but males are a bit
more spread out.c.The male and female salary range are almost
the same, as is their age and service.d. Average performance
ratings per gender are equal.Let's look at some other factors that
might influence pay - education(degree) and performance
ratings.1Last week, we found that average performance ratings
do not differ between males and females in the population.Now
we need to see if they differ among the grades. Is the average
performace rating the same for all grades?(Assume variances
are equal across the grades for this ANOVA.)ABCDEFNull
Hypothesis:Alt. Hypothesis:Place B17 in Outcome range
box.Interpretation:What is the p-value:Is P-value < 0.05?Do we
REJ or Not reject the null?If the null hypothesis was rejected,
what is the effect size value (eta squared):Meaning of effect
size measure:What does that decision mean in terms of our
equal pay question:2While it appears that average salaries per
each grade differ, we need to test this assumption. Is the
average salary the same for each of the grade levels? (Assume
equal variance, and use the analysis toolpak function ANOVA.)
Use the input table to the right to list salaries under each grade
level.Null Hypothesis:Alt. Hypothesis:ABCDEFPlace B55 in
Outcome range box.What is the p-value:Is P-value < 0.05?Do
you reject or not reject the null hypothesis:If the null
hypothesis was rejected, what is the effect size value (eta
squared):Meaning of effect size measure:Interpretation:3The
table and analysis below demonstrate a 2-way ANOVA with
replication. Please interpret the results.BAMAHo: Average
41. compas by gender are equalMale1.0171.157Ha: Average compas
by gender are not equal0.8700.979Ho: Average compas are
equal for each degree1.0521.134Ho: Average compas are not
equal for each degree1.1751.149Ho: Interaction is not
significant1.0431.043Ha: Interaction is
significant1.0741.1341.0201.000Perform
analysis:0.9031.1220.9820.903Anova: Two-Factor With
Replication1.0861.0521.0751.140SUMMARYBAMATotal1.052
1.087MaleFemale1.0961.050Count1212241.0251.161Sum12.349
12.925.2491.0001.096Average1.02908333331.0751.0520416667
0.9561.000Variance0.0066864470.00651981820.00686604171.0
001.0411.0431.043Female1.0431.119Count1212241.2101.043Su
m12.79112.78725.5781.1871.000Average1.06591666671.06558
333331.065751.0430.956Variance0.0061024470.00421281060.0
049334131.0431.1291.1451.149TotalCount2424Sum25.1425.68
7Average1.04751.0702916667Variance0.00647034780.0051561
286ANOVASource of VariationSSdfMSFP-valueF
critSample0.002255020810.00225502080.38348211710.5389389
5074.0617064601 (This is the row variable or
gender.)Columns0.006233520810.00623352081.06005396090.3
0882956334.0617064601 (This is the column variable or
Degree.)Interaction0.006417187510.00641718751.09128776640
.30189150624.0617064601Within0.25873675440.0058803807To
tal0.273642479247Interpretation:For Ho: Average compas by
gender are equalHa: Average compas by gender are not
equalWhat is the p-value:Is P-value < 0.05?Do you reject or not
reject the null hypothesis:If the null hypothesis was rejected,
what is the effect size value (eta squared):Meaning of effect
size measure:For Ho: Average salaries are equal for all grades
Ha: Average salaries are not equal for all gradesWhat is the p-
value:Is P-value < 0.05?Do you reject or not reject the null
hypothesis:If the null hypothesis was rejected, what is the
effect size value (eta squared):Meaning of effect size
measure:For: Ho: Interaction is not significantHa: Interaction is
significantWhat is the p-value:Do you reject or not reject the
null hypothesis:If the null hypothesis was rejected, what is the
42. effect size value (eta squared):Meaning of effect size
measure:What do these decisions mean in terms of our equal
pay question:4Many companies consider the grade midpoint to
be the "market rate" - what is needed to hire a new
employee.MidpointSalaryDoes the company, on average, pay its
existing employees at or above the market rate?Null
Hypothesis:Alt. Hypothesis:Statistical test to use:Place the
cursor in B160 for correl.What is the p-value:Is P-value <
0.05?Do we REJ or Not reject the null?If the null hypothesis
was rejected, what is the effect size value:Since the effect size
was not discussed in this chapter, we do not have a formula for
it - it differs from the non-paired t.Meaning of effect size
measure:NAInterpretation:5. Using the results up thru this
week, what are your conclusions about gender equal pay for
equal work at this point?
Week 4Week 4Confidence Intervals and Chi Square (Chs 11 -
12)For questions 3 and 4 below, be sure to list the null and
alternate hypothesis statements. Use .05 for your significance
level in making your decisions.For full credit, you need to also
show the statistical outcomes - either the Excel test result or the
calculations you performed.1Using our sample data, construct a
95% confidence interval for the population's mean salary for
each gender. Interpret the results. How do they compare with
the findings in the week 2 one sample t-test outcomes (Question
1)?MeanSt error t valueLow to HighMalesFemales<Reminder:
standard error is the sample standard deviation divided by the
square root of the sample size.>Interpretation:2Using our
sample data, construct a 95% confidence interval for the mean
salary difference between the genders in the population. How
does this compare to the findings in week 2, question
2?DifferenceSt Err.T valueLow to HighYes/NoCan the means be
equal?Why?How does this compare to the week 2, question 2
result (2 sampe t-test)?a.Why is using a two sample tool (t-test,
confidence interval) a better choice than using 2 one-sample
techniques when comparing two samples?3We found last week
that the degrees compa values within the population. do not
43. impact compa rates. This does not mean that degrees are
distributed evenly across the grades and genders.Do males and
females have athe same distribution of degrees by grade?(Note:
while technically the sample size might not be large enough to
perform this test, ignore this limitation for this exercise.)What
are the hypothesis statements:Ho: Ha:Note: You can either use
the Excel Chi-related functions or do the calculations
manually.Data input tables - graduate degrees by gender and
grade levelOBSERVEDA BCDEFTotalDo manual calculations
per cell here (if desired)M GradA BCDEFFem GradM GradMale
UndFem GradFemale UndMale UndFemale UndSum
=EXPECTEDM GradFor this exercise - ignore the requirement
for a correctionFem Gradfor expected values less than 5.Male
UndFemale UndInterpretation:What is the value of the chi
square statistic: What is the p-value associated with this value:
Is the p-value <0.05?Do you reject or not reject the null
hypothesis: If you rejected the null, what is the Cramer's V
correlation:What does this correlation mean?What does this
decision mean for our equal pay question: 4Based on our sample
data, can we conclude that males and females are distributed
across grades in a similar patternwithin the population?What are
the hypothesis statements:Ho: Ha:Do manual calculations per
cell here (if desired)A BCDEFA BCDEFOBS COUNT -
mMOBS COUNT - fFSum = EXPECTEDWhat is the value of
the chi square statistic: What is the p-value associated with this
value: Is the p-value <0.05?Do you reject or not reject the null
hypothesis: If you rejected the null, what is the Phi
correlation:What does this correlation mean?What does this
decision mean for our equal pay question: 5. How do you
interpret these results in light of our question about equal pay
for equal work?
Week 5Week 5 Correlation and Regression1. Create a
correlation table for the variables in our data set. (Use analysis
ToolPak or StatPlus:mac LE function Correlation.)a. Reviewing
the data levels from week 1, what variables can be used in a
Pearson's Correlation table (which is what Excel produces)?b.
44. Place table here (C8 in Output range box):c.Using r =
approximately .28 as the signicant r value (at p = 0.05) for a
correlation between 50 values, what variables aresignificantly
related to Salary?To compa?d.Looking at the above correlations
- both significant or not - are there any surprises -by that I mean
any relationships you expected to be meaningful and are not and
vice-versa?e.Does this help us answer our equal pay for equal
work question?2Below is a regression analysis for salary being
predicted/explained by the other variables in our sample
(Midpoint, age, performance rating, service, gender, and degree
variables. (Note: since salary and compa are different ways of
expressing an employee’s salary, we do not want to have both
used in the same regression.)Plase interpret the findings.Ho:
The regression equation is not significant.Ha: The regression
equation is significant.Ho: The regression coefficient for each
variable is not significant Note: technically we have one for
each input variable.Ha: The regression coefficient for each
variable is significant Listing it this way to save
space.SalSUMMARY OUTPUTRegression StatisticsMultiple
R0.9915590747R Square0.9831893985Adjusted R
Square0.9808437332Standard
Error2.6575925726Observations50ANOVAdfSSMSFSignificanc
e
FRegression617762.29967387432960.383278979419.151611129
41.8121523852609E-
36Residual43303.70032612577.062798282Total4918066Coeffic
ientsStandard Errort StatP-valueLower 95%Upper 95%Lower
95.0%Upper 95.0%Intercept-1.74962121233.6183676583-
0.48353881570.6311664899-9.04675504275.547512618-
9.04675504275.547512618Midpoint1.21670105050.0319023509
38.13828811638.66416336978111E-
351.15236382831.28103827271.15236382831.2810382727Age-
0.00462801020.065197212-0.07098478760.9437389875-
0.13611071910.1268546987-
0.13611071910.1268546987Performace Rating-
0.05659644050.0344950678-1.64071109710.1081531819-
45. 0.12616237470.0129694936-
0.12616237470.0129694936Service-
0.04250035730.0843369821-0.50393500330.6168793519-
0.21258209120.1275813765-
0.21258209120.1275813765Gender2.4203372120.86084431762.
81158528040.00739661880.6842791924.1563952320.68427919
24.156395232Degree0.27553341430.79980230480.34450190090
.732148119-1.33742165471.8884884833-
1.33742165471.8884884833Note: since Gender and Degree are
expressed as 0 and 1, they are considered dummy variables and
can be used in a multiple regression equation.Interpretation:For
the Regression as a whole:What is the value of the F statistic:
What is the p-value associated with this value: Is the p-value
<0.05?Do you reject or not reject the null hypothesis: What
does this decision mean for our equal pay question: For each of
the coefficients:InterceptMidpointAgePerf.
Rat.ServiceGenderDegreeWhat is the coefficient's p-value for
each of the variables: Is the p-value < 0.05?Do you reject or not
reject each null hypothesis: What are the coefficients for the
significant variables?Using only the significant variables, what
is the equation?Salary =Is gender a significant factor in
salary:If so, who gets paid more with all other things being
equal?How do we know? 3Perform a regression analysis using
compa as the dependent variable and the same
independentvariables as used in question 2. Show the result,
and interpret your findings by answering the same
questions.Note: be sure to include the appropriate hypothesis
statements.Regression hypothesesHo:Ha:Coefficient hypotheses
(one to stand for all the separate variables)Ho:Ha:Put C94 in
output range boxInterpretation:For the Regression as a
whole:What is the value of the F statistic: What is the p-value
associated with this value: Is the p-value < 0.05?Do you reject
or not reject the null hypothesis: What does this decision mean
for our equal pay question: For each of the coefficients:
InterceptMidpointAgePerf. Rat.ServiceGenderDegreeWhat is
the coefficient's p-value for each of the variables: Is the p-value
46. < 0.05?Do you reject or not reject each null hypothesis: What
are the coefficients for the significant variables?Using only the
significant variables, what is the equation?Compa = Is gender a
significant factor in compa:If so, who gets paid more with all
other things being equal?How do we know? 4Based on all of
your results to date, do we have an answer to the question of are
males and females paid equally for equal work?If so, which
gender gets paid more? How do we know?Which is the best
variable to use in analyzing pay practices - salary or compa?
Why?What is most interesting or surprising about the results we
got doing the analysis during the last 5 weeks?5Why did the
single factor tests and analysis (such as t and single factor
ANOVA tests on salary equality) not provide a complete answer
to our salary equality question?What outcomes in your life or
work might benefit from a multiple regression examination
rather than a simpler one variable test?