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.
Case Study Hereditary AngioedemaAll responses must be in your .docxcowinhelen
Case Study: Hereditary Angioedema
All responses must be in your own words. Answers that have been copied and pasted will not receive credit.
1. Translate “angioedema”. [Note: I am not looking for a description of the disorder. Rather, I would like you to translate the medical term itself.]
2. The complement system is described as a ‘cascade system’. How does the system fit into this description of being a cascade? [Suggestion: Google the definition of cascade, then think about the complement system in light of the definition]
3. Is complement involved in the innate, or the adaptive immune system, or both? Please explain you answer.
4. What role does C1INH play in the complement system? Why is it so important?
5. What was the physiologic cause of Richard’s abdominal pain?
6. How can one distinguish the swelling of HAE from the swelling of allergic angioedema?
7. What is bradykinin’s role in HA?
8. Do you think Richard’s infancy colic was related to his HA? No need to research this. Just use your intuition. Explain your thinking.
9. What is typically used to treat attacks of HAE?
10. Swelling in the extremities is not dangerous. What other areas of the body are subject to swelling? What is the most dangerous location for swelling to occur and why is it the most dangerous?
2018
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.
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.
BUS308 – Week 5 Lecture 1 A Different View Expected Ou.docxcurwenmichaela
BUS308 – Week 5 Lecture 1
A Different View
Expected Outcomes
After reading this lecture, the student should be familiar with:
1. What a confidence interval for a statistic is.
2. What a confidence interval for differences is.
3. The difference between statistical and practical significance.
4. The meaning of an Effect Size measure.
Overview
Years ago, a comedy show used to introduce new skits with the phrase “and now for
something completely different.” That seems appropriate for this week’s material.
This week we will look at evaluating our data results in somewhat different ways. One of
the criticisms of the hypothesis testing procedure is that it only shows one value, when it is
reasonably clear that a number of different values would also cause us to reject or not reject a
null hypothesis of no difference. Many managers and researchers would like to see what these
values could be; and, in particular, what are the extreme values as help in making decisions.
Confidence intervals will help us here.
The other criticism of the hypothesis testing procedure is that we can “manage” the
results, or ensure that we will reject the null, by manipulating the sample size. For example, if
we have a difference in a customer preference between two products of only 1%, is this a big
deal? Given the uncertainty contained in sample results, we might tend to think that we can
safely ignore this result. However, if we were to use a sample of, say, 10,000, we would find
that this difference is statistically significant. This, for many, seems to fly in the face of
reasonableness. We will look at a measure of “practical significance,” meaning the likelihood of
the difference being worth paying any attention to, called the effect size to help us here.
Confidence Intervals
A confidence interval is a range of values that, based upon the sample results, most likely
contains the actual population parameter. The “most likely” element is the level of confidence
attached to the interval, 95% confidence interval, 90% confidence interval, 99% confidence
interval, etc. They can be created at any time, with or without performing a statistical test, such
as the t-test.
A confidence interval may be expressed as a range (45 to 51% of the town’s population
support the proposal) or as a mean or proportion with a margin of error (48% of the town
supports the proposal, with a margin of error of 3%). This last format is frequently seen with
opinion poll results, and simply means that you should add and subtract this margin of error from
the reported proportion to obtain the range. With either format, the confidence percent should
also be provided.
Confidence intervals for a single mean (or proportion) are fairly straightforward to
understand, and relate to t-test outcomes simply. Details on how to construct the interval will be
given in this week’s second lecture. We want to understand how to interpret and understa.
7 HYPOTHETICALS AND YOU TESTING YOUR QUESTIONS7 MEDIA LIBRARY.docxtaishao1
7 HYPOTHETICALS AND YOU TESTING YOUR QUESTIONS
7: MEDIA LIBRARY
Premium Videos
Core Concepts in Stats Video
· Probability and Hypothesis Testing
Lightboard Lecture Video
· Hypothesis Testing
Difficulty Scale
(don’t plan on going out tonight)
WHAT YOU WILL LEARN IN THIS CHAPTER
· Understanding the difference between a sample and a population
· Understanding the importance of the null and research hypotheses
· Using criteria to judge a good hypothesis
SO YOU WANT TO BE A SCIENTIST
You might have heard the term hypothesis used in other classes. You may even have had to formulate one for a research project you did for another class, or you may have read one or two in a journal article. If so, then you probably have a good idea what a hypothesis is. For those of you who are unfamiliar with this often-used term, a hypothesis is basically “an educated guess.” Its most important role is to reflect the general problem statement or question that was the motivation for asking the research question in the first place.
That’s why taking the care and time to formulate a really precise and clear research question is so important. This research question will guide your creation of a hypothesis, and in turn, the hypothesis will determine the techniques you will use to test it and answer the question that was originally asked.
So, a good hypothesis translates a problem statement or a research question into a format that makes it easier to examine. This format is called a hypothesis. We will talk about what makes a hypothesis a good one later in this chapter. Before that, let’s turn our attention to the difference between a sample and a population. This is an important distinction, because while hypotheses usually describe a population, hypothesis testing deals with a sample and then the results are generalized to the larger population. We also address the two main types of hypotheses (the null hypothesis and the research hypothesis). But first, let’s formally define some simple terms that we have used earlier in Statistics for People Who (Think They) Hate Statistics.
SAMPLES AND POPULATIONS
As a good scientist, you would like to be able to say that if Method A is better than Method B in your study, this is true forever and always and for all people in the universe, right? Indeed. And, if you do enough research on the relative merits of Methods A and B and test enough people, you may someday be able to say that.
But don’t get too excited, because it’s unlikely you will ever be able to speak with such confidence. It takes too much money ($$$) and too much time (all those people!) to do all that research, and besides, it’s not even necessary. Instead, you can just select a representative sample from the population and test your hypothesis about the relative merits of Methods A and B on that sample.
Given the constraints of never enough time and never enough research funds, with which almost all scientists live, the next best strategy is to take a portion of a lar.
7 HYPOTHETICALS AND YOU TESTING YOUR QUESTIONS7 MEDIA LIBRARY.docxevonnehoggarth79783
7 HYPOTHETICALS AND YOU TESTING YOUR QUESTIONS
7: MEDIA LIBRARY
Premium Videos
Core Concepts in Stats Video
· Probability and Hypothesis Testing
Lightboard Lecture Video
· Hypothesis Testing
Difficulty Scale
(don’t plan on going out tonight)
WHAT YOU WILL LEARN IN THIS CHAPTER
· Understanding the difference between a sample and a population
· Understanding the importance of the null and research hypotheses
· Using criteria to judge a good hypothesis
SO YOU WANT TO BE A SCIENTIST
You might have heard the term hypothesis used in other classes. You may even have had to formulate one for a research project you did for another class, or you may have read one or two in a journal article. If so, then you probably have a good idea what a hypothesis is. For those of you who are unfamiliar with this often-used term, a hypothesis is basically “an educated guess.” Its most important role is to reflect the general problem statement or question that was the motivation for asking the research question in the first place.
That’s why taking the care and time to formulate a really precise and clear research question is so important. This research question will guide your creation of a hypothesis, and in turn, the hypothesis will determine the techniques you will use to test it and answer the question that was originally asked.
So, a good hypothesis translates a problem statement or a research question into a format that makes it easier to examine. This format is called a hypothesis. We will talk about what makes a hypothesis a good one later in this chapter. Before that, let’s turn our attention to the difference between a sample and a population. This is an important distinction, because while hypotheses usually describe a population, hypothesis testing deals with a sample and then the results are generalized to the larger population. We also address the two main types of hypotheses (the null hypothesis and the research hypothesis). But first, let’s formally define some simple terms that we have used earlier in Statistics for People Who (Think They) Hate Statistics.
SAMPLES AND POPULATIONS
As a good scientist, you would like to be able to say that if Method A is better than Method B in your study, this is true forever and always and for all people in the universe, right? Indeed. And, if you do enough research on the relative merits of Methods A and B and test enough people, you may someday be able to say that.
But don’t get too excited, because it’s unlikely you will ever be able to speak with such confidence. It takes too much money ($$$) and too much time (all those people!) to do all that research, and besides, it’s not even necessary. Instead, you can just select a representative sample from the population and test your hypothesis about the relative merits of Methods A and B on that sample.
Given the constraints of never enough time and never enough research funds, with which almost all scientists live, the next best strategy is to take a portion of a lar.
Community Teaching Plan Teaching Experience Paper 1Unsatisf.docxdonnajames55
Community Teaching Plan: Teaching Experience Paper
1
Unsatisfactory
0.00%
2
Less than Satisfactory
75.00%
3
Satisfactory
83.00%
4
Good
94.00%
5
Excellent
100.00%
80.0 %Content
30.0 %Comprehensive Summary of Teaching Plan With Epidemiological Rationale for Topic
Summary of community teaching plan is not identified or missing.
Summary of community teaching plan is incomplete.
Summary of community teaching plan is offered but some elements are vague.
Focus of community teaching is clear with a detailed summary of each component. Rationale is not provided.
Focus of community teaching is clear, consistent with Functional Health Patterns (FHP) assessment findings and supported by explanation of epidemiological rationale.
50.0 %Evaluation of Teaching Experience With Discussion of Community Response to Teaching Provided. Areas of Strength and Areas of Improvement Described
Evaluation of teaching experience is omitted or incomplete.
Evaluation of teaching experience is unclear and/or discussion of community response to teaching is missing.
Evaluation of teaching experience is provided with a brief discussion of community response to teaching.
A detailed evaluation of teaching experience with discussion of community response to teaching and areas of strength/improvement is provided.
Comprehensive evaluation of teaching experience with discussion of community response provided along with a detailed description of barriers and strategies to overcome barriers is provided.
15.0 %Organization and Effectiveness
5.0 %Thesis Development and Purpose
Paper lacks any discernible overall purpose or organizing claim.
Thesis is insufficiently developed and/or vague; purpose is not clear.
Thesis is apparent and appropriate to purpose.
Thesis is clear and forecasts the development of the paper. It is descriptive and reflective of the arguments and appropriate to the purpose.
Thesis is comprehensive; contained within the thesis is the essence of the paper. Thesis statement makes the purpose of the paper clear.
5.0 %Paragraph Development and Transitions
Paragraphs and transitions consistently lack unity and coherence. No apparent connections between paragraphs are established. Transitions are inappropriate to purpose and scope. Organization is disjointed.
Some paragraphs and transitions may lack logical progression of ideas, unity, coherence, and/or cohesiveness. Some degree of organization is evident.
Paragraphs are generally competent, but ideas may show some inconsistency in organization and/or in their relationships to each other.
A logical progression of ideas between paragraphs is apparent. Paragraphs exhibit a unity, coherence, and cohesiveness. Topic sentences and concluding remarks are appropriate to purpose.
There is a sophisticated construction of paragraphs and transitions. Ideas progress and relate to each other. Paragraph and transition construction guide the reader. Paragraph structure is seamless.
5.0 %Mechanics of Writing (includes spelling.
Case Study Hereditary AngioedemaAll responses must be in your .docxcowinhelen
Case Study: Hereditary Angioedema
All responses must be in your own words. Answers that have been copied and pasted will not receive credit.
1. Translate “angioedema”. [Note: I am not looking for a description of the disorder. Rather, I would like you to translate the medical term itself.]
2. The complement system is described as a ‘cascade system’. How does the system fit into this description of being a cascade? [Suggestion: Google the definition of cascade, then think about the complement system in light of the definition]
3. Is complement involved in the innate, or the adaptive immune system, or both? Please explain you answer.
4. What role does C1INH play in the complement system? Why is it so important?
5. What was the physiologic cause of Richard’s abdominal pain?
6. How can one distinguish the swelling of HAE from the swelling of allergic angioedema?
7. What is bradykinin’s role in HA?
8. Do you think Richard’s infancy colic was related to his HA? No need to research this. Just use your intuition. Explain your thinking.
9. What is typically used to treat attacks of HAE?
10. Swelling in the extremities is not dangerous. What other areas of the body are subject to swelling? What is the most dangerous location for swelling to occur and why is it the most dangerous?
2018
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.
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.
BUS308 – Week 5 Lecture 1 A Different View Expected Ou.docxcurwenmichaela
BUS308 – Week 5 Lecture 1
A Different View
Expected Outcomes
After reading this lecture, the student should be familiar with:
1. What a confidence interval for a statistic is.
2. What a confidence interval for differences is.
3. The difference between statistical and practical significance.
4. The meaning of an Effect Size measure.
Overview
Years ago, a comedy show used to introduce new skits with the phrase “and now for
something completely different.” That seems appropriate for this week’s material.
This week we will look at evaluating our data results in somewhat different ways. One of
the criticisms of the hypothesis testing procedure is that it only shows one value, when it is
reasonably clear that a number of different values would also cause us to reject or not reject a
null hypothesis of no difference. Many managers and researchers would like to see what these
values could be; and, in particular, what are the extreme values as help in making decisions.
Confidence intervals will help us here.
The other criticism of the hypothesis testing procedure is that we can “manage” the
results, or ensure that we will reject the null, by manipulating the sample size. For example, if
we have a difference in a customer preference between two products of only 1%, is this a big
deal? Given the uncertainty contained in sample results, we might tend to think that we can
safely ignore this result. However, if we were to use a sample of, say, 10,000, we would find
that this difference is statistically significant. This, for many, seems to fly in the face of
reasonableness. We will look at a measure of “practical significance,” meaning the likelihood of
the difference being worth paying any attention to, called the effect size to help us here.
Confidence Intervals
A confidence interval is a range of values that, based upon the sample results, most likely
contains the actual population parameter. The “most likely” element is the level of confidence
attached to the interval, 95% confidence interval, 90% confidence interval, 99% confidence
interval, etc. They can be created at any time, with or without performing a statistical test, such
as the t-test.
A confidence interval may be expressed as a range (45 to 51% of the town’s population
support the proposal) or as a mean or proportion with a margin of error (48% of the town
supports the proposal, with a margin of error of 3%). This last format is frequently seen with
opinion poll results, and simply means that you should add and subtract this margin of error from
the reported proportion to obtain the range. With either format, the confidence percent should
also be provided.
Confidence intervals for a single mean (or proportion) are fairly straightforward to
understand, and relate to t-test outcomes simply. Details on how to construct the interval will be
given in this week’s second lecture. We want to understand how to interpret and understa.
7 HYPOTHETICALS AND YOU TESTING YOUR QUESTIONS7 MEDIA LIBRARY.docxtaishao1
7 HYPOTHETICALS AND YOU TESTING YOUR QUESTIONS
7: MEDIA LIBRARY
Premium Videos
Core Concepts in Stats Video
· Probability and Hypothesis Testing
Lightboard Lecture Video
· Hypothesis Testing
Difficulty Scale
(don’t plan on going out tonight)
WHAT YOU WILL LEARN IN THIS CHAPTER
· Understanding the difference between a sample and a population
· Understanding the importance of the null and research hypotheses
· Using criteria to judge a good hypothesis
SO YOU WANT TO BE A SCIENTIST
You might have heard the term hypothesis used in other classes. You may even have had to formulate one for a research project you did for another class, or you may have read one or two in a journal article. If so, then you probably have a good idea what a hypothesis is. For those of you who are unfamiliar with this often-used term, a hypothesis is basically “an educated guess.” Its most important role is to reflect the general problem statement or question that was the motivation for asking the research question in the first place.
That’s why taking the care and time to formulate a really precise and clear research question is so important. This research question will guide your creation of a hypothesis, and in turn, the hypothesis will determine the techniques you will use to test it and answer the question that was originally asked.
So, a good hypothesis translates a problem statement or a research question into a format that makes it easier to examine. This format is called a hypothesis. We will talk about what makes a hypothesis a good one later in this chapter. Before that, let’s turn our attention to the difference between a sample and a population. This is an important distinction, because while hypotheses usually describe a population, hypothesis testing deals with a sample and then the results are generalized to the larger population. We also address the two main types of hypotheses (the null hypothesis and the research hypothesis). But first, let’s formally define some simple terms that we have used earlier in Statistics for People Who (Think They) Hate Statistics.
SAMPLES AND POPULATIONS
As a good scientist, you would like to be able to say that if Method A is better than Method B in your study, this is true forever and always and for all people in the universe, right? Indeed. And, if you do enough research on the relative merits of Methods A and B and test enough people, you may someday be able to say that.
But don’t get too excited, because it’s unlikely you will ever be able to speak with such confidence. It takes too much money ($$$) and too much time (all those people!) to do all that research, and besides, it’s not even necessary. Instead, you can just select a representative sample from the population and test your hypothesis about the relative merits of Methods A and B on that sample.
Given the constraints of never enough time and never enough research funds, with which almost all scientists live, the next best strategy is to take a portion of a lar.
7 HYPOTHETICALS AND YOU TESTING YOUR QUESTIONS7 MEDIA LIBRARY.docxevonnehoggarth79783
7 HYPOTHETICALS AND YOU TESTING YOUR QUESTIONS
7: MEDIA LIBRARY
Premium Videos
Core Concepts in Stats Video
· Probability and Hypothesis Testing
Lightboard Lecture Video
· Hypothesis Testing
Difficulty Scale
(don’t plan on going out tonight)
WHAT YOU WILL LEARN IN THIS CHAPTER
· Understanding the difference between a sample and a population
· Understanding the importance of the null and research hypotheses
· Using criteria to judge a good hypothesis
SO YOU WANT TO BE A SCIENTIST
You might have heard the term hypothesis used in other classes. You may even have had to formulate one for a research project you did for another class, or you may have read one or two in a journal article. If so, then you probably have a good idea what a hypothesis is. For those of you who are unfamiliar with this often-used term, a hypothesis is basically “an educated guess.” Its most important role is to reflect the general problem statement or question that was the motivation for asking the research question in the first place.
That’s why taking the care and time to formulate a really precise and clear research question is so important. This research question will guide your creation of a hypothesis, and in turn, the hypothesis will determine the techniques you will use to test it and answer the question that was originally asked.
So, a good hypothesis translates a problem statement or a research question into a format that makes it easier to examine. This format is called a hypothesis. We will talk about what makes a hypothesis a good one later in this chapter. Before that, let’s turn our attention to the difference between a sample and a population. This is an important distinction, because while hypotheses usually describe a population, hypothesis testing deals with a sample and then the results are generalized to the larger population. We also address the two main types of hypotheses (the null hypothesis and the research hypothesis). But first, let’s formally define some simple terms that we have used earlier in Statistics for People Who (Think They) Hate Statistics.
SAMPLES AND POPULATIONS
As a good scientist, you would like to be able to say that if Method A is better than Method B in your study, this is true forever and always and for all people in the universe, right? Indeed. And, if you do enough research on the relative merits of Methods A and B and test enough people, you may someday be able to say that.
But don’t get too excited, because it’s unlikely you will ever be able to speak with such confidence. It takes too much money ($$$) and too much time (all those people!) to do all that research, and besides, it’s not even necessary. Instead, you can just select a representative sample from the population and test your hypothesis about the relative merits of Methods A and B on that sample.
Given the constraints of never enough time and never enough research funds, with which almost all scientists live, the next best strategy is to take a portion of a lar.
Community Teaching Plan Teaching Experience Paper 1Unsatisf.docxdonnajames55
Community Teaching Plan: Teaching Experience Paper
1
Unsatisfactory
0.00%
2
Less than Satisfactory
75.00%
3
Satisfactory
83.00%
4
Good
94.00%
5
Excellent
100.00%
80.0 %Content
30.0 %Comprehensive Summary of Teaching Plan With Epidemiological Rationale for Topic
Summary of community teaching plan is not identified or missing.
Summary of community teaching plan is incomplete.
Summary of community teaching plan is offered but some elements are vague.
Focus of community teaching is clear with a detailed summary of each component. Rationale is not provided.
Focus of community teaching is clear, consistent with Functional Health Patterns (FHP) assessment findings and supported by explanation of epidemiological rationale.
50.0 %Evaluation of Teaching Experience With Discussion of Community Response to Teaching Provided. Areas of Strength and Areas of Improvement Described
Evaluation of teaching experience is omitted or incomplete.
Evaluation of teaching experience is unclear and/or discussion of community response to teaching is missing.
Evaluation of teaching experience is provided with a brief discussion of community response to teaching.
A detailed evaluation of teaching experience with discussion of community response to teaching and areas of strength/improvement is provided.
Comprehensive evaluation of teaching experience with discussion of community response provided along with a detailed description of barriers and strategies to overcome barriers is provided.
15.0 %Organization and Effectiveness
5.0 %Thesis Development and Purpose
Paper lacks any discernible overall purpose or organizing claim.
Thesis is insufficiently developed and/or vague; purpose is not clear.
Thesis is apparent and appropriate to purpose.
Thesis is clear and forecasts the development of the paper. It is descriptive and reflective of the arguments and appropriate to the purpose.
Thesis is comprehensive; contained within the thesis is the essence of the paper. Thesis statement makes the purpose of the paper clear.
5.0 %Paragraph Development and Transitions
Paragraphs and transitions consistently lack unity and coherence. No apparent connections between paragraphs are established. Transitions are inappropriate to purpose and scope. Organization is disjointed.
Some paragraphs and transitions may lack logical progression of ideas, unity, coherence, and/or cohesiveness. Some degree of organization is evident.
Paragraphs are generally competent, but ideas may show some inconsistency in organization and/or in their relationships to each other.
A logical progression of ideas between paragraphs is apparent. Paragraphs exhibit a unity, coherence, and cohesiveness. Topic sentences and concluding remarks are appropriate to purpose.
There is a sophisticated construction of paragraphs and transitions. Ideas progress and relate to each other. Paragraph and transition construction guide the reader. Paragraph structure is seamless.
5.0 %Mechanics of Writing (includes spelling.
BUS308 – Week 1 Lecture 1
Statistics
Expected Outcomes
After reading this lecture, the student should be familiar with:
1. The basic ideas of data analysis.
2. Key statistical concepts and terms.
3. The basic approach for this class.
4. The case focus for the class.
What we are all about
Data, measurements, counts, etc., is often considered the language of business. However,
it also plays an important role in our personal lives as well. Data, or more accurately, the
analysis of data answers our questions. These may be business related or personal. Some
questions we may have heard that require data to answer include:
1. On average, how long does it take you to get to work? Or, alternately, when do you
have to leave to get to work on time?
2. For budget purposes, what is the average expense for utilities, food, etc.?
3. Has the quality rejection rate on production Line 3 changed?
4. Did the new attendance incentive program reduce the tardiness for the department?
5. Which vendor has the best average price for what we order?
6. Which customers have the most complaints about our products?
7. Has the average production time decreased with the new process?
8. Do different groups respond differently to an employee questionnaire?
9. What are the chances that a customer will complain about or return a product?
Note that all of these very reasonable questions require that we collect data, analyze it,
and reach some conclusion based upon that result.
Making Sense of Data
This class is about ways to turn data sets, lots of raw numbers, into information that we
can use. This may include simple descriptions of the data with measures such as average, range,
high and low values, etc. It also includes ways to examine the information within the data set so
that we can make decisions, identify patterns, and identify existing relationships. This is often
called data analysis; some courses discuss this approach with the term “data-based decision
making.” During this class we will focus on the logic of analyzing data and interpreting these
results.
What this class is not
This class is not a mathematics course. I know, it is called statistics and it deals with
numbers, but we do not focus on creating formulas or even doing calculations. Excel will do all
of the calculations for us; for those of you who have not used Excel before, and even for some
who have, you will be pleasantly surprised at how powerful and relatively easy to use it is.
It is also not a class in collecting the data. Courses in research focus on how to plan on
collecting data so that it is fair and unbiased. Statistics deals with working on the data after it has
been collected.
Class structure
There are two main themes to this class. The first focuses on interpreting statistical
outcomes. When someone says, the result is statistically significant with a p-value of 0.01; we
need, as professionals, to know what it means. .
BUS 308 Week 3 Lecture 1 Examining Differences - Continued.docxcurwenmichaela
BUS 308 Week 3 Lecture 1
Examining Differences - Continued
Expected Outcomes
After reading this lecture, the student should be familiar with:
1. Issues around multiple testing
2. The basics of the Analysis of Variance test
3. Determining significant differences between group means
4. The basics of the Chi Square Distribution.
Overview
Last week, we found out ways to examine differences between a measure taken on two
groups (two-sample test situation) as well as comparing that measure to a standard (a one-sample
test situation). We looked at the F test which let us test for variance equality. We also looked at
the t-test which focused on testing for mean equality. We noted that the t-test had three distinct
versions, one for groups that had equal variances, one for groups that had unequal variances, and
one for data that was paired (two measures on the same subject, such as salary and midpoint for
each employee). We also looked at how the 2-sample unequal t-test could be used to use Excel
to perform a one-sample mean test against a standard or constant value. This week we expand
our tool kit to let us compare multiple groups for similar mean values.
A second tool will let us look at how data values are distributed – if graphed, would they
look the same? Different shapes or patterns often means the data sets differ in significant ways
that can help explain results.
Multiple Groups
As interesting as comparing two groups is, often it is a bit limiting as to what it tells us.
One obvious issue that we are missing in the comparisons made last week was equal work. This
idea is still somewhat hard to get a clear handle on. Typically, as we look at this issue, questions
arise about things such as performance appraisal ratings, education distribution, seniority impact,
etc.
Some of these can be tested with the tools introduced last week. We can see, for
example, if the performance rating average is the same for each gender. What we couldn’t do, at
this point however, is see if performance ratings differ by grade, do the more senior workers
perform relatively better? Is there a difference between ratings for each gender by grade level?
The same questions can be asked about seniority impact. This week will give us tools to expand
how we look at the clues hidden within the data set about equal pay for equal work.
ANOVA
So, let’s start taking a look at these questions. The first tool for this week is the Analysis
of Variance – ANOVA for short. ANOVA is often confusing for students; it says it analyzes
variance (which it does) but the purpose of an ANOVA test is to determine if the means of
different groups are the same! Now, so far, we have considered means and variance to be two
distinct characteristics of data sets; characteristics that are not related, yet here we are saying that
looking at one will give us insight into the other.
The reason is due to the way the variance is an.
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., ...
Movie Case Study
Cbt Case Studies
Case Management Essay
Approach to Case Study
Case Formulation
Ethics Case Study Essay
Gastritis Case Study
Case Study Essay examples
Case Study Talisha
Case Study: Anxiety Essay
Case Studies
Lesson 2 Statistics Benefits, Risks, and MeasurementsAssignmen.docxSHIVA101531
Lesson 2: Statistics: Benefits, Risks, and Measurements
Assignments
· See your Course Syllabus for the reading assignments.
· Work through the Lesson 2 online notes that follow.
· Complete the Practice Questions and Lesson 2 Assignment.
Learning Objectives
Chapters 1 and 3
After successfully completing this lesson, you should be able to:
· Identify the three conditions needed to conduct a proper study.
· Apply the seven pitfalls that can be encountered when asking questions in a survey.
· Distinguish between measurement variables and categorical variables.
· Distinguish between continuous variables and discrete variables for those that are measurement variables.
· Distinguish between validity, reliability, and bias.
Terms to Know
From Chapter 1
· statistics
· population
· sample
· observational study
· experiment
· selection bias
· nonresponse bias
From Chapter 3
· data (variable)
· categorical variables
· measurement variables
· measurement (discrete) variables
· measurement (continuous) variables
· validity
· reliability
· bias
2.1 What is Statistics?
Section 2.1. Chapter 1
Overview
What is statistics? If you think statistics is just another math course with many formulas and lifeless numbers, you are not alone. However, this is a myth that hopefully will be debunked as you work through this course. Statistics is about data. More precisely, statistics is a collection of procedures and principles for gaining and processing information from collected data. Knowing these principles and procedures will help you make intelligent decisions in everyday life when faced with uncertainty. The following examples are meant to illuminate the definition of statistics.
Example 2.1. Angry Women
Who are those angry women? (Streitfield, D., 1988 and Wallis, 1987.) In 1987, Shere Hite published a best-selling book called Women and Love: A Cultural Revolution in Progress. This 7-year research project produced a controversial 922-page publication that summarized the results from a survey that was designed to examine how American women feel about their relationships with men. Hite mailed out 100,000 fifteen-page questionnaires to women who were members of a wide variety of organizations across the U.S. These organizations included church, political, volunteer, senior citizen, and counseling groups, among many others. Questionnaires were actually sent to the leader of each organization. The leader was asked to distribute questionnaires to all members. Each questionnaire contained 127 open-ended questions with many parts and follow-ups. Part of Hite’s directions read as follows: “Feel free to skip around and answer only those questions you choose.” Approximately 4500 questionnaires were returned. Below are a few statements from this 1987 publication.
· 84% of women are not emotionally satisfied with their relationships
· 95% of women reported emotional and psychological harassment from their partners
· 70% of women married 5 years or more are having extramarital ...
In this presentation, I share some ideas on how as a communication major you can develop the mindset of an analyst. I share insights gained from five personal career milestones
BUS310ASSIGNMENTImagine that you work for a company with an ag.docxcurwenmichaela
BUS310ASSIGNMENT
Imagine that you work for a company with an age diverse workforce. You have baby boomers working with millenials. Their backgrounds are different, and how they view work is different. This is causing some friction within the workforce. Before the tension escalates, you need to have a meeting to discuss the issue. Prepare a five to seven (5-7) slide PowerPoint presentation for your staff meeting that addresses this issue and proposes a solution.
Create a five to seven (5-7) slide PowerPoint presentation in which you:
1. Propose a solution that will relieve friction in your company’s age diverse workforce.
2. Format your assignment according to the following formatting requirements:
a. Format the PowerPoint presentation with headings on each slide and at least one (1) relevant graphic (photograph, graph, clip art, etc.). Ensure that the presentation is visually appealing and readable from up to 18 feet away. Check with your professor for any additional instructions.
b. Include a title slide containing the title of the assignment, your name, your professor’s name, the course title, and the date.
The specific course learning outcomes associated with this assignment are:
· Explain effective approaches to the broad spectrum of employee relations, including career development, fostering ethical behavior, discipline, labor relations, and dismissals.
· Use technology and information resources to research issues in human resource management.
· Write clearly and concisely about human resource management using proper writing mechanics.
Click here to view the grading rubric for this assignment.
Team Project Deliverable and Presentation
You team works for XYZ Company, which has a directional strategy focused on expanding the company through horizontal integration. Your team can determine the official name of the company and industry. The company does a great job keeping close watch on its cash position and consistently maintains a positive cash flow; is very solvent; controls its overhead expenses; has solid marketing and sales, production, and human resources performance metrics, and fosters a culture of strategic thinkers. Historically, your company has expanded through a combination of organic (new startups) and inorganic growth and feels it’s time to consider acquisition opportunities.
The Board is looking to engage in a friendly acquisition of a company that will not only increase its market share, but allow it to penetrate new markets and increase the company’s abilities to meet current and future consumer needs and expectations. Since management’s attitude is to pursue a friendly acquisition as opposed to a hostile takeover, your team may consider looking at conglomerates that have experienced significant growth through inorganic growth (acquisitions) and may now be looking to refocus on their core business and are willing to consider divesting some of its businesses that are within your industry. There could be other companies.
BUS308 – Week 1 Lecture 1
Statistics
Expected Outcomes
After reading this lecture, the student should be familiar with:
1. The basic ideas of data analysis.
2. Key statistical concepts and terms.
3. The basic approach for this class.
4. The case focus for the class.
What we are all about
Data, measurements, counts, etc., is often considered the language of business. However,
it also plays an important role in our personal lives as well. Data, or more accurately, the
analysis of data answers our questions. These may be business related or personal. Some
questions we may have heard that require data to answer include:
1. On average, how long does it take you to get to work? Or, alternately, when do you
have to leave to get to work on time?
2. For budget purposes, what is the average expense for utilities, food, etc.?
3. Has the quality rejection rate on production Line 3 changed?
4. Did the new attendance incentive program reduce the tardiness for the department?
5. Which vendor has the best average price for what we order?
6. Which customers have the most complaints about our products?
7. Has the average production time decreased with the new process?
8. Do different groups respond differently to an employee questionnaire?
9. What are the chances that a customer will complain about or return a product?
Note that all of these very reasonable questions require that we collect data, analyze it,
and reach some conclusion based upon that result.
Making Sense of Data
This class is about ways to turn data sets, lots of raw numbers, into information that we
can use. This may include simple descriptions of the data with measures such as average, range,
high and low values, etc. It also includes ways to examine the information within the data set so
that we can make decisions, identify patterns, and identify existing relationships. This is often
called data analysis; some courses discuss this approach with the term “data-based decision
making.” During this class we will focus on the logic of analyzing data and interpreting these
results.
What this class is not
This class is not a mathematics course. I know, it is called statistics and it deals with
numbers, but we do not focus on creating formulas or even doing calculations. Excel will do all
of the calculations for us; for those of you who have not used Excel before, and even for some
who have, you will be pleasantly surprised at how powerful and relatively easy to use it is.
It is also not a class in collecting the data. Courses in research focus on how to plan on
collecting data so that it is fair and unbiased. Statistics deals with working on the data after it has
been collected.
Class structure
There are two main themes to this class. The first focuses on interpreting statistical
outcomes. When someone says, the result is statistically significant with a p-value of 0.01; we
need, as professionals, to know what it means. .
BUS 308 Week 3 Lecture 1 Examining Differences - Continued.docxcurwenmichaela
BUS 308 Week 3 Lecture 1
Examining Differences - Continued
Expected Outcomes
After reading this lecture, the student should be familiar with:
1. Issues around multiple testing
2. The basics of the Analysis of Variance test
3. Determining significant differences between group means
4. The basics of the Chi Square Distribution.
Overview
Last week, we found out ways to examine differences between a measure taken on two
groups (two-sample test situation) as well as comparing that measure to a standard (a one-sample
test situation). We looked at the F test which let us test for variance equality. We also looked at
the t-test which focused on testing for mean equality. We noted that the t-test had three distinct
versions, one for groups that had equal variances, one for groups that had unequal variances, and
one for data that was paired (two measures on the same subject, such as salary and midpoint for
each employee). We also looked at how the 2-sample unequal t-test could be used to use Excel
to perform a one-sample mean test against a standard or constant value. This week we expand
our tool kit to let us compare multiple groups for similar mean values.
A second tool will let us look at how data values are distributed – if graphed, would they
look the same? Different shapes or patterns often means the data sets differ in significant ways
that can help explain results.
Multiple Groups
As interesting as comparing two groups is, often it is a bit limiting as to what it tells us.
One obvious issue that we are missing in the comparisons made last week was equal work. This
idea is still somewhat hard to get a clear handle on. Typically, as we look at this issue, questions
arise about things such as performance appraisal ratings, education distribution, seniority impact,
etc.
Some of these can be tested with the tools introduced last week. We can see, for
example, if the performance rating average is the same for each gender. What we couldn’t do, at
this point however, is see if performance ratings differ by grade, do the more senior workers
perform relatively better? Is there a difference between ratings for each gender by grade level?
The same questions can be asked about seniority impact. This week will give us tools to expand
how we look at the clues hidden within the data set about equal pay for equal work.
ANOVA
So, let’s start taking a look at these questions. The first tool for this week is the Analysis
of Variance – ANOVA for short. ANOVA is often confusing for students; it says it analyzes
variance (which it does) but the purpose of an ANOVA test is to determine if the means of
different groups are the same! Now, so far, we have considered means and variance to be two
distinct characteristics of data sets; characteristics that are not related, yet here we are saying that
looking at one will give us insight into the other.
The reason is due to the way the variance is an.
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., ...
Movie Case Study
Cbt Case Studies
Case Management Essay
Approach to Case Study
Case Formulation
Ethics Case Study Essay
Gastritis Case Study
Case Study Essay examples
Case Study Talisha
Case Study: Anxiety Essay
Case Studies
Lesson 2 Statistics Benefits, Risks, and MeasurementsAssignmen.docxSHIVA101531
Lesson 2: Statistics: Benefits, Risks, and Measurements
Assignments
· See your Course Syllabus for the reading assignments.
· Work through the Lesson 2 online notes that follow.
· Complete the Practice Questions and Lesson 2 Assignment.
Learning Objectives
Chapters 1 and 3
After successfully completing this lesson, you should be able to:
· Identify the three conditions needed to conduct a proper study.
· Apply the seven pitfalls that can be encountered when asking questions in a survey.
· Distinguish between measurement variables and categorical variables.
· Distinguish between continuous variables and discrete variables for those that are measurement variables.
· Distinguish between validity, reliability, and bias.
Terms to Know
From Chapter 1
· statistics
· population
· sample
· observational study
· experiment
· selection bias
· nonresponse bias
From Chapter 3
· data (variable)
· categorical variables
· measurement variables
· measurement (discrete) variables
· measurement (continuous) variables
· validity
· reliability
· bias
2.1 What is Statistics?
Section 2.1. Chapter 1
Overview
What is statistics? If you think statistics is just another math course with many formulas and lifeless numbers, you are not alone. However, this is a myth that hopefully will be debunked as you work through this course. Statistics is about data. More precisely, statistics is a collection of procedures and principles for gaining and processing information from collected data. Knowing these principles and procedures will help you make intelligent decisions in everyday life when faced with uncertainty. The following examples are meant to illuminate the definition of statistics.
Example 2.1. Angry Women
Who are those angry women? (Streitfield, D., 1988 and Wallis, 1987.) In 1987, Shere Hite published a best-selling book called Women and Love: A Cultural Revolution in Progress. This 7-year research project produced a controversial 922-page publication that summarized the results from a survey that was designed to examine how American women feel about their relationships with men. Hite mailed out 100,000 fifteen-page questionnaires to women who were members of a wide variety of organizations across the U.S. These organizations included church, political, volunteer, senior citizen, and counseling groups, among many others. Questionnaires were actually sent to the leader of each organization. The leader was asked to distribute questionnaires to all members. Each questionnaire contained 127 open-ended questions with many parts and follow-ups. Part of Hite’s directions read as follows: “Feel free to skip around and answer only those questions you choose.” Approximately 4500 questionnaires were returned. Below are a few statements from this 1987 publication.
· 84% of women are not emotionally satisfied with their relationships
· 95% of women reported emotional and psychological harassment from their partners
· 70% of women married 5 years or more are having extramarital ...
In this presentation, I share some ideas on how as a communication major you can develop the mindset of an analyst. I share insights gained from five personal career milestones
BUS310ASSIGNMENTImagine that you work for a company with an ag.docxcurwenmichaela
BUS310ASSIGNMENT
Imagine that you work for a company with an age diverse workforce. You have baby boomers working with millenials. Their backgrounds are different, and how they view work is different. This is causing some friction within the workforce. Before the tension escalates, you need to have a meeting to discuss the issue. Prepare a five to seven (5-7) slide PowerPoint presentation for your staff meeting that addresses this issue and proposes a solution.
Create a five to seven (5-7) slide PowerPoint presentation in which you:
1. Propose a solution that will relieve friction in your company’s age diverse workforce.
2. Format your assignment according to the following formatting requirements:
a. Format the PowerPoint presentation with headings on each slide and at least one (1) relevant graphic (photograph, graph, clip art, etc.). Ensure that the presentation is visually appealing and readable from up to 18 feet away. Check with your professor for any additional instructions.
b. Include a title slide containing the title of the assignment, your name, your professor’s name, the course title, and the date.
The specific course learning outcomes associated with this assignment are:
· Explain effective approaches to the broad spectrum of employee relations, including career development, fostering ethical behavior, discipline, labor relations, and dismissals.
· Use technology and information resources to research issues in human resource management.
· Write clearly and concisely about human resource management using proper writing mechanics.
Click here to view the grading rubric for this assignment.
Team Project Deliverable and Presentation
You team works for XYZ Company, which has a directional strategy focused on expanding the company through horizontal integration. Your team can determine the official name of the company and industry. The company does a great job keeping close watch on its cash position and consistently maintains a positive cash flow; is very solvent; controls its overhead expenses; has solid marketing and sales, production, and human resources performance metrics, and fosters a culture of strategic thinkers. Historically, your company has expanded through a combination of organic (new startups) and inorganic growth and feels it’s time to consider acquisition opportunities.
The Board is looking to engage in a friendly acquisition of a company that will not only increase its market share, but allow it to penetrate new markets and increase the company’s abilities to meet current and future consumer needs and expectations. Since management’s attitude is to pursue a friendly acquisition as opposed to a hostile takeover, your team may consider looking at conglomerates that have experienced significant growth through inorganic growth (acquisitions) and may now be looking to refocus on their core business and are willing to consider divesting some of its businesses that are within your industry. There could be other companies.
BUS308 Statistics for ManagersDiscussions To participate in .docxcurwenmichaela
BUS308
Statistics for Managers
Discussions
To participate in the following discussions, go to this week's
Discussion
link in the left navigation.
Language
Numbers and measurements are the language of business.. Organizations look at results, expenses, quality levels, efficiencies, time, costs, etc. What measures does your department keep track of ? How are the measures collected, and how are they summarized/described? How are they used in making decisions? (Note: If you do not have a job where measures are available to you, ask someone you know for some examples or conduct outside research on an interest of yours.)
Guided Response: Review several of your classmates’ posts. Respond to at least two of your classmates by providing recommendations for the measures being discussed.
Levels
Managers and professionals often pay more attention to the levels of their measures (means, sums, etc.) than to the variation in the data (the dispersion or the probability patterns/distributions that describe the data). For the measures you identified in Discussion 1, why must dispersion be considered to truly understand what the data is telling us about what we measure/track? How can we make decisions about outcomes and results if we do not understand the consistency (variation) of the data? Does looking at the variation in the data give us a different understanding of results?
Guided Response: Review several of your classmates’ posts. Respond to at least two classmates by commenting on the situations that are being illustrated.
.
BUS308 Week 4 Lecture 1
Examining Relationships
Expected Outcomes
After reading this lecture, the student should be familiar with:
1. Issues around correlation
2. The basics of Correlation analysis
3. The basics of Linear Regression
4. The basics of the Multiple Regression
Overview
Often in our detective shows when the clues are not providing a clear answer – such as
we are seeing with the apparent continuing contradiction between the compa-ratio and salary
related results – we hear the line “maybe we need to look at this from a different viewpoint.”
That is what we will be doing this week.
Our investigation changes focus a bit this week. We started the class by finding ways to
describe and summarize data sets – finding measures of the center and dispersion of the data with
means, medians, standard deviations, ranges, etc. As interesting as these clues were, they did not
tell us all we needed to know to solve our question about equal work for equal pay. In fact, the
evidence was somewhat contradictory depending upon what measure we focused on. In Weeks 2
and 3, we changed our focus to asking questions about differences and how important different
sample outcomes were. We found that all differences were not important, and that for many
relatively small result differences we could safely ignore them for decision making purposes –
they were due to simple sampling (or chance) errors. We found that this idea of sampling error
could extend into work and individual performance outcomes observed over time; and that over-
reacting to such differences did not make much sense.
Now, in our continuing efforts to detect and uncover what the data is hiding from us, we
change focus again as we start to find out why something happened, what caused the data to act
as it did; rather than merely what happened (describing the data as we have been doing). This
week we move from examining differences to looking at relationships; that is, if some measure
changes does another measure change as well? And, if so, can we use this information to make
predictions and/or understand what underlies this common movement?
Our tools in doing this involve correlation, the measurement of how closely two
variables move together; and regression, an equation showing the impact of inputs on a final
output. A regression is similar to a recipe for a cake or other food dish; take a bit of this and
some of that, put them together, and we get our result.
Correlation
We have seen correlations a lot, and probably have even used them (formally or
informally). We know, for example, that all other things being equal; the more we eat. the more
we weigh. Kids, up to the early teens, grow taller the older they get. If we consistently speed,
we will get more speeding tickets than those who obey the speed limit. The more efforts we put
into studying, the better grades we get. All of these are examples of correlations.
Correlatio.
BUS225 Group Assignment1. Service BlueprintCustomer acti.docxcurwenmichaela
BUS225 Group Assignment
1. Service Blueprint
Customer actions include the choice of visiting a Calvin Klein retail store, browsing clothes and asking for recommendations from a sales representative. Visible actions performed by Calvin Klein’s sales representative include greet customers upon arrival, check for inventory, bring clothes to customers and process payment. These actions are visible to customers and one invisible action performed by the sales representative would be finding customer clothes in the back room. The support processes include inventory-tracking system, inventory in the back room and POS systems, which allow the sales representative to deliver service smoothly.
2. Introduction
Calvin Klein is one amongst the leading fashion style and marketing studios within the world. It styles and markets women’s and men’s designer assortment attire and a variety of different products that area unit factory-made and marketed through an intensive network of licensing agreements and different arrangements worldwide.
2.1 Target Market
Calvin Klein targets male and female, and the millenials. The demographics of the people that would be receiving these messages from the “My Calvins” campaign would be men and women between the ages of 15-30, not married and have a median income.
Millenials believe that the next generation of robots are not going to replace people, but instead help to improve the effectiveness and service of industries. In today’s world, to suggest that automation will eliminate the need for human workers is proving to be as ridiculous as suggesting that tablets will replace laptops.
In the industrial world, robot design is pivoting from giant mechanical arms that take up factory floors, to smaller, more collaborative bots, that are designed to work alongside people. While these collaborative bots only make up 3% of the market today, they will make up 34% of the market by 2025.
3. Trend and importance of robotics
3.1. Role of robotics
The service sector is at an inflection point with regard to productivity gains and service industrialization similar to the industrial revolution in manufacturing that started in the eighteenth century. Robotics in combination with rapidly improving technologies like artificial intelligence (AI), mobile, cloud, big data and biometrics will bring opportunities for a wide range of innovations that have the potential to dramatically change service industries. The purpose of this paper is to explore the potential role service robots will play in the future and to advance a research agenda for service researchers (Wirtz et al. 2018).
Advancements in technology are radically transforming service, and increasingly providing the underlying basis for service strategy. Technological capabilities inevitably advance, firms will tend to move from standardized to personalized and from transactional to relational over time, implying that firms should be alert to technological opportunities to .
BUS301 Memo Rubric Spring 2020 - Student.docxBUS301 Writing Ru.docxcurwenmichaela
BUS301 Memo Rubric Spring 2020 - Student.docx
BUS301 Writing Rubric
Performance Dimensions
N/A
Not Met
Met
Comments
Organization (OABC)
Opening gets attention, provides context, and introduces topic
0
1
Agenda previews content of the document
0
1
Body
0
2
Sound paragraphing decisions (length and development)
Paragraphs limited to one topic per paragraph
Complete discussion of one topic before moving to next topic
Transitions and flow between paragraphs smooth
The overall flow/logic/structure of document is apparent
Closing summarizes and concludes, recommends, if appropriate
0
1
Content
The content of the document is relevant; information meaningful
0
2
The document is developed with adequate support and examples
0
2
The content is accurate and appropriate, with insightful analysis
0
2
Proofreading
The grammar and spelling are correct (proofread)
0
3
Punctuation—comma usage, capitalization, etc.—used correctly
0
3
The sentence structure and length are appropriate
0
1
Format
Appropriate formatting is used for type of document written
0
1
Good use of font, margins, spacing, headings, and visuals
0
1
[11/2016]
Example - Good - Corrected student example Spring 2020.docx
TO: Professor __________
FROM: Suzy Student
DATE: February 1, 2020
SUBJECT: Out of Class Experience – Cybersecurity Conference
Cybersecurity is a topic everyone should be concerned about, so I attended the 3rd Annual Cybersecurity Event held in the Grawn Atrium. I gained insight and knowledge from listening to the speakers that came from different kinds of industries. In this memo, I will discuss what I learned from the speaker and two takeaways: 1) cybersecurity is everywhere, 2) personal identifiable information, and 3) cybersecurity for the business student.
Cybersecurity is Everywhere
The conference was an opportunity to learn about cybersecurity. The first speaker talked about how companies are attacked in many different ways every day. The “bad guys” are trying to steal company information as well as employee information. Both kinds of information are valuable on the black market. The second speaker talked about the internet of things (IoT). These are things that are attached to the internet. The speaker talked about autonomous cars and medical equipment (heart) that talks to the internet. She talked about how cyber can and should influence designs. “Things” must be created with cybersecurity included in every step of the design. The last speaker talked about how my information has value. The “bad guys” steal my information and people want to buy it. Making money is one reason hackers steal millions of records.
Personal Identifiable Information
Personal Identifiable Information (PII) is any information relating to an identifiable person. There are laws in place to help make sure this information is secure. This topic is a takeaway for me because I had no idea my data had any value t.
BUS1431Introduction and PreferencesBUS143 Judgmen.docxcurwenmichaela
BUS143
1
Introduction and Preferences
BUS143: Judgment and Decision Making
Ye Li
All rights reserved ®
Why you decided to take this class
“Decisions are the essence of
management. They’re what
managers do—sit around all
day making (or avoiding)
decisions. Managers are judged
on the outcomes, and most of
them—most of us—have only
the foggiest idea how we do
what we do.”
Thomas Stewart
Former editor (2002-2008),
Harvard Business Review
BUS143
2
Decision Making: Two Questions
• Why is decision making difficult?
• What constitutes a good decision?
Decision Making: Good Process
• What is a decision?
– A costly commitment to a course of action.
• Outcomes versus Process
Outcomes
Good Bad
Process
Good
Bad
Bad “luck”
Good “luck”
BUS143
3
Components of a Good Decision
• I have considered my ABCs
– Alternatives
– Beliefs
– Consequences
• I am devoting an appropriate amount of
resources
• I have avoided major decision traps
Decision Making Components: The ABCs
• Alternatives
– Identification and articulation
– Construction/refinement
• Beliefs
– Identification and quantification of uncertainties
– Information collection/gathering
• Consequences
– Identification of consequences (and objectives
addressed by consequences)
– When possible, quantification of tradeoffs among
objectives
BUS143
4
Decision Making: Good Process
• Putting it all together (for now)…
Good decision making is choosing the
alternative that best meets your objectives
in the face of uncertainty about what
consequences will ensue.
3 Perspectives on Decision Making
• Normative
– How should people make decisions?
Related concepts: rational; optimizing; forward-looking
• Descriptive
– How do people make decisions?
Related concepts: boundedly rational; limited cognitive capacity;
heuristics or rule-based; myopic
• Prescriptive
– How can we help people make better decisions?
– Prescriptive advice via practical applications, in…
Management
Marketing
Finance
HR
Life!
BUS143
5
Example
• Problem
– Imagine two 1-mile-long (1.61km) pieces of railroad track, put
end to end, and attached to the ground at the extremes.
When it gets hot, each piece of track expands by 1 inch
(2.54cm), forcing the pieces to rise above the ground where
they meet in the middle.
How high will the track be in the middle?
• Normative rule:
– Pythagorean Theorem:
• Descriptive reality:
– Most people underestimate x. (We anchor on 1 inch.)
• Prescription:
– Use normative rule (geometry). Don’t rely on intuition.
More Examples
• Normative rule:
– Lighter objects should
be judged as lighter.
• Descriptive reality:
– Sometimes our vision
tricks us.
• Prescription:
– Use an outside reference
or instrument
– Note: Pilots have specific
strategies for
counteracting visual
illusions
Which box looks lighter?
BUS143
6
Class Philosophy
• Overarching goal:
– Help you to.
BUS210 analysis – open question codesQ7a01 Monthly OK02 Not .docxcurwenmichaela
BUS210 analysis – open question codes
Q7a
01 Monthly OK
02 Not trading hours
03 Every 2 weeks
05 Don’t know
Q8
01 More information wanted
02 More security/Police
03 More involvement from business
04 Inconvenient times
05 Street activation needs improvement
06 Too busy to be involved
08 More outside main areas
Q11
01 Toilets
02 Security/Police
03 Problems with access
04 Better parking needed
05 Has been positive improvement
Q14
01 Pedestrian flows
02 Tourist/visitor information
03 Business statistics – local and general
D2 Business Types
01 Accommodation/hospitality
02 Retail
03 Bank
04 Café/fast food
05 Professional services
06 Travel
07 NGO/Charity
08 Manufacturing
09 Media/art
Questionnaire
Introduce: We have been commissioned by the X Sydney Council to conduct independent research of its BID members. The research will be used to improve Council activities. Your comments will be confidential.
For the following statement, can you tell me whether you agree or disagree? Then ask: is that strongly/mildly agree/disagree?
1 = strongly agree 2 = mildly agree 3 = mildly disagree 4 = strongly disagree
5 = Don’t know (don’t say) 6 = N/A (don’t say) READ OUT AS INDICATED IN QUESTIONS BELOW
Write in rating
START QUESTIONS HERE: Firstly, some questions about Council BID membership and street activation groups
Q1 (read out scale options) I’m active in the Council BID
Q2 (read out scale options again) Local businesses support the BID
Q3 The BID should be doing more for businesses in X Sydney
Q4 I am satisfied with the street activation activities organised by the Council BID
Q5 I participate in the BID street activation groups (yes/no question) if yes go to Q7
Yes/No
Q6 I am interested in participating in a BID street activation group
Q7 Do you think BID member meetings should be more frequent?
If yes, how often (write in) ……………………………………………
YES/NO/Don’t know
Q8 Do you have any comments in relation to the questions I’ve just asked?
(write in)
……………………………………………………………………………………………………………
……………………………………………………………………………………………………………
……………………………………………………………………………………………………………
(read out) Now, Just a few questions about safety and amenities
Q9 (Read out scale again) Being able to access safety, crime prevention tools information and reporting forms all in one place through the BID website is something I value
Q10 The public space and amenity quality is good in the Council area
Q11 Do you have any comments about safety and amenities
(write in)
……………………………………………………………………………………………………………
……………………………………………………………………………………………………………
……………………………………………………………………………………………………………
And finally a few questions about communications (read out)
Q12 I a.
Bus101 quiz (Business Organizations)The due time is in 1hrs1 .docxcurwenmichaela
Bus101 quiz (Business Organizations)
The due time is in 1hrs
1/ Both socialism and communism are variations of:
Select one:
a. command economies.
b. competitive economies.
c. free-market economies.
d. plutocratic systems.
2 / To be effective, empowerment will require lower-level workers to :
Select one:
a. have more training.
b. accept less responsibility and lower wages.
c. receive less training.
d. have written policies regulating each aspect of their work.
3)
As a small business owner, Tanika can't afford to provide her employees with the high wages and benefits offered by big corporations. One way to retain her employees and create a high level of motivation would be to:
Select one:
a. threaten to fire her existing employees and hire new workers.
b. adopt a policy of promoting the workers who have been employed the longest.
c. empower her employees to develop their own ideas.
d. hire only family members, since they are more loyal.
4/
Anita is employed as plant manager for Mojo Industries, Incorporated. Though she spends some time performing all management functions, she is particularly concerned with tactical planning and controlling. Anita's position would be classified as part of Mojo's:
Select one:
a. top management.
b. lateral management.
c. supervisory management.
d. middle management.
5/
Which of the following policies would tend to foster entrepreneurship?
Select one:
a. establishing a currency that is tradable on world markets.
b. establishing more regulations to protect the environment.
c. developing policies to reduce corruption between individuals.
d. allowing public ownership of businesses.
6)
All else held equal, socially responsible firms:
Select one:
a. are viewed more favorably by consumers.
b. enjoy significantly higher profits.
c. often experience customer loyalty problems.
d. fail to earn sufficient profits for their owners.
7) After personal savings, the next largest source of capital for entrepreneurs is from:
Select one:
a. large multinational banks.
b. the Small Business Administration.
c. state and local governments.
d. friends and family.
8/
Patrick's Products has a manufacturing plant near Chicago. The plant specializes in compact washers and dryers for countries in which consumers have less living space. Patrick's Products participates in the global market through:
Select one:
a. importing.
b. dumping.
c. exporting.
d. balancing trade.
9/
Managers who listen to their subordinates and allow them to participate in decision-making are using the ____________ style of leadership.
Select one:
a. autocratic
b. free-rein
c. participative
d. bureaucratic
10/
Which of the following statements about partnerships is the most accurate?
Select one:
a. A partnership is simply a corporation with fewer than 100 owners.
b. A major advantage of a partnership is that it offers owners limited liability.
c. A major drawback of a partnership is that it is difficult to terminate.
d. Partnerships are taxed at the lowest corporate tax .
BUS 625 Week 4 Response to Discussion 2Guided Response Your.docxcurwenmichaela
BUS 625 Week 4 Response to Discussion 2
Guided Response: Your initial response should be a minimum of 300 words in length. Respond to at least two of your classmates by commenting on their posts. Though two replies are the basic expectation for class discussions, for deeper engagement and learning, you are encouraged to provide responses to any comments or questions others have given to you.
Below there are two of my classmate’s discussion that needs I need to response to their names are Umadevi Sayana
and Britney Graves
Umadevi Sayana
TuesdayMar 17 at 7:50am
Manage Discussion Entry
Twitter mining analyzed the Twitter message in predicting, discovering, or investigating the causation. Twitter mining included text mining that designed specifically to leverage Twitter content and context tweets. With the use of text mining, twitter was able to include analysis of additional information that associates to tweets, which include hashtags, names, and other related characteristics. The mining also employs much information as several tweets, likes, retweets, and favorites trying to understand the considerations better. Twitter using text mining was successful in capturing and reflecting different events that relate to other conventional and social media. In 2013, there were over 500 million messages per day for twitter and became impossible for any human to analyze. It became important than to develop computer-based algorithms, including data mining. Twitter implements text mining in analyzing the sentiment that associates with twitter messages. It based on the analysis of the keyword that words are having a negative, positive, or neutral sentiment (Sunmoo, Noémie& Suzanne, (Links to an external site.)n.d). Positive words, for example like great, beautiful, love, and negative words of stupid, evil, and waste, do regularly have lexicons. Using text mining, Twitter was able to capture sentiments by capturing many dictionary symbols. Moreover, the sentiment applied to abbreviations, emoticons, and repeated characters, symbols, and abbreviations.
The sentiments on topics of economics, politics, and security are usually negative, and sentiments related to sports are harmful. Twitter also used text mining to collect and analyze for topic modeling techniques over time. To pull out the data from Twitter, TwitterR used. “Someone well versed in database architecture and data storage is needed to extract the relevant information in different databases and to merge them into a form that is useful for analysis” ( Sharpe, De Veaux & Velleman, 2019, p.753). It provides the interface that connects to Twitter web API; retweetedby/ids also used combined with RCurl package in finding out several tweets that retweeted. Text mining is also used in Twitter to clean the text by taking out hyperlinks, numbers, stop words, punctuations, followed by stem completion. Text mining also implemented for social network analysis.
Web mining focus on data knowledge discovery .
BUS 625 Week 2 Response for Discussion 1 & 2Week 2 Discussion 1 .docxcurwenmichaela
BUS 625 Week 2 Response for Discussion 1 & 2
Week 2 Discussion 1 Response
Guided Response: Your initial response should be a minimum of 300 words in length. Respond to at least two of your classmates by commenting on their posts. In your response, provide your own interpretation of their distribution graph. Note any differences between your classmate’s interpretation and your own. Though two replies are the basic expectation for class discussions, for deeper engagement and learning you are encouraged to provide responses to any comments or questions others have given to you. Continuing to engage with peers and the instructor will further the conversation and provide you with opportunities to demonstrate your content expertise, critical thinking, and real-world experiences with the discussion topics.
Below there are two of my classmate’s discussion that needs I need to response to their names are Kristopher Wentworth and Ashley Thiberville
Kristopher Wentworth
This graph is a representation of single people versus married couples from the year 1950 to the year 2019. This information was gathered and presented by the U.S. Department of Commerce and the U.S. Census Bureau who have a good record of presenting accurate data and are highly credible. The U.S. Department of Commerce is responsible for promoting economic growth in the united states. The U.S. Census Bureau is an agency of the Federal government that is responsible for producing data about the people of America and the economy.
So, the graph that I chose to talk about is one showing the gap between how many people are married and how many people are single in the united states from 1950 - 2019. I chose this graph because it caught my attention right away because of the contrasting colors but also because of the information displayed. It is crazy to think that since 1950 the American population has more than doubled according to this graph and with the growing population, the numbers of married couples and singles rise too. However, if you look at the percentages of singles they haven't changed all too much. For example, the number of single Americans in 1950 was 37.3M and in 2019 it was 125.7M. Even with such a large population boom the percentage that was never married really hadn't changed going from 69% to 68%.
The presentation of this graph is excellent with the line graph being yellow and on a blue backdrop, it allows it to really stand out. The shape of the graph shows a sharp incline as the population in us explodes. Since this graph is focused on the single population of America it puts the focus on that with stats like "never been married, divorced, widowed" because there are multiple ways to be single and really only one way to be married.
Ashley Thiberville
The above histogram was compiled by the United States Census Bureau to show the rise of one-person households in the US. The Census Bureau is a branch of the Department of Commerce within the United States gov.
Bus 626 Week 6 - Discussion Forum 1Guided Response Respon.docxcurwenmichaela
Bus 626 Week 6 - Discussion Forum 1
Guided Response: Respond to at least two of your fellow students’ and to your instructor’s posts in a substantive manner and provide information or concepts that they may not have considered. Each response should have a minimum of 100 words. Support your position by using information from the week’s readings. You are encouraged to post your required replies earlier in the week to promote more meaningful and interactive discourse in this discussion forum. Continue to monitor the discussion forum until Day 7 and respond with robust dialogue to anyone who replies to your initial post.
Jocelyn Harnett
Egypt has a sizable trade deficit that has continued to grow through the 21st century. The country has imports that make up a third of GDP and exports that make up one tenth of GDP. Egypt has many critical trade partners that include China, the United States, and the Gulf Arab countries. Throughout history Egypt has had an unstable government which has led to an unstable economy. This is related to the fluctuations the country has experienced in tariffs and taxes. The country has stabilized in recent years, but the historic instability still remains a critical factor when considering the expansion of Wal-Mart into Egypt. The trade deficit would not be a concern under normal conditions due to the fact that this means money is flowing into the country and creating new opportunities, but because the government is not stable Wal-Mart would want to ascertain that money was being invested properly in the future. If money is not being utilized correctly than the trade deficit becomes a concern because future generations are inheriting a debt that had no payback associated with it. The exchange rate of the Egyptian pound has gotten stronger to the US Dollar, which is a good indicator the economy is heading in the correct direction. Wal-Mart expansion could benefit from getting into the market in Egypt at the right time to see major profits.
Egypt is a market that will continue to grow as the internal government becomes stabilized and the country continues to focus on improving the economic welfare of the people. Currently the market in Egypt is volatile and companies that select to make an investment here must be aware of the many different cultural aspects that will affect success. The government is working to “find solutions and solve difficulties for people and businesses” (Bawaba, 2019) and has seen success in the first half of 2019. “At the time of May 31, 2019, the whole country had 721,516 businesses doing business, increasing 23,921 enterprises (3.43 %) compared to the end of 2018.” (Bawaba, 2019). This sort of success validates a foreign company wanting to make an investment, but continued analysis of the country’s government stability will be needed before each new storefront is added.
References:
Bawaba, A. (2019). Egypt : "Reviewing tax policies, finding solutions to solve difficulties for people and .
BUS 499, Week 8 Corporate Governance Slide #TopicNarration.docxcurwenmichaela
BUS 499, Week 8: Corporate Governance
Slide #
Topic
Narration
1
Introduction
Welcome to Senior Seminar in Business Administration.
In this lesson we will discuss Corporate Governance.
Please go to the next slide.
2
Objectives
Upon completion of this lesson, you will be able to:
Describe how corporate governance affects strategic decisions.
Please go to the next slide.
3
Supporting Topics
In order to achieve these objectives, the following supporting topics will be covered:
Separation of ownership and managerial control;
Ownership concentration;
Board of directors;
Market for corporate control;
International corporate governance; and
Governance mechanisms and ethical behavior.
Please go to the next slide.
4
Separation of Ownership and Managerial Control
To start off the lesson, corporate governance is defined as a set of mechanisms used to manage the relationship among stakeholders and to determine and control the strategic direction and performance of organizations. Corporate governance is concerned with identifying ways to ensure that decisionsare made effectively and that they facilitate strategic competitiveness. Another way to think of governance is to establish and maintain harmony between parties.
Traditionally, U. S. firms were managed by founder- owners and their descendants. As firms became larger the managerial revolution led to a separation of ownership and control in most large corporations. This control of the firm shifted from entrepreneurs to professional managers while ownership became dispersed among unorganized stockholders. Due to these changes modern public corporation was created and was based on the efficient separation of ownership and managerial control.
The separation of ownership and managerial control allows shareholders to purchase stock. This in turn entitles them to income from the firm’s operations after paying expenses. This requires that shareholders take a risk that the firm’s expenses may exceed its revenues.
Shareholders specialize in managing their investment risk. Those managing small firms also own a significant percentage of the firm and there is often less separation between ownership and managerial control. Meanwhile, in a large number of family owned firms, ownership and managerial control are not separated at all. The primary purpose of most large family firms is to increase the family’s wealth.
The separation between owners and managers creates an agencyrelationship. An agency relationship exists when one or more persons hire another person or persons as decision- making specialists to perform a service. As a result an agency relationship exists when one party delegates decision- making responsibility to a second party for compensation. Other examples of agency relationships are consultants and clients and insured and insurer. An agency relationship can also exist between managers and their employees, as well as between top- level managers and the firm’s owners.
The sep.
BUS 499, Week 6 Acquisition and Restructuring StrategiesSlide #.docxcurwenmichaela
BUS 499, Week 6: Acquisition and Restructuring Strategies
Slide #
Topic
Narration
1
Introduction
Welcome to Business Administration.
In this lesson we will discuss Acquisition and Restructuring Strategies.
Please go to the next slide.
2
Objectives
Upon completion of this lesson, you will be able to:
Identify various levels and types of strategy in a firm.
Please go to the next slide.
3
Supporting Topics
In order to achieve this objective, the following supporting topics will be covered:
The popularity of merger and acquisition strategies;
Reasons for acquisitions;
Problems in achieving acquisition success;
Effective acquisitions; and
Restructuring.
Please go to the next slide.
4
The Popularity of Merger and Acquisition Strategies
The acquisition strategy has been a popular strategy among U.S. firms for many years. Some believe that this strategy played a central role in an effective restructuring of U.S. business during the 1980s and 1990s and into the twenty-first century.
An acquisition strategy is sometimes used because of the uncertainty in the competitive landscape. A firm may make an acquisition to increase its market power because of a competitive threat, to enter a new market because of the opportunity available in that market, or to spread the risk due to the uncertain environment.
The strategic management process calls for an acquisition strategy to increase a firm’s strategic competitiveness as well as its returns to shareholders. Thus, an acquisition strategy should be used only when the acquiring firm will be able to increase its value through ownership of the acquired firm and the use of its assets.
Please go to the next slide.
5
Mergers, Acquisitions, and Takeovers
A merger is a strategy through which two firms agree to integrate their operations on a relatively coequal basis. Few true mergers actually occur, because one party is usually dominant in regard to market share or firm size.
An acquisition is a strategy through which one firm buys a controlling, or one hundred percent, interest in another firm with the intent of making the acquired firm a subsidiary business within its portfolio. In this case, the management of the acquired firm reports to the management of the acquiring firm. Although most mergers are friendly transactions, acquisitions can be friendly or unfriendly.
A takeover is a special type of an acquisition strategy wherein the target firm does not solicit the acquiring firm’s bid. The number of unsolicited takeover bids increased in the economic downturn of 2001 to 2002, a common occurrence in economic recessions; because the poorly managed firms that are undervalued relative to their assets are more easily identified.
On a comparative basis, acquisitions are more common than mergers and takeovers.
Please go to the next slide.
6
Reasons for Acquisitions
There are a number of reasons firms decide to acquire another company. These are:
Increased market power;
Overcoming entry barriers;
Co.
BUS 499, Week 4 Business-Level Strategy, Competitive Rivalry, and.docxcurwenmichaela
BUS 499, Week 4: Business-Level Strategy, Competitive Rivalry, and Competitive Dynamics
Slide #
Topic
Narration
1
Introduction
Welcome to Senior Seminar in Business Administration.
In this lesson, we will discuss Business-Level Strategy, Competitive Rivalry, and Competitive Dynamics.
Next slide.
2
Objectives
Upon completion of this lesson, you will be able to:
Identify various levels and types of strategy in a firm.
Next slide.
3
Supporting Topics
In order to achieve this objective, the following supporting topics will be covered:
Customers: their relationship with business-level strategies;
The purpose of a business-level strategy;
Types of business-level strategies;
A model of competitive rivalry;
Competitor analysis;
Drivers of competitive actions and responses;
Competitive rivalry;
Likelihood of attack;
Likelihood of response; and
Competitive dynamics.
Next slide.
4
Customer Relationships
Strategic competitiveness results only when the firm is able to satisfy a group of customers by using its competitive advantages as the basis for competing in individual product markets. A key reason firms must satisfy customers with their business-level strategy is that returns earned from relationships with customers are the lifeblood of all organizations. The most successful companies try to find new ways to satisfy current customers and/or meet the needs of new customers.
The firm’s relationships with its customers are strengthened when it delivers superior value to them. Strong interactive relationships with customers often provide the foundation for the firm’s efforts to profitably serve customers’ unique needs.
The reach dimension of relationships with customers is concerned with the firm’s access and connection to customers. Richness is concerned with the depth and detail of the two-way flow of information between the firm and the customer. Affiliation is concerned with facilitating useful interactions with customers.
Deciding who the target customer is that the firm intends to serve with its business-level strategy is an important decision. Companies divide customers into groups based on differences in the customers’ needs to make this decision. Dividing customers into groups based on their needs is called market segmentation, which is a process that clusters people with similar needs into individual and identifiable groups.
Next slide.
5
Customer Relationships, continued
After the firm decides who it will serve, it must identify the targeted customer group’s needs that its good or services can satisfy. Successful firms learn how to deliver to customers what they want and when they want it. In a general sense, needs are related to a product’s benefits and features. Having close and frequent interactions with both current and potential customers helps firms identify those individuals’ and groups’ current and future needs.
As explained in previous lessons, core competencies are resources and capabilities that serve as a source of.
BUS 437 Project Procurement Management Discussion QuestionsWe.docxcurwenmichaela
BUS 437 Project Procurement Management Discussion Questions
Week 2 Discussion
“Effective Management.” There are three (3) recommendations for effective management of projects in concurrent multiphase environments: Organizational System Design, System Implementation, and Managing in Concurrent Engineering.· Which of these three (3) recommendations for effective management would you or do you use most often? Why?
Week 3 Discussion
Top of Form
“Managing Configuration and Data for Effective Project Management.” The process protocol model consists of thirteen (13) steps from Inception to Feedback.· What are the steps?· Can any be skipped in this process model? What are the steps?
Week 4 Discussion“Organizational Project Management Maturity Model.” Students will respond to the following:· What is the four-step process of innovation and learning and how can your organization apply these steps to manage a project?· Of the five (5) levels of an organizational project management maturity model, which level is often the most difficult to manage? Why?
INTEGRATED SEMESTER ASSIGNMENT
(FINC 300, INFO 300, MGMT 300, MKTG 300)
DUE: April 12, 2019
INSTRUCTIONS:
The objective of the integrated semester is to help you extend your knowledge of how the finance,
operations, management, and marketing disciplines work and how they integrate their functioning in
the real world of business. This assignment is an assessment of how well you understand this
integration. It is worth 10% of your course grade.
YOUR ASSIGNMENT IS TO ANSWER ALL OF THE QUESTIONS, IN A SINGLE DOCUMENT:
• The assignment should be prepared as a Word document, 12 -14 pages in length (approx. 3
pages for each discipline’s questions).
• The document should be double spaced, using Ariel font #12.
• Label each section (e.g., FINANCE) to indicate which discipline’s questions you are
answering.
• Add any Appendices at the end of the Word document.
• Upload the entire Word file through the link on Canvas to each of your Integrated Semester
courses by the due date.
Note: Your reference sources, in addition to the base case and question sets, should be online sites
and articles, Bloomberg terminals, your Integrated Semester textbooks and PowerPoint slides. Also
note, Turnitin, a software tool that improves writing and prevents plagiarism, will be used to assess
your sourcing of information. Do your own work.
FINANCE ASSIGNMENT
The objective of the integrated semester is to help you extend your knowledge of how the finance,
operations, management, and marketing disciplines work and how they integrate their functioning in
the real world of business. This assignment is an assessment of how well you understand this
integration. It is worth 10% of your course grade.
Use either the Bloomberg terminals located at the Feliciano School of Business or other reputable
sources such as finance.yahoo.com, morningstar.com or Wall Street Jo.
BUS 480.01HY Case Study Assignment Instructions .docxcurwenmichaela
BUS 480.01HY Case Study Assignment
Instructions
Instructions: Each of you have been assigned a company to complete a case study analysis report.
The case distribution can be found on BlackBoard (course content -> case study analysis - > case
study distribution). Complete a thorough research on your company in order to complete the
analysis. It is required for you to use scholarly journals and peer-reviewed articles, which can be
found on the University’s website in the library section. I have provided you with very detailed
information on how to complete a thorough case analysis report. I am available during my office
hours to discuss. I will also schedule a case analysis session during lunch time this week. If you are
able to make it, please attend for one-on-one assistance.
Your “draft is due this Thursday, October 11th. I am not looking for perfection here, but please do
your best in writing and researching. Your final product will be due on Thursday, October 18th.
BUS 480.01HY Case Study Assignment
Instructions
1. Format – please review the case study format guidelines placed on BlackBoard
The use of headers and sub-headers is strongly suggested
2. Submission
1. Submit to BlackBoard (course content -> case study analysis - > Case Study Analysis
Report). Failure to submit in proper area will result in a 0.
3. Introduction
In 3-4 paragraphs describe the case facts and background. This should include BRIEF
information about the firm, however do NOT simply duplicate what is in the case itself.
As things change quickly in business, you may wish to check the current status of the
firm and briefly discuss the most current information.
4. Body
This should be about 4-5 pages in length (minimum – this is only a guideline). Review
posted guidelines for more information/detail
a) State the Problem/Key Issues
What are the key marketing or business issues in the case? These might be problems,
opportunities or challenges the firm is facing. For example:
o Sales have declined by 10 percent in the last year.
o The competition has launched a new and innovative product.
o Consumer tastes have changed and the firm’s most successful product is at risk.
o The CEO made a public racial slur and has affected the company internally and
externally.
5. Conclusion (include recommendations in this section)
For the issues you identified above, you must identify potential solutions and analyze
each of them. For example, for the decline in sales noted above we might try any of the
following, among other options:
1. increase advertising
2. develop a new product
3. implement diversity training
4. launch a brand awareness campaign
For each of the alternatives, you should analyze the costs, benefits, resources required
and possible outcomes. Typically, you will have 3-4 of these alternatives. Any given
alternative solution might address multiple issues. If t.
BUS 308 Week 5 Lecture 3 A Different View Effect Sizes .docxcurwenmichaela
BUS 308 Week 5 Lecture 3
A Different View: Effect Sizes
Expected Outcomes
After reading this lecture, the student should be familiar with:
1. What effect size measures exist for different statistical tests.
2. How to interpret an effect size measure.
3. How to calculate an effect size measure for different tests.
Overview
While confidence intervals can give us a sense of how much variation is in our decisions,
effect size measures help us understand the practical significance of our decision to reject the
null hypothesis. Not all statistically significant results are of the same importance in decision
making. A difference in means of 25 cents is more important with means around a dollar than
with means in the millions of dollars, yet with the right sample size both groups can have this
difference be statistically significant.
Effect size measures help us understand the practice importance of our decision to reject
the null hypothesis.
Excel has limited functions available for us to use on Effect Size measures. We generally
need to take the output from the other functions and generate our Effect Size values.
Effect Sizes
One issue many have with statistical significance is the influence of sample size on the
decision to reject the null hypothesis. If the average difference in preference for a soft drink was
found to be ½ of 1%; most of us would not expect this to be statistically significant. And,
indeed, with typical sample sizes (even up to 100), a statistical test is unlikely to find any
significant difference. However, if the sample size were much larger; for example, 100,000; we
would suddenly find this miniscule difference to be significant!
Statistical significance is not the same as practical significance. If for example, our
sample of 100,000 was 1% more in favor of an expensive product change, would it really be
worthwhile making the change? Regardless of how large the sample was, it does not seem
reasonable to base a business decision on such a small difference.
Enter the idea of Effect Size. The name is descriptive but at the same time not very
illuminating on what this measure does. We will get to specific measures shortly, but for now,
let’s look at how an Effect Size measure can help us understand our findings. First, the name:
Effect Size. What effect? What size? In very general terms, the effect we are monitoring is the
effect that occurs when we change one of the variables. For example, is there an effect on the
average compa-ratio when we change from male to female. Certainly, but not all that much, as
we found no significant difference between the average male and female compa-ratios. Is there
an effect when we change from male to female on the average salary? Definitely. And it is
much larger than what we observed on the compa-ratio means. We found a significant
difference in the average salary for males than females – around $14,000.
The Effect Siz.
BUS 340 Week 5BUS 340 Business CommunicationsWee.docxcurwenmichaela
BUS 340
Week 5
BUS 340
Business Communications
Week 5
It is imperative that we make this last week of class a priority. You’ve made it this far – you are almost to the finish line!
Take advantage of your peers knowledge and experience in the discussion forums
Ask questions if you have them. I am here to help you succeed
BUS 340: Business Communications
Introduction to Week 5
In our last week together, we will examine practices for creating and delivering oral presentations. Since ours is a virtual classroom, this means that what we are unable to convey verbally will be that much more critical to demonstrate via the written word.
As such, this topic will expand to evaluate the component of effective online presentations.
Overview & Requirements
Read: Chapters 13-176 in our text book
Two Discussion Questions: Original Post due by Thursday, 11:59pm
Respond to at least two classmates (or your instructor) for each discussion question: Due by Monday 11:59 pm
Week 3 Assignment: Business Proposal Due by Monday 11:59 pm
Review: Coughlin and Mishra’s article (located under Required Resources)
Week 5
Bovee, C. L. & Thill, J. V. (2016). Business communication today. (13th ed.).
Chapter 13: Finding, Evaluating, and Processing Information
Chapter 14: Planning Reports and Proposals
Chapter 15: Writing and Completing Reports and Proposals
Chapter 16: Developing Presentations in a Social Media Environment
Chapter 17: Enhancing Presentations with Slides and Other Visuals
Coughlin, D. (2014). Focusing on the fundamentals of effective communication within an organization. Effective Executive, 17(1).
Mishra, S. (2015). Effective communication for corporate sector: A need for a paradigm shift. Indian Journal of Health and Wellbeing, 6(7).
Week 5
Required Reading
London, M., & Mone, E. (2012). Leadership for today and the future. (1st. ed.). San Diego, CA: Bridgepoint Education, Inc.
5
This week we will:
Craft an effective message for electronic media
Assess practices for creating and delivering oral presentations
Evaluate the components of effective online presentations
Create a business proposal that identifies the benefits of change
Week 5
Objectives for Week 5
ActivityDue DateFormat Grading Percent Read Chapters 13-17Forecast PresentationsDay 3
(1st post) Discussion 4Respond to at least two classmates’ (or your instructor’s) postsDay 7DiscussionIncluded as part of 2 pts Enhancing Presentations Day 3
(1st post) Discussion 4Respond to at least two classmates’ (or your instructor’s) postsDay 7DiscussionIncluded as part of 4 pts Business ProposalDay 7 Assignment 25
Week 5
7
In our contemporary organizations, business communication is far more detailed and complex than most people in the workplace realize.
Without acute attention to detail, many people in the workplace miss details that prove critical to successful business communication
Week 5
8
Tips for Creating & Delivering effective
oral present.
BUS 308 – Week 4 Lecture 2 Interpreting Relationships .docxcurwenmichaela
BUS 308 – Week 4 Lecture 2
Interpreting Relationships
Expected Outcomes
After reading this lecture, the student should be able to:
1. Interpret the strength of a correlation
2. Interpret a Correlation Table
3. Interpret a Linear Regression Equation
4. Interpret a Multiple Regression Equation
Overview
As in many detective stories, we will often find that when one thing changes, we see that
something else has changed as well. Moving to correlation and regression opens up new insights
into our data sets, but still lets us use what we have learned about Excel tools in setting up and
generating our results.
The correlation between events is mirrored in data analysis examinations with correlation
analysis. This week’s focus changes from detecting and evaluating differences to looking at
relationships. As students often comment, finding significant differences in gender-based
measures does not explain why these differences exist. Correlation, while not always explaining
why things happen gives data detectives great clues on what to examine more closely and helps
move us towards understanding why outcomes exist and what impacts them. If we see
correlations in the real world, we often will spend time examining what might underlie them;
finding out if they are spurious or causal.
Regression lets us use relationships between and among our variables to predict or
explain outcomes based upon inputs, factors we think might be related. In our quest to
understand what impacts the compa-ratio and salary outcomes we see, we have often been
frustrated due to being basically limited to examining only two variables at a time, when we felt
that we needed to include many other factors. Regression, particularly multiple regression, is the
tool that allows us to do this.
Linear Correlation
When two things seem to move in a somewhat predictable way, we say they are
correlated. This correlation could be direct or positive, both move in the same direction, or it
could be inverse or negative, where when one increases the other decreases. The Law of Supply
in economics is a common example of an inverse (or negative) correlation, where the more
supply we have of something, the less we typically can charge for it; the Law of Demand is an
example of a direct (or positive) correlation as the more demand exists for something, the more
we can charge for it. Height and weight in young children is another common example of a
direct correlation, as one increases so does the other measure.
Probably the most commonly used correlation is the Pearson Correlation Coefficient,
symbolized by r. It measures the strength of the association – the extent to which measures
change together – between interval or ratio level measures as well as the direction of the
relationship (inverse or direct). Several measures in our company data set could use the Pearson
Correlation to show relationships; salary and midpoint, salary and yea.
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.
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
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.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
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?
1. 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.”
2. 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
3. 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 measuring related or similar things, we have two types of
differences: differences in
consistency and differences in average values. Some examples
of things that should be the
“same” could be:
• The time it takes to drive to work in the morning.
• The quality of parts produced on the same manufacturing line.
• The time it takes to write a 3-page paper in a class.
• The weight of a 10-pound bag of potatoes.
• Etc.
All of these “should” be the same, as each relates to the same
outcome. Yet, they all differ. We
all experience differences in travel time, and the time it takes to
produce the same output on the
job or in school (such as a 3-page paper). Production standards
all recognize that outcomes
should be measured within a range rather than a single point.
For example, few of us would be
upset if a 10-pound bag of potatoes weighed 9.85 pounds or
would think we were getting a great
deal if the bag weighed 10.2 pounds. We realize that it is
virtually impossible for a given
4. number of potatoes to weigh exactly the same and we accept
this as normal.
One reason for our acceptance is that we know that variation
occurs. Variation is simply
the differences that occur in things that should be “the same.”
If we can measure things with
enough detail, everything we do in life has variation over time.
When we get up in the morning,
how long it takes to get to work, how effective we are at doing
the same thing over and over, etc.
Except for physical constants, we can say that things differ and
we need to recognize this. A side
note: variation exists in virtually everything we study (we have
more than one language, word,
sentence, paragraph, past actions, financial transactions, etc.),
but only in statistics do we bring
this idea front and center for examination.
This suggests that any population that we are interested in will
consist of things that are
slightly different, even if the population contains only one
“thing.” Males are not all the same,
neither are females. Manufactured parts differ in key
measurements; this is the reason we have
quality control checking to make sure the differences are not
too large. So, even if we measure
everything in our population we will have a mean that is
accompanied by a standard deviation
(or range). Managers and professionals need to manage this
variation, whether it is quantitative
(such as salary paid for similar work) or even qualitative (such
as interpersonal interactions with
customers).
The second reason that we are so concerned with differences is
5. that we rarely have all the
evidence, or all the possible measures of what we are looking
for. Having this would mean we
have access to the entire population (everything we are
interested in); rarely is this the case.
Generally, all decisions, analysis, research, etc. is done with
samples, a selected subset of the
population. And, with any sample we are not going have all the
information needed, obviously;
but we also know that each sample we take is going to differ a
bit. (Remember, variation is
everywhere, including in the consistency of sample values.) If
you are not sure of this, try
flipping a coin 10 times for 10 trials, do you expect or get the
exact same number of heads for
each trial? Variation!
Since we are making decisions using samples, we have even
more variation to consider
than simply that with the population we are looking at. Each
sample will be slightly different
from its population and from others taken from the same
population.
How do we make informed decisions with all this variation and
our not being able to
know the “real” values of the measures we are using? This
question is much like how detectives
develop the “motive” for a crime – do they know exactly how
the guilty party felt/thought when
they say “he was jealous of the success the victim had.” This
could be true, but it is only an
approximation of the true feelings, but it is “close enough” to
6. say it was the reason. It is similar
with data samples, good ones are “close enough” to use the
results to make decisions with. The
question we have now focuses on how do we know what the
data results show?
The answer lies with statistical tests. They can use the
observed variation to provide
results that let us make decisions with a known chance of being
wrong! Most managers hope to
be right just over 50% of the time, a statistical decision can be
correct 95% or more of the time!
Quite an improvement.
Sampling. The use of samples brings us to a distinction in
summary statistics, between
descriptive and inferential statistics. With one minor exception
(discussed shortly), these two
appear to be the same: means, standard deviations, etc.
However, one very important distinction
exists in how we use these. Descriptive statistics, as we saw
last week, describes a data set. But,
that is all they do. We cannot use them to make claims or
inferences about any other larger
group.
Making inferences or judgements about a larger population is
the role of inferential
statistics and statistical tests. So, what makes descriptive
statistics sound enough to become
inferential statistics? The group they were taken from! If we
have a sample that is randomly
selected from the population (meaning that each member has the
same chance of being selected
at the start), then we have our best chance of having a sample
that accurately reflects the
7. population, and we can use the statistics developed from that
sample to make inferences back to
the population. (How we develop a randomly selected sample is
more of a research course issue,
and we will not go into these details. You are welcome to
search the web for approaches.)
Random Sampling. If we are not working with a random
sample, then our descriptive
statistics apply only to the group they are developed for. For
example, asking all of our friends
their opinion of Facebook only tells us what our friends feel; we
cannot say that their opinions
reflect all Facebook users, all Facebook users that fall in the
age range of our friends, or any
other group. Our friends are not a randomly selected group of
Facebook users, so they may not
be typical; and, if not typical users, cannot be considered to
reflect the typical users.
If our sample is random, then we know (or strongly suspect) a
few things. First, the
sample is unlikely to contain both the smallest and largest value
that exists in the larger
population, so an estimate of the population variation is likely
to be too small if based on the
sample. This is corrected by using a sample standard deviation
formula rather than a population
formula. We will look at what this means specifically in the
other lectures this week; but Excel
will do this for us easily.
Second, we know that our summary statistics are not the same
8. as the population’s
parameter values. We are dealing with some (generally small)
errors. This is where the new
statistics student often begins to be uncomfortable. How can we
make good judgements if our
information is wrong? This is a reasonable question, and one
that we, as data detectives, need to
be comfortable with.
The first part of the answer falls with the design of the sample,
by selecting the right
sample size (how many are in the sample), we can control the
relative size of the likely error.
For example, we can design a sample where the estimated error
for our average salary is about
plus or minus $1,000. Does knowing that our estimates could
be $1000 off change our view of
the data? If the female average was a thousand dollars more
and the male salary was a thousand
dollars less, would you really change your opinion about them
being different? Probably not
with the difference we see in our salary values (around 38K
versus 52K). If the actual averages
were closer together, this error range might impact our
conclusions, so we could select a sample
with a smaller error range. (Again, the technical details on how
to do this are found in research
courses. For our statistics class, we assume we have the correct
sample.)
Note, this error range is often called the margin of error. We
see this most often in
opinion polls. For example, if a poll said that the percent of
Americans who favored Federal
Government support for victims of natural disasters (hurricanes,
floods, etc.) was 65% with a
9. margin of error of +/- 3%; we would say that the true proportion
was somewhat between 62% to
68%, clearly a majority of the population. Where the margin of
error becomes important to
know is when results are closer together, such as when support
is 52% in favor versus 48%
opposed, with a margin of error of 3%. This means the actual
support could be as low as 49% or
as high as 55%; meaning the results are generally too close to
make a solid decision that the issue
is supported by a majority, the proverbial “too close to call.”
The second part of answering the question of how do we make
good decisions introduces
the tools we will be looking at this week, decision making
statistical tests that focus on
examining the size of observed differences to see if they are
“meaningful” or not. The neat part
of these tools is we do not need to know what the sampling
error was, as the techniques will
automatically include this impact into our results!
The statistical tools we will be looking at for the next couple of
weeks all “work” due to a
couple of assumptions about the population. First, the data
needs to be at the interval or ratio
level; the differences between sequential values needs to be
constant (such as in temperature or
money). Additionally, the data is assumed to come from a
population that is normally
distributed, the normal curve shape that we briefly looked at
last week. Note that many
statisticians feel that minor deviations from these strict
assumptions will not significantly impact
the outcomes of the tests.
10. The tools for this week and next use the same basic logic. If we
take a lot of samples
from the population and graph the mean for all of them, we will
get a normal curve (even if the
population is not exactly normal) distribution called the
sampling distribution of the mean.
Makes sense as we are using sample means. This distribution
has an overall, or grand, mean
equal to that of the population. The standard deviation equals
the standard deviation of the
population divided by the square root of the population. (Let’s
take this on faith for now, trust
me you do not want to see the math behind proving these. But
if you do, I invite you to look it
up on the web.) Now, knowing – in theory – what the mean
values will be from population
samples, we can look at how any given sample differs from
what we think the population mean
is. This difference can be translated into what is essentially a
z-score (although the specific
measure will vary depending upon the test we are using) that we
looked at last week. With this
statistic, we can determine how likely (the probability of)
getting a difference as large or larger
than we have purely by chance (sampling error from the actual
population value) alone.
If we have a small likelihood of getting this large of a
difference, we say that our
difference is too large to have been purely a sampling error, and
we say a real difference exists or
that the mean of the population that the sample came from is not
what we thought.
11. That is the basic logic of statistical testing. Of course, the
actual process is a bit more
structured, but the logic holds: if the probability of getting our
result is small (for example 4% or
0.04), we say the difference is significant. If the probability is
large (for example 37% or 0.37),
then we say there is not enough evidence to say the difference is
anything but a simple sampling
error difference from the actual population result.
The tools we will be adding to our bag of tricks this week will
allow us to examine
differences between data sets. One set of tools, called the t-
test, looks at means to see if the
observed difference is significant or merely a chance difference
due mostly to sampling error
rather than a true difference in the population. Knowing if
means differ is a critical issue in
examining groups and making decisions.
The other tool – the F-test for variance, does the same for the
data variation between
groups. Often ignored, the consistency within groups is an
important characteristic in
understanding whether groups having similar means can be said
to be similar or not. For
example, if a group of English majors all took two classes
together, one math and one English,
would you expect the grade distributions to be similar, or would
you expect one to show a larger
range (or variation) than the other?
We will see throughout the class that consistency and
differences are key elements to
understanding what the data is hiding from us, or trying to tell
us – depending on how you look
12. at it. In either case, as detectives our job is to ferret out the
information we need to answer our
questions.
Hypothesis Testing-Are Differences Meaningful
Here is where the crime scene experts come in. Detectives have
found something but are
not completely sure of how to interpret it. Now the training and
tools used by detectives and
analysts take over to examine what is found and make some
interpretations. The process or
standard approach that we will use is called the hypothesis
testing procedure. It consists of six
steps; the first four (4) set up the problem and how we will
make our decisions (and are done
before we do anything with the actual data), the fifth step
involves the analysis (done with
Excel), and the final and sixth step focuses on interpreting the
result.
The hypothesis testing procedure is a standardized decision-
making process that ensures
we make our decisions (on whether things are significantly
different or not) is based on the data,
and not some other factors. Many times, our results are more
conservative than individual
managerial judgements; that is, a statistical decision will call
fewer things significantly different
than many managerial judgement calls. This statistical
tendency is, at times, frustrating for
managers who want to show that things have changed. At other
times, it is a benefit such as if
13. we are hoping that things, such as error rates, have not changed.
While a lot of statistical texts have slightly different versions of
the hypothesis testing
procedure (fewer or more steps), they are essentially the same,
and are a spinoff of the scientific
method. For this class, we will use the following six steps:
1. State the null and alternate hypothesis
2. Select a level of significance
3. Identify the statistical test to use
4. State the decision rule. Steps 1 – 4 are done before we
examine the data
5. Perform the analysis
6. Interpret the result.
Step 1
A hypothesis is a claim about an outcome. It comes in two
forms. The first is the null
hypothesis – sometimes called the testable hypothesis, as it is
the claim we perform all of our
statistical tests on. It is termed the “Null” hypothesis, shown as
Ho, as it basically says “no
difference exists.” Even if we want to test for a difference,
such as males and females having a
different average compa-ratio; in statistics, we test to see if
they do not.
Why? It is easier to show that something differs from a fixed
point than it is to show that
the difference is meaningful – I mean how can we focus on
“different?” What does “different”
mean? So, we go with testing no difference. The key rule
about developing a null hypothesis is
that it always contains an equal claim, this could be equal (=),
14. equal to or less than (<=), or equal
to or more than (=>).
Here are some examples:
Ex 1: Question: Is the female compa-ratio mean = 1.0?
Ho: Female compa-ratio mean = 1.0.
Ex 2: Q: is the female compa-ratio mean = the male compa-
ratio mean?
Ho: Female compa-ratio mean = Male compa-ratio mean.
Ex. 3: Q: Is the female compa-ratio more than the male compa-
ratio? Note that this
question does not contain an equal condition. In this case, the
null is the opposite of what
the question asks:
Ho: Female compa-ratio <= Male compa-ratio.
We can see by testing this null, we can answer our initial
question of a directional
difference. This logic is key to developing the correct test
claim.
A null hypothesis is always coupled with an alternate
hypothesis. The alternate is the
opposite claim as the null. The alternate hypothesis is shown as
Ha. Between the two claims, all
possible outcomes must be covered. So, for our three examples,
the complete step 1 (state the
null and alternate hypothesis statements) would look like:
15. Ex 1: Question: Is the female compa-ratio mean = 1.0?
Ho: Female compa-ratio mean = 1.0.
Ha: Female compa-ratio mean =/= (not equal to) 1.0
Ex 2: Q: is the female compa-ratio mean = the male compa-
ratio mean?
Ho: Female compa-ratio mean = Male compa-ratio mean.
Ha: Female compa-ratio mean =/= Male compa-ration mean.
Ex. 3: Q: Is the female compa-ratio more than the male compa-
ratio?
Ho: Female compa-ratio <= Male compa-ratio
Ha: Female compa-ratio > Male compa-ratio. (Note that in this
case, the alternate
hypothesis is the question being asked, but the null is what we
always use as the
test hypothesis.)
When developing the null and alternate hypothesis,
1. Look at the question being asked.
2. If the wording implies an equality could exist (equal to, at
least, no more than, etc.),
we have a null hypothesis and we write it exactly as the
question asks.
3. If the wording does not suggest an equality (less than, more
than, etc.), it refers to the
16. alternate hypothesis. Write the alternate first.
4. Then, for whichever hypothesis statement you wrote, develop
the other to contain all
of the other cases. An = null should have a =/= alternate, an =>
null should have a <
alternate; a <= null should have a > alternate, and vice versa.
5. The order the variables are listed in each hypothesis must be
the same, if we list
males first in the null, we need to list males first in the
alternate. This minimizes
confusion in interpreting results.
Note: the hypothesis statements are claims about the population
parameters/values based
on the sample results. So, when we develop our hypothesis
statements, we do not consider the
sample values when developing the hypothesis statements. For
example, consider our desire to
determine if the compa-ratio and salary means for males and
females are different in the
population, based on our sample results. While the compa-ratio
means seemed fairly close
together, the salary means seemed to differ by quite a bit; in
both cases, we would test if the male
and female means were equal since that is the question we have
about the values in the
population.
If you look at the examples, you can notice two distinct kinds of
null hypothesis
statements. One has only an equal sign in it, while the other
17. contains an equal sign and an
inequality sign (<=, but it could be =>). These two types
correspond to two different research
questions and test results.
If we are only interested in whether something is equal or not,
such as if the male average
salary equals the female average salary; we do not really care
which is greater, just if they could
be the same in the population or not. For our equal salary
question, it is not important if we find
that the male’s mean is > (greater than) the female’s mean or if
the male’s mean is < (less than)
the female’s mean; we only care about a difference existing or
not in the population. This, by the
way, is considered a two-tail test (more on this later), as either
conditions would cause us to say
the null’s claim of equality is wrong: a result of “rejecting the
null hypothesis.”
The other condition we might be interested in, and we need a
reason to select this
approach, occurs when we want to specifically know if one
mean exceeds the other. In this
situation, we care about the direction of the difference. For
example, only if the male mean is
greater than the female mean or if the male mean is less than the
female mean.
Step 2
The level of significance is another concept that is critical in
statistics but is often not
used in typical business decisions. One senior manager told the
author that their role was to
ensure that the “boss’ decisions were right 50% +1 of the time
18. rather than 50% -1.” This
suggests that the level of confidence that the right decisions are
being made is around 50%. In
statistics, this would be completely unacceptable.
A typically statistical test has a level of confidence that the
right decision is being made is
about 95%, with a typical range from 90 to 99%. This is done
with our chosen level of
significance. For this class, we will always use the most
common level of 5%, or more
technically alpha = 0.05. This means we will live with a 5%
chance of saying a difference is
significant when it is not and we really have only a chance
sampling error.
Remember, no decision that does not involve all the possible
information that can be
collected will ever have a zero possibility of being wrong. So,
saying we are 95% sure we made
the right call is great. Marketing studies often will use an alpha
of .10, meaning that are 90%
sure when they say the marketing campaign worked. Medical
studies will often use an alpha of
0.01 or even 0.001, meaning they are 99% or even 99.9% sure
that the difference is real and not
a chance sampling error.
Step 3
Choosing the statistical test and test statistic depends upon the
data we have and the
question we are asking. For this week, we will be using compa-
ratio data in the examples and
19. salary data in the homework – both are continuous and at least
interval level data. The questions
we will look at this week will focus on seeing if there is a
difference in the average pay (as
measured by either the compa-ratio or salary) between males
and females in the population,
based on our sample results. After all, if we cannot find a
difference in our sample, should we
even be working on the question?
In the quality improvement world, one of the strategies for
looking for and improving
performance of a process is to first look at and reduce the
variation in the data. If the data has a
lot of variation, we cannot really trust the mean to be very
reflective of the entire data set.
Our first statistical test is called the F-test. It is used when we
have at least interval level
data and we are interested in determining if the variances of two
groups are significantly
different or if the observed difference is merely chance
sampling error. The test statistic for this
is the F.
Once we know if the variances are the same or not, we can
move to looking for
differences between the group means. This is done with the T-
test and the t-statistic. Details on
these two tests will be given later; for now, we just need to
know what we are looking at and
what we will be using.
Step 4
One of the rules in researching questions is that the decision
20. rule, how we are going to
make our decision once the analysis is done, should be stated
upfront and, technically, even
before we even get to the data. This helps ensure that our
decision is data driven rather than
being made by emotional factors to get the outcome we want
rather than the outcome that fits the
data. (Much like making our detectives go after the suspect that
did the crime rather than the one
they do not like and want to arrest, at least when they are being
honest detectives.)
The decision rule for our class is very simple, and will always
be the same:
Reject the null hypothesis if the p-value is less than our alpha
of .05. (Note: this would
be the same as saying that if the p-value is not less than 0.05,
we would fail to reject the null
hypothesis.)
We introduced the p-value last week, it is the probability of our
outcome being as large or
larger than we have by pure chance alone. The further from the
actual mean a sample mean is,
the less chance we have of getting a value that differs from the
mean that much or more; the
closer to the actual mean, the greater our chance would be of
getting that difference or more
purely by sampling error.
Our decision rule ties our criteria for significance of the
outcome, the step 2 choice of
alpha, with the results that the statistical tests will provide (and,
the Excel tests will give us the p-
values for us to use in making the decisions).
21. These four steps define our analysis, and are done before we do
any analysis of the data.
Step 5
Once we know how we will analyze and interpret the results, it
is time to get our sample
data and set it up for input into an Excel statistical function.
Some examples of how this data
input works will be discussed in the third lecture for this week.
This step is fairly easy, simply identify the statistical test we
want to use. The test to use
is based on our question and the related hypothesis claims. For
this week, if we are looking at
variance equality, we will use the F-test. If we are looking at
mean equality, we will use the T-
test.
Step 6
Here is where we bring everything together and interpret the
outcomes.
What is constant about this step is the need to:
1. Look at the appropriate p-value (indicated in the test outputs,
as we will see in lecture
2).
2. Compare the p-value with our value for alpha (0.05).
3. Make a decision: if the test p-value is less than or equal to
(<=) 0.05, we will reject
22. the null hypothesis. If the test p-value is more than (=>) 0.05,
we will fail to reject
the null hypothesis.
Rejecting the null hypothesis means that we feel the alternate
hypothesis is the more
accurate statement about the populations we are testing. This is
the same for all of our statistical
tests.
Once we have made our decision to reject or fail to reject the
null hypothesis, we need to
close the loop, and go back and answer our original question.
We need to take the statistical
result or rejecting or failing to reject the null and turn it into an
“English” answer to the question.
Doing so depends on how the original question lead to the
hypothesis statements. Examples of
this follow in Lecture 2.
Lectures 2 and 3 will show how to use this process in
conjunction with Excel and the F
and T tests. For now, focus on the logic of setting up the
testing instructions.
Summary
This week we begin our journey discovering ways to make
decisions on data, and more
specifically differences in data sets, based on generally agreed
upon approaches rather than by
“guess and by golly.” The process is called hypothesis testing
and is part of the scientific
method of research and decision making.
23. In this approach we always test a claim of no difference (the
null hypothesis) whether or
not we are suspect or desire to see an actual difference. The
null hypothesis is paired with an
alternate hypothesis that is exactly the opposite claim.
Decisions are made based on a p-value
which is the probability that we would see a difference as large
or larger as we got if the null
hypothesis is true. Small p-values mean we reject the null as
not being an accurate description of
the population we are looking at.
The hypothesis testing process (or procedure) has six steps.
The first four are completed
before we look at the data; the fifth step is the actual
calculation of the statistical test and the
final and sixth step is where the analysis of the results is done.
The steps are:
1. State the null and alternate hypothesis
2. Select a level of significance
3. Identify the statistical test to use
4. State the decision rule
5. Perform the analysis
6. Interpret the result
If you have any questions on this material, please ask your
instructor.
After finishing with this lecture, please go to the first
discussion for the week and engage
in a discussion with others in the class over the first couple of
24. days before reading the second
lecture.
BUS 308 Week 2 Lecture 2
Statistical Testing for Differences – Part 1
After reading this lecture, the student should know:
1. How statistical distributions are used in hypothesis testing.
2. How to interpret the F test (both options) produced by Excel
3. How to interpret the T-test produced by Excel
Overview
Lecture 1 introduced the logic of statistical testing using the
hypothesis testing procedure.
It also mentioned that we will be looking at two different tests
this week. The t-test is used to
determine if means differ, from either a standard or claim or
from another group. The F-test is
used to examine variance differences between groups.
This lecture starts by looking at statistical distributions – they
underline the entire
statistical testing approach. They are kind of like the
detective’s base belief that crimes are
committed for only a couple of reasons – money, vengeance, or
love. The statistical distribution
that underlies each test assumes that statistical measures (such
25. as the F value when comparing
variances and the t value when looking at means) follow a
particular pattern, and this can be used
to make decisions.
While the underlying distributions differ for the different tests
we will be looking at
throughout the course, they all have some basic similarities that
allow us to examine the t
distribution and extrapolate from it to interpreting results based
on other distributions.
Distributions. The basic logic for all statistical tests: If the null
hypothesis claim is
correct, then the distribution of the statistical outcome will be
distributed around a central value,
and larger and smaller values will be increasingly rare. At some
point (and we define this as our
alpha value), we can say that the likelihood of getting a
difference this large is extremely
unlikely and we will say that our results do not seem to come
from a population that matches the
claims of the null hypothesis.
Note that this logic has several key elements:
1. The test is based on an assumption that the null hypothesis is
correct. This gives us a
starting point, even if later proven wrong.
2. All sample results are turned into a statistic that matches the
test selected (for
example, the F statistic when using the F-test, or the t-statistic
when using the T-test.)
3. The calculated statistic is compared to a related statistical
26. distribution to see how
likely an outcome we have.
4. The larger the test statistic, the more unlikely it is that the
result matches or comes
from the population described by the null hypothesis claim.
We will demonstrate these ideas by looking at the questions
being asked in this week’s
homework. We will show results of the related Excel tests, and
discuss how to interpret the
output.
We need to remember that seeing different value (mean,
variance, etc.) from different
samples does not tell us that the population parameters we are
estimating are, in fact, different.
The one thing we know about sampling is that each sample will
be a bit different. They
generally provide a “close enough” estimate to the population
values of concern for decision and
action purposes. But, they are not an exact match. This
difference is examined by the use of the
statistical tests, which tell us how much importance we should
attach to observed differences.
Lecture Examples
The lectures for each week will also look at our class question
of whether or not males
and females are paid equally for equal work. These additional
analyses provide some different
clues on what the data is trying to tell us about company pay
practices.
27. While your analysis will focus directly on the salary that males
and females are being
paid, the lecture examples will use an alternate method of
examining pay practices. Many
compensation professionals often use a relative pay measure
called the “comparison-ratio,” or
compa-ratio, to examine pay patterns within the company.
Some background on this measure. Many companies use grades
to group jobs of equal
value to the company into groups that have a similar pay range
– the values that a company is
willing to pay employees for the job. (As strong as a performer
a mail room clerk is, they will
rarely be paid the same as the CEO.) Many companies will set
the middle of this range, the
midpoint, as the average salary that that market pays to hire
someone into the job. This is how
companies remain competitive in their hiring.
Now, compensation professionals will generally want to analyze
how the company is
paying employees relative to these market rates (as summarized
by the midpoint). One approach
is to divide each employee’s salary by its related midpoint. The
outcome is the compa-ratio
which is considered an alternate measure of pay that eliminates
the impact of different grades.
The compa-ratio reports pay as a ratio of the actual salary
divided by the salary grade’s midpoint.
The compa-ratio shows if an employee is being paid more than
the midpoint (measure’s
value > 1.0) or less than the midpoint (< 1.0). This measure
allows us to look at salary
28. dispersion within a company without focusing on the exact
dollar values. It allows a comparison
between what the company is paying and what the outside
market is paying (which most
company’s target as the midpoint of a salary range) for the jobs.
The compa-ratio shows if employees are paid above or below
the grade midpoint and it
can be used to see what the dispersion pattern of pay. Equal
pay would expect to see similar
distributions, variances and means, between males and females
in this measure.
The lecture examples will cover the same statistical tests as the
homework assignments
but will focus on the compa-ratio pay measure rather than
salary. As such, the results presented
each week should be included and/or factored into your weekly
conclusions on what the data has
told us about the answer to our question.
The first step in looking at whether males and females are paid
equally would be to look
at the average pay of each. Given our sample is a random
sample of the population of employees
(and, therefore considered to be representative of the
population), the average salaries or average
compa-ratios (they measure related but not identical measures
of pay) will give us an indication
of whether things are the same for each gender or not.
One issue in looking at averages is the variation within the
groups. If both groups have
29. the same or very similar variation across the salaries then we
test the averages for a difference
using one approach. If the group variances are significantly
different, we use a slightly
approach. So, the first step is to examine group variances. This
is done with the F-test.
F-test
As noted, the F-test is used to compare variances to determine if
the differences noted
could be from simple sampling error (also known as pure chance
alone) or if the differences are
large enough to be considered statistically different. The F
statistic is simply one group’s
variance divided by the other group’s variance. (When done by
hand, it is traditional to have the
largest variance in the numerator, but this is not critical when
Excel performs the test for us.) So,
if the variances are equal, then the result of one variance
divided by the other would equal 1.0 –
this is the center of the F distribution. How about a situation
where one variance equaled 4 and
the other equaled 5 (randomly picked numbers for this
example)? If we divided the larger by the
smaller, we would get 5/4 = 1.25 while if we divided the
smaller by the larger, we would get 0.8.
Note that these values are on each side of the center value of
1.00. This is what is meant by “two
tails” with the F-test – one tail of the distribution has values
less than 1.0 while the other has
values greater than 1.0.. Our value of F depends first on the
variances (of course) and then on
how we do the division. The likelihood of these two variances
coming from populations that
have the same variance does not depend upon which tail the
30. result is in, but rather how likely it is
to see a difference from 1.00. This is given to us by the F-test
p-value (probability value of
seeing a difference as large or larger than what we have if the
null hypothesis is true).
One new concept introduced with the F-test is the idea of
degrees of freedom (df). While
the technical explanation is somewhat tedious, we can
understand the concept with a simple
example. If we have 5 numbers, for example: 1, 2, 3, 4, and 5;
we also have a sum of them; in
this case 15. Now, assume we can change any of the numbers in
the data set with the only
requirement being that the total must remain the same. How
many of the numbers are we free to
change; or what is our degree of freedom in making changes?
In this case, we can change any 4
of the values, as soon as we do so we automatically get the fifth
value (whatever is needed to
make the sum equal to 15). Thus, to generalize, our df is the
count we have minus 1 (equaling n
-1). N-1 is the formula for the degrees of freedom for each
variable in the F-test. We will se this
idea in other statistical tests, each of which has its own formula
to calculate it. The nice thing is
that Excel will give us this outcome without our needing to
worry about it, and we rarely have to
actually use it in any of our work – but, it is technically part of
most statistical tests.
There are two versions of the F-test available for use. One is
located in the Data ribbon
under the Analysis block in the Data Analysis link and is called
31. F-Test Two-Sample for
Variances. The other is located in the Fx Statistical list (which
is duplicated in the Formulas
ribbon under the More Functions option and the Statistical list)
and is called simply F.test.
While both test variances, there is an important difference. The
F-Test Two Sample for
Variances option provides some additional summary statistics
(mean, variance, count) for each
sample, but only provides a one-tail test outcome. One-tail
results, whether with the F-Test or
the T-test are used to test a directional difference in variances,
when we want to know if one
variance is larger (or smaller) than the other. Since, in general
we are interested in the simpler
question of whether the variances are equal or not (without
regard to which is larger), when
using this form to test for equality or not, we need to double the
p-value to find the two t-tail p-
value we need for our decision on rejecting the null hypothesis.
On the other hand, the F.test found in Fx or Formulas returns
only the two-tail p-value;
enough for a decision on rejecting or failing to reject the null
hypothesis of no difference but
nothing else. Technically, this is the version we should use
when conducting our two-tail
questions in the homework, but (as noted) either can be used if
we remember to double the p-
value for the one -tail outcome.
Example: Testing for Variance Equality
As mentioned above, it is often beneficial to start with looking
at variance equality when
32. comparing groups. We need to start our analysis of equal pay
for equal work by seeing if there is
even an issue to be concerned with. So, we have selected our
random sample of 25 males and 25
females from our corporate population. (A couple of
assumptions; the company exists in only
one location, and all our employees in the sample are exempt
professions or managers with at
least a bachelor’s college degree.)
Our initial question is: Are the male and female compa-ratio
variances equal? (Note, if
they are, this would mean that the standard deviations of both
groups are the same.) As with all
statistical tests, we will be using our samples to make
judgements or inferences about the
population values. While the sample result values will differ,
this difference may not be large
enough to show that the population values are not the same.
Question 1. One of the first things of interest to detectives is if
the behavior of the
suspects differs from what they normally do. That is, who’s
behavior varies from the norm?
Relating this to our compa-ratio measures has us asking if the
compa-ratio variance for males
and females are equal within the population. (In the homework,
the question asks about salary
variances. The logic and approach for answering the salary-
based question is the same as shown
below.)
Variance equality is tested using the F-Test. There are two
versions of this test available
to us that we could use, and both will be shown below. Note
that equal standard deviations do
33. not automatically mean that the means are close, it just tells us
if the dispersion patterns are
similar. If similar, the means of each group can be considered
equally reflective of the data. The
following focuses on just setting up the data for and performing
the statistical test.
The following show only the output for the six hypothesis
testing steps. How the Excel
F-tests are set up is covered in Lecture 3 for this week.
Step 1: The question asked is whether the variances for males
and females are equal. The
hypothesis statements for an equality test are shown below.
Ho: Male compa-ratio variance = Female compa-ratio variance
Ha: Male compa-ratio variance =/= Female compa-ratio
variance
(Since the question asks about equality and not a directional
difference, this is a two-tail
test. The Null must contain the names of the two variables
involved (Male and Female),
the statistic being tested (variance), and the relationship sign
(=). The alternate provides
the opposite view so that between them all possible outcomes
are covered. We are only
concerned if the variances are equal, not whether one or the
other is larger (or smaller).)
Step 2: We state our decision-making criteria here. It is: Alpha
= 0.05 (This will be the
34. same for all statistical tests we perform in the class, and
therefore the same in all
hypothesis set-ups.)
Step 3: The test, test statistic, and the reason for selecting the
test are stated here. For this
example, we are using: F statistic and F-test for Variance. We
use these as they are
designed to test variance equality.
Step 4: Our decision-making rule is presented here: Reject the
null hypothesis if the p-
value is < alpha = 0.05.
(This step is also the same for every statistical test we will
perform; it says we will reject
the null hypothesis if the probability of getting a result as large
as what we see is less than
5% or a probability of 0.05.)
Note that these steps are set-up before we even look at the data.
While, we may have set
up the data columns, we should not have done any analysis yet.
These steps tell us how
we will make a decision from the results we get.
Step 5: Perform the analysis. This is the step where Excel
performs the analysis and
produces output tables. The setting up of each Excel test is
covered in Lecture 3, we are
primarily interested in how to interpret the results in this
lecture.
Here is a screenshot of the results for both versions of the F-
test. (Only one is needed for
the question.)
35. Step 6: Conclusions and Interpretation. This is where we
interpret what the data is trying
to tell us.
Before moving on to interpreting these results, let’s look at
what we have. The F-Test
Two-Sample for Variances output clearly has more information
than the F.TEST. We
have the labels identifying each group as well as the mean,
variance, and count
(Observations) for each group. The df, equaling the (sample
size -1), is shown as well as
the calculated F statistic (which equals the left group’s (or Male
in this case) variance
divided by the right group’s (Female) variance. Note, Excel
divides the variances in the
order that they were entered into the data entry box, for this
example the Males were
entered first.
The next two rows are critical for our decision making; but they
are incomplete. They
show the one-tail critical values used in decision making. The
P(F<=f) one-tail is the P-
value, or the probability of getting an F-value as large or larger
than we have if the null
hypothesis is true. However, it is only a one-tail outcome,
while we want a two-tail
outcome, since we only care about the variances being equal or
not, not which one is
36. larger. So, the result as presented cannot be used directly.
What we need to do will be
covered after we look at the F.TEST result.
The F.TEST gives less information, but it provides us with
exactly what we need; the
two-tail p-value. For our data set, we have a 40% (rounded)
chance of getting an F value
this large or larger purely by chance alone when we are looking
at a two-tail outcome.
Note that this value, 0.39766 is twice the one-tail value of
0.19883 from the F-Test table.
This will always be true, the two-tail probabilities will be twice
the one-tail values.
So, if we want to use the F-TEST Two-Sample for Variance
tool, we need to double the
p-value before making our step 6 decisions.
We are now ready to move on to what step 6 asks for. This step
has several parts.
• What is the p-value: 0.3977 (our compa-ratio example result).
This value equals
EITHER tht F.TEST outcome or 2 times the F-test result. (If,
the F-test p-value is
in cell K-15, you could enter =2*K15 to get the value desired).
• What is your decision: REJ or Not reject the null? (If our p-
value is < (less than)
0.05, we say REJ, if the p-value is > (larger than) 0.05, we say
NOT reject. This is
what our decision rule says to do.) Our answer is for compa-
ratio variances:
NOT Rej. This means we do not reject the claim made by the
37. null hypothesis
and accept it as the most likely description of the variances
within the population.
• Why? This line asks us to explain why we made the decision
we did. Our compa-
ratio response is: The p-value is > (greater than) 0.05, and the
decision rule is
reject if the p-value was < 0.05. (The answer here is simply
why, based on the
reasoning shown above, you chose your REJ or NOT Rej
choice.)
• What is your conclusion about the variances in the population
for the male and
female salaries? This part asks us to translate the statistical
decision into a clear
answer to the initial question (Are male and female compa-
values equal?) Our
response: We do not have enough evidence to say that the
variances differ in
the population. The variances are equal in the population. Had
we rejected
the null hypothesis, we would have said the population
variances differed.
Note that this question did not tell us anything about actual pay
differences between the
genders. It did tell us that both groups are dispersed in a
similar manner, and thus supported
some of the conclusions we drew from looking at the data last
week.
Examples: Testing for Mean Equality
While we test for variance equality with an F test, we use the T-
38. Test to test for mean
equality testing. The t-test also uses the degree of freedom (df)
value in providing us with our
probability result; but again, Excel does the work for us. The t
distribution is a bell-shaped curve
that is flatter and a bit more spread out than the normal curve
we discussed last week. The center
is located at 0 (zero) and the tails (the negative and positive
values) are symmetrical.
The t statistic for the testing of two means is basically: (Mean1
– Mean2)/standard error
estimate. (The standard error formula varies according to which
type of t-test we are
performing.) Note that the t will be either positive or negative
depending upon which mean is
larger. So, if we are interested in simply equal or not equal, it
does not matter if we have a
positive or negative t value, only the size of the difference
matters. As with the F-test, half of
our alpha goes in the positive tail and half goes in the negative
tail when making our equality
decision.
We have two questions about means this week.
Question 2. The second question for this week asks about
salary mean equality between males
and females. Again, the set up for this question is covered in
Lecture 3, we are concerned here
with the interpretation of the results. (Note, the comments
about each step made above apply to
this example as well, but they will not be repeated except for
specific information related to the t-
39. test outcome.) Specific differences from the variance example
of question 1 will be highlighted
with italics. Again, the results discussed with each step are
shown in Lecture 2.
Since the question asks if male and female compa-ratios
(salaries in the homework), are equal we
have a equal versus non-equal hypothesis pair.
Step 1: Ho: Male compa-ratio mean = Female compa-ratio mean
Ha: Male compa-ratio mean =/= Female compa-ratio mean
Step 2: The decision criteria is constant: Alpha = 0.05
Step 3: t statistic and t-test, assuming equal variances. We use
these as they are
designed to test mean equality, and we are assuming (and
according to the F-test
have) equal variances.
Step 4: Again, our decision rule is the same: Reject the null
hypothesis if the p-value is
< alpha = 0.05.
Step 5: Perform the analysis. Here is the screen shot for the
results, using the same data
as with Question 1.
As with the F-test output, the t-test starts with the test name, the
group names, and some
descriptive statistics. Line 4 start with a new result, the Pooled
Variance; this is a
40. weighted average of the sample variances since we are assuming
that the related
population variances are equal. The next line, hypothesized
Mean Difference, shows up
only if a value was entered in the data input box setting up the
test (discussed in Lecture
3).
Next comes our friend degrees of freedom, which equal the sum
of both sample sizes
minus 2 (or N1-1 + N2 -1). The calculated T value (similar to
the calculated F value in
the F test output) comes next. Note that since we have a
negative t Stat, it falls in the left
tail of the t distribution. This is important in one tail tests but
not in two tail tests.
Following the calculated T value come the one and two tail
decision points. The one tail
p-value is found in the P(T<==t) one-tail row followed by the T
critical one-tail value.
The two tail results follow.
Step 6: Conclusions and Interpretation. This step has several
parts.
What is the p-value? 0.571 (our rounded compa-ratio example
result).
(Since we again have a two-tail test, we use the P(T<=t) two-
tail result.)
What is your decision: REJ or Not reject the null? NOT Rej
(our compa-ratio result)
41. Why? The p-value (0.571) is > (greater than) 0.05. (The compa-
ratio result)
What is your conclusion about the means in the population for
the male and female
salaries? We do not have enough evidence to say that the means
differ in the
population. So, our conclusion is that the means are equal in
the population. (Our
compa-ratio result.)
Question 3
The third question for this week asks about salary differences
based on educational level
rather than gender. Since education is a legitimate reason to
pay someone more, it will be
helpful to see if a graduate degree results in a higher average
pay. Note that this question has a
directional focus (do employees with an advanced degree
(degree code = 1) have higher average
salaries?). This means we must develop a direction set of
hypothesis statements. We will use
the terms UnderG (for undergraduate degree code 0) and Grad
(for graduate degree code 1) in
these statements. Again, the results discussed with each step
are shown in Lecture 2.
Step 1: Ho: UnderG mean compa-ratio => Grad mean compa-
ratio
Ha: UnderG mean ratio < Grad mean compa-ratio
(Note the way the inequalities are set up; since the question is if
degree 1 salary
means >, the question becomes the alternate hypothesis as it
42. does not contain an =
claim. These can be written with the Grad mean listed first but
the arrow heads
must point to Grad showing an expectation that grad means are
larger.)
Step 2: Alpha = 0.05 (Our constant decision criterion)
Step 3: t statistic and t-test assuming equal variances. We use
these as they are
designed to test mean equality. (The variance equality
assumption is part of the
question set-up.)
Step 4: Reject the null hypothesis if the p-value is < alpha =
0.05. (Our constant
decision rule.)
Step 5: Perform the analysis. Here is a screen print for a T-test
on the question of
whether the graduate and undergraduate degree compa-ratios
means in the population are
equal or not. We are assuming equal variances, we are using
the T-Test Two-Sample
Assuming Equal Variances form.
t-Test: Two-Sample Assuming Equal Variances
UnderG Grad
Mean 1.05172 1.07324
Variance 0.00581 0.005999
Observations 25 25
Pooled Variance 0.005904
43. Hypothesized Mean
Difference 0
df 48
t Stat -0.99016
P(T<=t) one-tail 0.163529
t Critical one-tail 1.677224
P(T<=t) two-tail 0.327059
t Critical two-tail 2.010635
The table output is read exactly the same way as with the
question 2 table with the
exception that we are interested in the one-tail outcome, so we
use the (highlighted) one-
tail p-value row in our decision making.
Step 6: Conclusions and Interpretation. This step has several
parts.
• What is the p-value? 0.164(rounded) (our rounded compa-ratio
example result).
(Since we again have a one-tail test, we use the P(T<=t) one-tail
result.)
• Is the t value in the t-distribution tail indicated by the arrow in
the Ha claim? Yes.
The t-value is negative, and the Ha arrow points to the left (or
negative) tail
of the t distribution. (Since we only care about a difference in
one direction, the
result must be consistent with the desired direction. Only large
negative values
are of interest in this case/set-up, since our difference is
44. calculated by (UnderG –
Grad); large negative values show a larger Grad salary. If we
had said Grad <=
Underg in the Null, the alternate arrow would have pointed to
the right or positive
tail, and a positive t would have been needed.)
• What is your decision: REJ or Not reject the null? NOT Rej
(our compa-ratio
result)
• Why? The p-value is > (greater than) 0.05. So, the sign does
not matter in this
case, but it is in the correct or negative tail. (The compa-ratio
result)
• What is your conclusion about the impact of education on
average salaries? We
do not have enough information to suggest that graduate degree
holders have a
higher average salary than undergraduate degree holders. (Our
compa-ratio
result.)
Question 4
While the week 1 salary results suggest that males and females
are not paid the same, this
week’s compa-ratio tests still do not suggest any inequality.
Gender Compa-ratio variances and
means are not significantly different. A somewhat surprising
result was that graduate degree
holders did not have higher compa-ratios.
45. We still cannot answer our equal pay for equal work question;
however, as we have yet
developed a measure of pay for equal work. Compa-ratios do
remove the impact of grades, but
too many other work-related variables still need to be examined.
Summary
The F and t tests are used to determine if, based upon random
sample results, the
population parameters can reasonably be said to differ. The F-
test looks for differences in
population variances, while the t-test examines population mean
differences. Both tests are
performed as part of the hypothesis testing procedure and
always is done in step 5.
Differences in sample results can be transformed into statistical
distributions that allow us
to determine the probability or likelihood of getting a difference
as large or larger than we found.
It is this transformation that allows us to make our decisions
about the differences we see in the
results.
When either test is set-up using the Data | Analysis toolpak
function, these tests will
provide summary sample descriptive statistics for the mean,
variance, and count as well as the
calculated statistic, the critical value of the statistic, and the p-
value. When set-up using Excel’s
Fx or Formula functions, only the p-value is returned.
For both tests, if the appropriate p-value is less than the
specified alpha (always 0.05 in
this class), we reject the null hypothesis and say the alternate is
46. the more likely description of the
population.
We can test for a simple difference (called a two-tail test) where
it does not matter which
group has the larger value or we can use a directional test
(called a one-tail test) where we are
concerned about which variable is larger (or smaller). The null
and alternate hypothesis define
which difference we are looking for.
The t-test has three versions: equal variances, unequal
variances, and paired. The paired
test is used when we have two measures on each subject (such
as the salary and midpoint for
each employee). The F-test is used to help us decide if we need
to use the equal or unequal
variance form of the t-test.
The Analysis toolpak F test defaults to a one-tail test so we
need to double its p-value
when testing for simple variance differences. The Fx (or
Formula) F-test lets us select a one- or
two-tail outcome.
Please ask your instructor if you have any questions about this
material.
When you have finished with this lecture, please respond to
Discussion Thread 2 for this
week with your initial response and responses to others over a
couple of days.
47. BUS 308 Week 2 Lecture 3
Setting up the F and T tests in Excel
After reading this lecture, the student should know:
1. How to set up data lists for the F and T tests.
2. How to set-up and conduct the F test (both options) produced
by Excel
3. How to set-up and conduct the T-test produced by Excel
Overview
One of the nice characteristics of Excel is that setting up and
running most functions and
tests is done in a very similar fashion, only having specific test
related differences showing up in
the different functions and tests.
This lecture will cover setting up data ranges that will be used
for all of our statistical
functions. It will then move into setting up the F and T tests
specifically.
Setting up Data
While in the hypothesis testing procedure it was said to set up
steps 1 – 4 before even
looking at the data, we can set up the data columns to be used at
any time. The set-up is simple
and straightforward. But, we have a couple of questions to
answer before we set things up.
48. Since this week needs us to compare male and female outcomes
(and Degree outcomes in
Question 3), we need to decide how we want our data to look.
Sticking strictly with the gender
related data (you can do similar things with the degree data
when ready), we need to decide if we
want our key data (compa-ratios, salary, etc.) to be in a long
column or in two columns. An
example of both is shown in the screen shot below.
Notice that Column S contains all of the compa-ratio values (all
50 if we could see the
entire range) and that they are grouped by gender, with the first
25 rows being female values and
the last 25 rows being male values. The other way to display
the data values is to have them
listed in separate columns, such as shown in columns Q and R –
each having a label heading.
Start by looking at what variables the questions are asking for.
For week 2, we have
Questions 1 and 2 asking for the same variables – compa-ratio
and gender1, so we can use the
same location for both questions. Question 3 asks for a
different set of variables, compa-ratio
and degree, so we should set up a different area for that
question. Remember, it is best to
NEVER sort the data on the data tab. An error in sorting that
missed a column could mess up the
data set and make it unusable for other problems.
In either case, copy the entire data column of interest (for
example, compa-ratio,
49. Gender1, Degree, etc.) from the Data Tab to the weekly
worksheet. Highlight the entire data
range of interest including the label in row 1, then press Control
+ C at the same time. Go over
to the weekly work sheet and find a column to the right of the
work area (generally columns Q or
higher will be OK) and press Control + V at the same time.
Repeat this for all the variables you
need.
After pasting the variables, use the Sort function in the Data tab
to arrange them in
whatever order you want. You can do multiple sorts at the same
time with this function – for
example, you can sort the compa-ratios by gender1 first (to
group all male and female values
together) and then within each gender group sort the values
from high to low by adding a second
sort row.
If you would like, you can then create new columns of data by
copying and pasting
sections of the data range – for example, creating Male and
Female columns. The advantage to
this approach is that you can include the labels in the data entry
boxes and have the variable
labels included in the output tables as the examples showed in
Lecture 2.
The F-Test Set-up
In each question asking for an analysis of data using the
hypothesis testing process, step 5
50. requires that you place the results of a statistical test in a
certain cell. This, is mostly for the
convenience of the instructors reviewing your work but deciding
where to put the output is
required for every test you run.
The following shows the setting up of the hypothesis testing
steps and conducting of the
F-test to answer our question about the equality of male and
female compa-ratio variance. (Note:
again, you will perform these steps for salary variance in your
homework.)
Before even getting to the test itself, we have a couple of
questions to answer. Part a of
question 1 asks where the data range is for this question. We
always need to know where the
data is that we are using for tests, even if – as is true in this
case – the data is on the same work
sheet. So, list where the variables are listed, such as in the
range S1:T51 or Q1:Q26 as in the
examples above. Either would be an appropriate entry for the
data shown. One reason for this
question is to allow instructors to see if a data copy or sorting
error occurred if the data results
are not correct.
The second question simply asks for you to decide if a one- or
two-tail test is required for
the question being asked. This is to help prepare you for the
actual hypothesis testing steps.
Now, the set-up concerns move to Step 5: Conduct the test.
Note that a cell location is
given for you to place your outputs. In most cases, the tests we
want to perform are located in
51. the Analysis ToolPak option found in the Analysis tab on the far
right of the Data Ribbon. Left
Click on the Data label on the green ribbon at the top of an
Excel page, then click on the
Analysis Tab or on the Data Analysis tool listed. Once the Data
Analysis list is shown, scroll
down to your desired tool.
Below is a screen shot of locating the F-test Two-Sample for
Variance in the Data Analysis list.
The F.TEST option for question 1 is found in the Fx (or
Formulas) Statistical list. Here
is a screenshot of where the F.Test is found in the fx Statistical
list.
Either test can be used for this question. After highlighting the
desired test, just select
OK at the bottom and a data entry box will open. Both are
somewhat similar, so only the F-Test
Two Sample for Variances data entry will be shown below.
Here is a screenshot of the data entry box for the F-Test Two-
Sample for Variance. Note
that the compa-ratios have been copied over to columns headed
by labels of Male and Female.
This lets our test results show the label for each group. Also
note, that for this screenshot, the
results are placed next to the data columns (AA2), while in your
52. assignment K10 should be listed
in the Output Range box.
Note, always enter the variables in the order listed in the null
hypothesis statement; since
the male values were entered in the Variable 1 range, the
hypothesis statements should list the
male variable first. This makes interpreting the test results
easier.
Entering cell values into any box is fairly simple. You can
simply type the data range
into the box, using a : between the starting and ending cells.
You can place the cursor in a box,
left click, and then move the cursor to the top cell in the data
range (include labels if present),
hold down the left button and drag the cursor to the end of the
data range and release the left
button. Or, you can click on the symbol at the right which opens
a box, then enter the data by
either technique just mentioned and click on the icon at the
right.
After entering the data ranges, click on the Labels box if, and
only if, you have included
labels in the data input range. An alpha of 0.05 is automatically
selected but can be changed
simply by entering another value. Finally, go to the Output
Options and click on the desired
location – for this class use Output Range and then enter the
cell location into the box. Click on
Ok and you are done.
The process is pretty straightforward, but once in a while an
error occurs. The most
53. common is when someone does not include labels in the input
range but checks the labels box.
This is fairly easy to spot – the data tables will have a data
value listed as a label, and – at least
for the questions this week – will show a data count of 24 rather
than the correct count of 25 per
group. If this occurs, simply go back and reenter the data with
the labels. Excel will tell you that
you are about to overwrite existing data, and that is what you
want to do, so check OK.
The F.Test is even simpler to set-up. Going to Fx (or
Formulas), statistical list, and
selecting the F.Test will produce a data entry box that simply
asks for each data range – as with
the top entries in the F-Test shown above. Complete them in
the same way and select Ok. (Do
not include labels in these ranges.) The F.Test outcome shows
up in the cell your cursor was on
when you opened the Fx link.
VIDEO Link: Here is a video on the F-Test Two Sample for
Variances: https://screencast-o-
matic.com/watch/cbQuFRIwDX .
The T-Test Set-up
There are three versions of the T-Test done for us by Excel.
The first two are similar
except one version is done if the variances are equal and the
other if the variances are not equal.
(Now we see an important reason for performing the F-test
first.)
54. The third version of the T-test is for paired data, and is called
T-test Paired Two Sample
for Means. Paired data are two measures taken on the same
subject. Examples include a math
and English test score for each student, preference sores for
different drinks, and, in our data set
the salary and midpoint values. Note that paired data must be
measured in the same units, and be
from the same subjects. Students in the past have incorrectly
used the paired t-test on male and
female salaries. These are not paired, as the measures are taken
on different people and cannot
be paired together for analysis.
In many ways, setting up Excel’s T-tests, and virtually all the
functions we will study,
follow the same steps as we just went through:
1. Set up the data into distinct groups.
2. Select the test function from either the Fx or Analysis list
3. Input the data ranges and output ranges into the appropriate
entry boxes, checking
Labels if appropriate.
4. Clicking on OK to produce the output.
As with the F-test, the T-test has a couple of options depending
upon what you want your
output to look like. The Fx (or Formulas) option returns simply
the p-value for the selected
version of the test. The Data | Analysis selection provides
descriptive statistics that are useful for
additional analysis (some of which we will discuss later in the
course).
55. The t-test requires that we select between three versions, one
assuming equal variances
between the populations, one assuming unequal variances in the
populations, and one requiring
paired data (two measures on each element in the sample, such
as salary and midpoint for each
person in our data set.) All have the same data set-up approach,
so only one will be shown.
Setting up the data and test for question 2 about mean equality
is similar to what one for
the F-test question, and we can actually use the same data
columns as we used in question 1 on
variances. Again, after sorting the data into your comparison
groups (with labels as we did for
the F-test), select the appropriate test from either the Fx or
Analysis list. A completed T-test
Two-Sample Assuming Equal Variances input table is shown
below.
The input box looks a lot like the one we saw for the F-test, and
is completed in the same way.
Enter the data ranges in the same order you have them listed in
the hypothesis statements, check
the labels box if appropriate, and identify your output range top
left cell (this is given in the
homework problems for a consistent format for instructor
grading).
There is one input that differs and which we have not yet
discussed, Hypothesized Mean
Difference. For the most part, we do not use this. An example
of when we might want to is
56. when we have made a change and want to test its effectiveness.
For example, we might have a
pre- and post-test in a training course. In the original design,
the average improvement might be
10 points on the post-test. If we change the design of the
training, we would be interested not
only in showing a significant change between the two tests but
also a better change due to the
revision. In this case, the first 10-point difference in the tests
is a given, we want to know if the
additional score change is significant. So, we enter 10 in the
HMD box, and the analysis looks at
only the mean difference larger than 10, the marginal
improvement due to the design change.
The input for the Fx T.Test contains 4 boxes, and produces the
p-value in the cell the
cursor is in. The first two boxes are the data range for each
variable, and these should not have a
label included. The third box asks whether you have a one or
two tail test. The forth box asks
for the kind of test, paired, equal variance, or unequal variance.
Once we click OK for the T.test. we get a output, the p-value.
When we click OK on the
Analysis ToolPak function we get a more descriptive table;
much like the differences with the
two versions of the F.
There is no difference in setting up a Data Analysis test for a
one- or two-tail outcome,
these results are examined in the output, not in the input
57. screens.
Question 3
The only data entry difference for this question is the need to
copy, paste, and sort the
degree and gender1 variable columns. The rest of the set-up is
exactly the same as done for
either question 1 or question 2.
Special Case: The One-Sample T-test
Often, we may want to test the results of a sample against a
standard; for example, is the
weight of a production run of 8 ounces of canned pears actually
equal to the standard of 8.02 oz.?
(Note, most manufactures will put in slightly more than the
label says to avoid being
underweight which could result in a fine.)
Excel is not set up to perform this test, but we can “trick” it to
do this for us. In the one-
sample case, we need two pieces of information, the sample
values and our comparison standard.
Set these up as if they were any two-sample data sets, have our
sample values (for example, 25
female compa-ratios in one column) and our comparison value
in another. The comparison data
column will only contain a single value equal to our comparison
value. For example, we might
want to test if the average female compa-ratio was greater than
the compa-ratio midpoint of 1.00.
The null would be H0: female compa-ratio mean <= 1.00 while
the alternate would be Ha:
Female compa-ratio mean > 1.00. The Compa-ratio data column
would contain the Female
58. compa-ratios and the other column (named for convenience as
Ho Data) would contain only the
value of 1.00, our standard value.
While we will leave the math for any interested student to
perform, if we take the T-test
unequal variance formulas for both the t-value and the df value
and have a variance of 0 for one
variable, both will reduce to the one-sample t-test formula and
df value. Knowing this, we can
use the unequal variance version of the t-test to perform what is
essentially a one-sample test for
us.
The output of this test will show a mean of 1.0 and a variance of
0 for the Ho Data
(comparison) value, and the correct values for the Female
compa-ratio variable, including the p-
values.
Here is a video on setting up and using the t-test in Excel:
https://screencast-o-
matic.com/watch/cb6lYcImnn
Summary
Conducting an F or t test is fairly straightforward: set-up the
data, select the appropriate
test from the Analysis Toolpak or Fx/Formulas list, enter the
data into the set-up box, and
identify the cell you want the result placed in.
Setting up the data for either test is the same. Label two
columns with the name of each
59. group and list all the related measures (for example, all Male
salaries in a column named Male)
vertically under the label. Each test has a set-up box that will
ask for the ranges for each group.
When entering the data in the Analysis Toolpak function, be
sure to include each label.
Labels cannot be included in the Fx version of either test.
Please ask your instructor if you have any questions about this
material.
When you have finished with this lecture, please respond to
Discussion Thread 3 for this
week with your initial response and responses to others over a
couple of days before reading the
third lecture for the week.
https://screencast-o-matic.com/watch/cb6lYcImnn
https://screencast-o-matic.com/watch/cb6lYcImnn