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Available Information: The t test for independent means is used when we want to know whether there is a difference between populations. For instance, we may want to know if college men and women differ on some psychological characteristic. To assess this, we would need information gathered from a group of men and information gathered from a separate group of women. The means are considered \"independent\" when there is no overlap between groups; a person cannot be male and female at the same time. The t test for independent means is used only for tests of the sample means. Thus, our hypothesis tests whether the average difference between scores (M1 - M2) suggests that our students come from a population where there is no difference between men and women (?1 - ?2 = 0) or whether they come from different populations (e.g., college men/college women). From the given problem we have Means on Four Adjustment Scales for Adolescents from Structured versus Unstructured Homes. Also given their t value From the above information we have the p-value of t using Excel is as follows Social Maturity 106.82 113.94 t= From the above information we have the p-value of t using Excel is as follows Social Maturity 106.82 113.94 t= Social Maturity 106.82 113.94 t= Social Maturity 106.82 113.94 t= Solution Available Information: The t test for independent means is used when we want to know whether there is a difference between populations. For instance, we may want to know if college men and women differ on some psychological characteristic. To assess this, we would need information gathered from a group of men and information gathered from a separate group of women. The means are considered \"independent\" when there is no overlap between groups; a person cannot be male and female at the same time. The t test for independent means is used only for tests of the sample means. Thus, our hypothesis tests whether the average difference between scores (M1 - M2) suggests that our students come from a population where there is no difference between men and women (?1 - ?2 = 0) or whether they come from different populations (e.g., college men/college women). From the given problem we have Means on Four Adjustment Scales for Adolescents from Structured versus Unstructured Homes. Also given their t value From the above information we have the p-value of t using Excel is as follows Social Maturity 106.82 113.94 t= From the above information we have the p-value of t using Excel is as follows Social Maturity 106.82 113.94 t= Social Maturity 106.82 113.94 t= Social Maturity 106.82 113.94 t=.
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Week 6 DQ1. What is your research questionIs there a differen.docx
Week 6 DQ
1. What is your research question?
Is there a difference between the math utility of a male and a female?
2. What is the null hypothesis for your question?
Hn There is no difference in the math utility between male and female.
Alternative hypotheses can also be created in the case the null hypothesis is proven incorrect. Two alternative hypotheses are:
Ha1 Feales have a higher math utility.
Ha2 Males have a higher math utility.
3. What research design would align with this question?
According to Frankfort-Nachmias and Leon-Guerrero (2015) a descriptive research design would be best for this type of study.
4. What comparison of means test was used to answer the question (be sure to defend the use of the test using the article you found in your search)?
The independent-samples T test was used to analyze the means for this data.
5. What dependent variable was used and how is it measured?
The dependent variable is the student’s math utility. It is measured from -3.51 to 1.31(University high school longitudinal study dataset. (2009).
6. What independent variable is used and how is it measured?
Either male (1) of female (2) (University high school longitudinal study dataset. (2009).
7. If you found significance, what is the strength of the effect?
The significance was 0.0000. This is much better than the standard of .05 significance as outlined by Frankfort-Nachmias and Leon-Guerrero (2015).
8. Identify your research question and explain your results for a lay audience, what is the answer to your research question?
My research question was “Is there a difference between the math utility of a male and a female?” Based on the analysis of the means (or average) through testing using the independent-samples T test there was no measurable difference between the math utility of male or females. This leads us to accept the null hypothesis of “There is no difference in the math utility between male and female” as true.
Group Statistics
T1 Student's sex
N
Mean
Std. Deviation
Std. Error Mean
T1 Scale of student's mathematics utility
Male
9453
.0140
1.01962
.01049
Female
9349
-.0481
.97291
.01006
Independent Samples Test
Levene's Test for Equality of Variances
t-test for Equality of Means
F
Sig.
t
df
Sig. (2-tailed)
Mean Difference
Std. Error Difference
95% Confidence Interval of the Difference
Lower
Upper
T1 Scale of student's mathematics utility
Equal variances assumed
17.400
.000
4.276
18800
.000
.06216
.01454
.03367
.09066
Equal variances not assumed
4.277
18775.932
.000
.06216
.01453
.03367
.09065
University high school longitudinal study dataset. (2009).
References
Frankfort-Nachmias, C., & Leon-Guerrero, A. (2015). Social statistics for a diverse society (7th ed.). Thousand Oaks, CA: Sage Publications.
University high school longitudinal study dataset. (2009). Retrieved from class.waldenu.edu
The t Test for Related Samples
The t Test for Related Samples
Program Transcript
MAT.
Running head Organization behaviorOrganization behavior 2.docx
Running head: Organization behavior
Organization behavior 2
Organization behavior
Name:
Institution:
Course:
Date:
Organizational behavior analyzes the environment in different perspectives in order to come up with policies which make the organization convenient in its business operations. The organization must analyze various factors which affect it in order to frame the different policies. This means finding out the challenges or problems which an individual face in an organization and also the problems that groups faces in the organization. In this context, organization behavior is simply the way which an organization uses to solve the problems in its environment (Kreitner 2012). This discussion will involve Apple Inc.
One of the challenges facing Apple Inc. is managing human resources. Human resources in Apple Inc. are an invaluable asset and are always associated with the organization. Apple had experienced problems in managing its human resources. Some of the issues it experienced include failing to retain employees’ talents, not observing diverse recruitment to its fullest, non-performance among employees and employees not getting their benefits appropriately (O'Grady 2015). This went hand in hand with violation of rules governing employees, code of conduct and features which keep the value of team and organization high. The individuals’ and organization’s wellbeing depend highly on each other. This means that what people do while in the organization should reflect what is in their mind. The organizational value highly depends on social responsibility which the organization is portraying. They should put up policies for protecting the organizational environment. The issue has affected the behavior of Apple and the human resource management sorted them out (O'Grady 2015).
Managing human resources and employees ethics is a very important issue and a backbone of any organization. If managed well, the organization is likely to succeed easily. If not managed well, the issues will spoil the organization’s reputation completely and the organization may not undergo dissolution (Kreitner 2012).
References
Kreitner, Angelo Kinicki & Robert. 2012. Organization behavior. New York: Wiley.
O'Grady, Jason D. 2015. Apple Inc. Westport, Conn: Greenwood Press.
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GrStudents: Copy the Student Data file data values into this sheet to assist in doing your weekly assignments.The ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.The column labels in the table mean:ID – Employee sample number Salary – Salary in thousands Age – Age in yearsPerformance Rating - Appraisal rating (employee evaluation score)Service – Years of service (rounded)Gender – 0 = male, 1 = female Midpoi ...
Statistics: Chapter One
In this chapter you learn:
Definition of Statistics & Identify variables in a statistics.
Types of Statistics
Distinguish b/w quantitative & qualitative variables.
Determine the 4 levels of measurement.
Identify populations & samples.
Distinguish different types of Sampling
Do boys or girls have a larger growth spurt between the grades o.docx
Do boys or girls have a larger growth spurt between the grades of two and six?
By Nerlande Monfort
CMATH 6114
Comparative Study
The Plan
Ask a Question: Do boys or girls have a larger growth spurt between the grades of two and six?
Observational Unit: The boys and girls
Variable: Heights at grades 2 and 6
Collect Appropriate Data:
Since school is closed. I will be collecting data from students at our local Elementary and Middle School. The information is housed in the nurse’s office and is accessible through her. I will take the student information which is in alphabetical order and choose every third student until I gather heights for 20 girls and 20 boys. There are a total of 100 sixth graders in the school to choose from.
Analyze the Data: My data will be organized by grade and by gender. I will use a double line plot for boys and a separate double line plot for girls. I will find the mean of each plot to determine who had the larger growth spurt over those two grades.
Interpret the Results:
My expectations are to find that at this level girls will have the larger growth spurt. I am basing this simply on past observations. Boys seem to have their growth spurt in Middle school. Possible biases may include convenience sampling since my data is only being taken from one school. I may also have a measurement bias since these student’s heights were taken by hand and then copied onto a medical card. This information was then put into the computer.
ABSTRACT
In the study, the design applied to get the data will be simple random sampling without replacement.
The data will be analyzed and conclusions made by comparison of the students total heights in their genders at the two different grades.
Background
The previous research on this topic have reported the general growth but failed to count on the ages between these two grades. The applications of the previous research reports have shown reliance and believe in the general growth pattern of growth in young school going children though they have not specified on these two ages to explain whether it’s a just a believe that girls grow faster between these grades or its true from practical research findings.
Design of the research and data collection techniques
To ensure the collection of high-quality data, the data will be obtained from the identified population to get reliable and make conclusions. There will be a proper way of designing the sampling strategy used to ensure that potential boys and girls are picked who will be drawn from a representative sample of the intended population.
Design of the research and data collection techniques cont…
The samples will be obtained in a scientifically rigorous manner to ensure the findings will be generalizable to the intended population. The analysis of the data will not be homogenously because the selected survey compares two different grades and in different genders significantly.
Design of the research and data collect ...
The t Test for I.docx
The t Test for Independent Samples
The t Test for Independent Samples
Program Transcript
MATT JONES: The independent samples t-test is a comparison of means test
that compares two means across an independent categorical variable. Let's go to
SPSS to see how we conduct this procedure. In my independent sample t-test, I
would like to test for any possible differences between socioeconomic status and
respondent's race. In SPSS in my Variable View for the race variable, with the
label "What is respondent's race of first mention?," I can click on the values. The
reason I'm doing this is because an independent sample t-test can only test for
differences in two means at one time. Therefore, I can only choose two races for
this test. I can see in the race variable that there are a number of races present
within this variable.
For this test, I will choose respondents that self-identified as white, which is
denoted as 1, and respondents to self-identified as black or African American,
denoted as 2. I'll need to remember those for the next procedure. To perform this
procedure, Analyze, Compare Means, independent sample t-test. My test
variable is my dependent variable for the variable of which a mean is calculated
on. Therefore, it is that metric level variable, or any other variable where it makes
sense to calculate a mean. In this case, it's the socioeconomic status index of the
respondent.
Click on that. Move it over to the test variable. The grouping variable is my
categorical variable. And in this case, it is the respondent's race. I move
respondents race over to the grouping variable. Right away, behind the variable
name, you will see a set of parentheses with two question marks. This is SPSS's
prompt to tell me what races I should enter. SPSS knows that it can only
calculate two means and therefore is asking me to define groups. So I must click
on the define groups, group 1 and group 2. For group 1, I'm going to enter the
value number of 1, which were those respondents that self-identified as white.
For group 2, I'm going to enter 2, which for those respondents that self-identified
as black or African American. Click Continue, and once I click OK, I will receive
the output for my independent sample t-test.
The first piece of output I'm provided are the group statistics. I could look at the N
and get an idea of the sample that ended up in the test. There are 1,094 white
respondents and 191 black or African American respondents. I can see from the
descriptive statistics that the mean socioeconomic status index score for whites
is 50.99, and for black or African Americans it's 44.96. I'm also provided with
standard deviations for each mean, as well as the standard error of the mean.
Before I interpret the independent sample t-test, I must first examine the
Levene's test for equality of variances. An assumption of the independent
samples t-test is that variances are equal across the two groups. SPSS, by
...
Recommended
Week 6 DQ1. What is your research questionIs there a differen.docx
Week 6 DQ
1. What is your research question?
Is there a difference between the math utility of a male and a female?
2. What is the null hypothesis for your question?
Hn There is no difference in the math utility between male and female.
Alternative hypotheses can also be created in the case the null hypothesis is proven incorrect. Two alternative hypotheses are:
Ha1 Feales have a higher math utility.
Ha2 Males have a higher math utility.
3. What research design would align with this question?
According to Frankfort-Nachmias and Leon-Guerrero (2015) a descriptive research design would be best for this type of study.
4. What comparison of means test was used to answer the question (be sure to defend the use of the test using the article you found in your search)?
The independent-samples T test was used to analyze the means for this data.
5. What dependent variable was used and how is it measured?
The dependent variable is the student’s math utility. It is measured from -3.51 to 1.31(University high school longitudinal study dataset. (2009).
6. What independent variable is used and how is it measured?
Either male (1) of female (2) (University high school longitudinal study dataset. (2009).
7. If you found significance, what is the strength of the effect?
The significance was 0.0000. This is much better than the standard of .05 significance as outlined by Frankfort-Nachmias and Leon-Guerrero (2015).
8. Identify your research question and explain your results for a lay audience, what is the answer to your research question?
My research question was “Is there a difference between the math utility of a male and a female?” Based on the analysis of the means (or average) through testing using the independent-samples T test there was no measurable difference between the math utility of male or females. This leads us to accept the null hypothesis of “There is no difference in the math utility between male and female” as true.
Group Statistics
T1 Student's sex
N
Mean
Std. Deviation
Std. Error Mean
T1 Scale of student's mathematics utility
Male
9453
.0140
1.01962
.01049
Female
9349
-.0481
.97291
.01006
Independent Samples Test
Levene's Test for Equality of Variances
t-test for Equality of Means
F
Sig.
t
df
Sig. (2-tailed)
Mean Difference
Std. Error Difference
95% Confidence Interval of the Difference
Lower
Upper
T1 Scale of student's mathematics utility
Equal variances assumed
17.400
.000
4.276
18800
.000
.06216
.01454
.03367
.09066
Equal variances not assumed
4.277
18775.932
.000
.06216
.01453
.03367
.09065
University high school longitudinal study dataset. (2009).
References
Frankfort-Nachmias, C., & Leon-Guerrero, A. (2015). Social statistics for a diverse society (7th ed.). Thousand Oaks, CA: Sage Publications.
University high school longitudinal study dataset. (2009). Retrieved from class.waldenu.edu
The t Test for Related Samples
The t Test for Related Samples
Program Transcript
MAT.
Running head Organization behaviorOrganization behavior 2.docx
Running head: Organization behavior
Organization behavior 2
Organization behavior
Name:
Institution:
Course:
Date:
Organizational behavior analyzes the environment in different perspectives in order to come up with policies which make the organization convenient in its business operations. The organization must analyze various factors which affect it in order to frame the different policies. This means finding out the challenges or problems which an individual face in an organization and also the problems that groups faces in the organization. In this context, organization behavior is simply the way which an organization uses to solve the problems in its environment (Kreitner 2012). This discussion will involve Apple Inc.
One of the challenges facing Apple Inc. is managing human resources. Human resources in Apple Inc. are an invaluable asset and are always associated with the organization. Apple had experienced problems in managing its human resources. Some of the issues it experienced include failing to retain employees’ talents, not observing diverse recruitment to its fullest, non-performance among employees and employees not getting their benefits appropriately (O'Grady 2015). This went hand in hand with violation of rules governing employees, code of conduct and features which keep the value of team and organization high. The individuals’ and organization’s wellbeing depend highly on each other. This means that what people do while in the organization should reflect what is in their mind. The organizational value highly depends on social responsibility which the organization is portraying. They should put up policies for protecting the organizational environment. The issue has affected the behavior of Apple and the human resource management sorted them out (O'Grady 2015).
Managing human resources and employees ethics is a very important issue and a backbone of any organization. If managed well, the organization is likely to succeed easily. If not managed well, the issues will spoil the organization’s reputation completely and the organization may not undergo dissolution (Kreitner 2012).
References
Kreitner, Angelo Kinicki & Robert. 2012. Organization behavior. New York: Wiley.
O'Grady, Jason D. 2015. Apple Inc. Westport, Conn: Greenwood Press.
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GrStudents: Copy the Student Data file data values into this sheet to assist in doing your weekly assignments.The ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.The column labels in the table mean:ID – Employee sample number Salary – Salary in thousands Age – Age in yearsPerformance Rating - Appraisal rating (employee evaluation score)Service – Years of service (rounded)Gender – 0 = male, 1 = female Midpoi ...
Statistics: Chapter One
In this chapter you learn:
Definition of Statistics & Identify variables in a statistics.
Types of Statistics
Distinguish b/w quantitative & qualitative variables.
Determine the 4 levels of measurement.
Identify populations & samples.
Distinguish different types of Sampling
Do boys or girls have a larger growth spurt between the grades o.docx
Do boys or girls have a larger growth spurt between the grades of two and six?
By Nerlande Monfort
CMATH 6114
Comparative Study
The Plan
Ask a Question: Do boys or girls have a larger growth spurt between the grades of two and six?
Observational Unit: The boys and girls
Variable: Heights at grades 2 and 6
Collect Appropriate Data:
Since school is closed. I will be collecting data from students at our local Elementary and Middle School. The information is housed in the nurse’s office and is accessible through her. I will take the student information which is in alphabetical order and choose every third student until I gather heights for 20 girls and 20 boys. There are a total of 100 sixth graders in the school to choose from.
Analyze the Data: My data will be organized by grade and by gender. I will use a double line plot for boys and a separate double line plot for girls. I will find the mean of each plot to determine who had the larger growth spurt over those two grades.
Interpret the Results:
My expectations are to find that at this level girls will have the larger growth spurt. I am basing this simply on past observations. Boys seem to have their growth spurt in Middle school. Possible biases may include convenience sampling since my data is only being taken from one school. I may also have a measurement bias since these student’s heights were taken by hand and then copied onto a medical card. This information was then put into the computer.
ABSTRACT
In the study, the design applied to get the data will be simple random sampling without replacement.
The data will be analyzed and conclusions made by comparison of the students total heights in their genders at the two different grades.
Background
The previous research on this topic have reported the general growth but failed to count on the ages between these two grades. The applications of the previous research reports have shown reliance and believe in the general growth pattern of growth in young school going children though they have not specified on these two ages to explain whether it’s a just a believe that girls grow faster between these grades or its true from practical research findings.
Design of the research and data collection techniques
To ensure the collection of high-quality data, the data will be obtained from the identified population to get reliable and make conclusions. There will be a proper way of designing the sampling strategy used to ensure that potential boys and girls are picked who will be drawn from a representative sample of the intended population.
Design of the research and data collection techniques cont…
The samples will be obtained in a scientifically rigorous manner to ensure the findings will be generalizable to the intended population. The analysis of the data will not be homogenously because the selected survey compares two different grades and in different genders significantly.
Design of the research and data collect ...
The t Test for I.docx
The t Test for Independent Samples
The t Test for Independent Samples
Program Transcript
MATT JONES: The independent samples t-test is a comparison of means test
that compares two means across an independent categorical variable. Let's go to
SPSS to see how we conduct this procedure. In my independent sample t-test, I
would like to test for any possible differences between socioeconomic status and
respondent's race. In SPSS in my Variable View for the race variable, with the
label "What is respondent's race of first mention?," I can click on the values. The
reason I'm doing this is because an independent sample t-test can only test for
differences in two means at one time. Therefore, I can only choose two races for
this test. I can see in the race variable that there are a number of races present
within this variable.
For this test, I will choose respondents that self-identified as white, which is
denoted as 1, and respondents to self-identified as black or African American,
denoted as 2. I'll need to remember those for the next procedure. To perform this
procedure, Analyze, Compare Means, independent sample t-test. My test
variable is my dependent variable for the variable of which a mean is calculated
on. Therefore, it is that metric level variable, or any other variable where it makes
sense to calculate a mean. In this case, it's the socioeconomic status index of the
respondent.
Click on that. Move it over to the test variable. The grouping variable is my
categorical variable. And in this case, it is the respondent's race. I move
respondents race over to the grouping variable. Right away, behind the variable
name, you will see a set of parentheses with two question marks. This is SPSS's
prompt to tell me what races I should enter. SPSS knows that it can only
calculate two means and therefore is asking me to define groups. So I must click
on the define groups, group 1 and group 2. For group 1, I'm going to enter the
value number of 1, which were those respondents that self-identified as white.
For group 2, I'm going to enter 2, which for those respondents that self-identified
as black or African American. Click Continue, and once I click OK, I will receive
the output for my independent sample t-test.
The first piece of output I'm provided are the group statistics. I could look at the N
and get an idea of the sample that ended up in the test. There are 1,094 white
respondents and 191 black or African American respondents. I can see from the
descriptive statistics that the mean socioeconomic status index score for whites
is 50.99, and for black or African Americans it's 44.96. I'm also provided with
standard deviations for each mean, as well as the standard error of the mean.
Before I interpret the independent sample t-test, I must first examine the
Levene's test for equality of variances. An assumption of the independent
samples t-test is that variances are equal across the two groups. SPSS, by
...
Identify the Topic you Selected in the First Line of your Posting.docx
Identify the Topic you Selected in the First Line of your Posting
The topic I selected is nursing burnout. I attempted to select a t-test study for nursing burnout and EHR; however, I could not find a study covering these key words, so I settled for nursing burnout. The DNP project I wish to implement is to create an educational training for the new EHR start-up to decrease nurse stress and burnout.
Summarize the Study Discussed in your Selected Research Article and Provide a Complete APA Citation. Include in your Summary the Sample, Data Sources, Inferential Statistic Utilized, and Findings
Malliarou, M. M., Moustaka, E. C., & Konstantinidis, T. C. (2008). Burnout of nursing personnel in a regional university hospital. Health Science Journal, 2(3), 140-152.
The authors attempted through this study to determine whether burnout has various levels as correlated with demographic, education level, and professional indices. The study was conducted at a regional hospital with two questionnaires: a demographic questionnaire and the Maslach Burnout Inventory. Descriptive statistical analysis was completed with One Way Variance Analysis (ANOVA) and a t-test. The ANOVA was used to determine the statistical significance of the levels of burnout and the levels of demographic and education level data. The t-test was then used to compare the means of the two groups and the ANOVA was used to compare the means in multiple groups. The authors discovered that demographic and education level data did not have statistical significance for burnout prediction, and that the higher the level of perceived burnout, the more the nurse is likely to quit their position, leave the facility, or retire.
Evaluate the Purpose and Value of this Particular Research Study to the Topic.
The purpose of this research study was to determine if there are different levels of burnout and if burnout is correlated to demographic and educational level status. The t-test is a test that seeks to reject the null hypothesis and show that there is statistical significance between the variables (Laerd, 2013). The study question is an intriguing one! Attempting to look at different levels of burnout and if these levels can correlate to staff nurses demographics/education is one that has not been broached before. In this case, discovering that there is not a statistical significance is great news as well – this information can be used when constructing additional studies. This information can also be used when creating policies in facilities for preventing burnout, understanding what burn out is, what it is not, and factors that create burnout and those that do not.
Did Using Inferential Statistics Strengthen or Weaken the Study’s Application to Evidence-Based Practice?
In this case, the use of inferential statistics strengthened the study and the information gained from the study that can be generalized to the population at large. Being able to statistically calculate that there.
Using the data in the file named Ch. 11 Data Set 2, test the resea.docx
Using the data in the file named Ch. 11 Data Set 2, test the research hypothesis at the .05 level of significance that boys raise their hands in class more often than girls. Do this practice problem by hand using a calculator. What is your conclusion regarding the research hypothesis? Remember to first decide whether this is a one- or two-tailed test.
Using the same data set (Ch. 11 Data Set 2), test the research hypothesis at the .01 level of significance that there is a difference between boys and girls in the number of times they raise their hands in class. Do this practice problem by hand using a calculator. What is your conclusion regarding the research hypothesis? You used the same data for this problem as for Question 1, but you have a different hypothesis (one is directional and the other is nondirectional). How do the results differ and why?
Practice the following problems by hand just to see if you can get the numbers right. Using the following information, calculate the
t
test statistic.
Using the results you got from Question 3 and a level of significance at .05, what are the two-tailed critical values associated with each? Would the null hypothesis be rejected?
Using the data in the file named Ch. 11 Data Set 3, test the null hypothesis that urban and rural residents both have the same attitude toward gun control. Use IBM
®
SPSS
®
software to complete the analysis for this problem.
A public health researcher tested the hypothesis that providing new car buyers with child safety seats will also act as an incentive for parents to take other measures to protect their children (such as driving more safely, child-proofing the home, and so on). Dr. L counted all the occurrences of safe behaviors in the cars and homes of the parents who accepted the seats versus those who did not. The findings: a significant difference at the .013 level. Another researcher did exactly the same study; everything was the same—same type of sample, same outcome measures, same car seats, and so on. Dr. R’s results were marginally significant (recall Ch. 9) at the .051 level. Which result do you trust more and why?
In the following examples, indicate whether you would perform a
t
test of independent means or dependent means.
Two groups were exposed to different treatment levels for ankle sprains. Which treatment was most effective?
A researcher in nursing wanted to know if the recovery of patients was quicker when some received additional in-home care whereas when others received the standard amount.
A group of adolescent boys was offered interpersonal skills counseling and then tested in September and May to see if there was any impact on family harmony.
One group of adult men was given instructions in reducing their high blood pressure whereas another was not given any instructions.
One group of men was provided access to an exercise program and tested two times over a 6-month period for heart health.
For Ch. 12 Data Set 3, comput.
Anova and T-Test
The T-Test
Independent Sample T-Test
Paired Sample T-Test
One Sample T-Test
Test of Significance
The One-Way ANOVA
Post Hoc Comparisons
Contrasts
BUS308 – Week 1 Lecture 2 Describing Data Expected Out.docx
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.
BUS 308 Week 3 Lecture 1 Examining Differences - Continued.docx
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.
ScenarioStatistical significance is found in a study, but the ef.docx
Scenario
Statistical significance is found in a study, but the effect in reality is very small (i.e., there was a very minor difference in attitude between men and women). Were the results meaningful?
An independent samples t test was conducted to determine whether differences exist between men and women on cultural competency scores. The samples consisted of 663 women and 650 men taken from a convenience sample of public, private, and non-profit organizations. Each participant was administered an instrument that measured his or her current levels of cultural competency. The cultural competency score ranges from 0 to 10, with higher scores indicating higher levels of cultural competency. The descriptive statistics indicate women have higher levels of cultural competency ( M = 9.2, SD = 3.2) than men ( M = 8.9, SD = 2.1). The results were significant t (1311) = 2.0, p <.05, indicating that women are more culturally competent than are men. These results tell us that gender-specific interventions targeted toward men may assist in bolstering cultural competency.
Instructions: Critically evaluate the scenario you selected based upon the following points:
Critically evaluate the sample size.
Critically evaluate the statements for meaningfulness.
Critically evaluate the statements for statistical significance.
Based on your evaluation, provide an explanation of the implications for social change.
I attached the scenario to this message
add at least two references and citations
Number of Pages: 1 Page
Statistical significance is found in a study, but the effect in reality is very small (i.e., there was a very minor difference in attitude between men and women). Were the results meaningful?
According to The American Statistical Association's Statement on the Use of P Values (2016), Statistical reasoning should not be replaced by P-values. Once significance is noted and the effect is very small, the researcher should not conclude significance just because the P-value suggested so. Other tests should be performed which include construction of confidence intervals. If after several tests significance is still being seen, then the researcher can conclude significance otherwise, he/she should opt for other approaches for the research such as increasing the sample size.
Sample Sizes
Sample sizes used were 663 for women and 650 for men. The data was collected from three points that is public, private and non-profit organizations. Though the sample seems good enough, the entire population figure is not provided. Another problem is that, sample sizes from the three locations were not disclosed which probably means that the samples were all added up and used in the analysis. Sample sizes from each location should be carefully determined based on the population of each location. Since Convenience sampling is error bound, the samples shouldn’t be summed up and used in the analysis. Each location should be examined separately. Thi.
INFERENTIAL STATISTICS: AN INTRODUCTION
For instance, we use inferential statistics to try to infer from the sample data what the population might think. Or, we use inferential statistics to make judgments of the probability that an observed difference between groups is a dependable one or one that might have happened by chance in this study.
Assessment 3 ContextYou will review the theory, logic, and a.docx
Assessment 3 Context
You will review the theory, logic, and application of t-tests. The t-test is a basic inferential statistic often reported in psychological research. You will discover that t-tests, as well as analysis of variance (ANOVA), compare group means on some quantitative outcome variable.
Recall that null hypothesis tests are of two types: (1) differences between group means and (2) association between variables. In both cases there is a null hypothesis and an alternative hypothesis. In the group means test, the null hypothesis is that the two groups have equal means, and the alternative hypothesis is that the two groups do not have equal means. In the association between variables type of test, the null hypothesis is that the correlation coefficient between the two variables is zero, and the alternative hypothesis is that the correlation coefficient is not zero.
Notice in each case that the hypotheses are mutually exclusive. If the null is false, the alternative must be true. The purpose of null hypothesis statistical tests is generally to show that the null has a low probability of being true (the p value is less than .05) – low enough that the researcher can legitimately claim it is false. The reason this is done is to support the allegation that the alternative hypothesis is true.
In this context you will be studying the details of the first type of test. This is the test of difference between group means. In variations on this model, the two groups can actually be the same people under different conditions, or one of the groups may be assigned a fixed theoretical value. The main idea is that two mean values are being compared. The two groups each have an average score or mean on some variable. The null hypothesis is that the difference between the means is zero. The alternative hypothesis is that the difference between the means is not zero. Notice that if the null is false, the alternative must be true. It is first instructive to consider some of the details of groups. Means, and difference between them.
Null Hypothesis Significance Test
The most common forms of the Null Hypothesis Significance Test (NHST) are three types of t tests, and the test of significance of a correlation. The NHST also extends to more complex tests, such as ANOVA, which will be discussed separately. Below, the null hypothesis and the alternative hypothesis are given for each of the following tests. It would be a valuable use of your time to commit the information below to memory. Once this is done, then when we refer to the tests later, you will have some structure to make sense of the more detailed explanations.
1. One-sample t test: The question in this test is whether a single sample group mean is significantly different from some stated or fixed theoretical value - the fixed value is called a parameter.
· Null Hypothesis: The difference between the sample group mean and the fixed value is zero in the population.
· Alternative hypothesis: T.
A03710104
International Journal of Humanities and Social Science Invention (IJHSSI) is an international journal intended for professionals and researchers in all fields of Humanities and Social Science. IJHSSI publishes research articles and reviews within the whole field Humanities and Social Science, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
The Journal will bring together leading researchers, engineers and scientists in the domain of interest from around the world. Topics of interest for submission include, but are not limited to :
1) Current assets = Net working capital + Current liabilities = $1,0.pdf
1) Current assets = Net working capital + Current liabilities = $1,015 + $6,725 = $7,740
Current ratio = Current assets / Current liabilities = $7,740 / $6,725 = 1.15
2) Quick ratio = (Current assets - Inventory) / Current liabilities = ($7,740 - $1,135) / $6,725 =
0.98
Solution
1) Current assets = Net working capital + Current liabilities = $1,015 + $6,725 = $7,740
Current ratio = Current assets / Current liabilities = $7,740 / $6,725 = 1.15
2) Quick ratio = (Current assets - Inventory) / Current liabilities = ($7,740 - $1,135) / $6,725 =
0.98.
More Related Content
Similar to Available Information The t test for independent means is used wh.pdf
Identify the Topic you Selected in the First Line of your Posting.docx
Identify the Topic you Selected in the First Line of your Posting
The topic I selected is nursing burnout. I attempted to select a t-test study for nursing burnout and EHR; however, I could not find a study covering these key words, so I settled for nursing burnout. The DNP project I wish to implement is to create an educational training for the new EHR start-up to decrease nurse stress and burnout.
Summarize the Study Discussed in your Selected Research Article and Provide a Complete APA Citation. Include in your Summary the Sample, Data Sources, Inferential Statistic Utilized, and Findings
Malliarou, M. M., Moustaka, E. C., & Konstantinidis, T. C. (2008). Burnout of nursing personnel in a regional university hospital. Health Science Journal, 2(3), 140-152.
The authors attempted through this study to determine whether burnout has various levels as correlated with demographic, education level, and professional indices. The study was conducted at a regional hospital with two questionnaires: a demographic questionnaire and the Maslach Burnout Inventory. Descriptive statistical analysis was completed with One Way Variance Analysis (ANOVA) and a t-test. The ANOVA was used to determine the statistical significance of the levels of burnout and the levels of demographic and education level data. The t-test was then used to compare the means of the two groups and the ANOVA was used to compare the means in multiple groups. The authors discovered that demographic and education level data did not have statistical significance for burnout prediction, and that the higher the level of perceived burnout, the more the nurse is likely to quit their position, leave the facility, or retire.
Evaluate the Purpose and Value of this Particular Research Study to the Topic.
The purpose of this research study was to determine if there are different levels of burnout and if burnout is correlated to demographic and educational level status. The t-test is a test that seeks to reject the null hypothesis and show that there is statistical significance between the variables (Laerd, 2013). The study question is an intriguing one! Attempting to look at different levels of burnout and if these levels can correlate to staff nurses demographics/education is one that has not been broached before. In this case, discovering that there is not a statistical significance is great news as well – this information can be used when constructing additional studies. This information can also be used when creating policies in facilities for preventing burnout, understanding what burn out is, what it is not, and factors that create burnout and those that do not.
Did Using Inferential Statistics Strengthen or Weaken the Study’s Application to Evidence-Based Practice?
In this case, the use of inferential statistics strengthened the study and the information gained from the study that can be generalized to the population at large. Being able to statistically calculate that there.
Using the data in the file named Ch. 11 Data Set 2, test the resea.docx
Using the data in the file named Ch. 11 Data Set 2, test the research hypothesis at the .05 level of significance that boys raise their hands in class more often than girls. Do this practice problem by hand using a calculator. What is your conclusion regarding the research hypothesis? Remember to first decide whether this is a one- or two-tailed test.
Using the same data set (Ch. 11 Data Set 2), test the research hypothesis at the .01 level of significance that there is a difference between boys and girls in the number of times they raise their hands in class. Do this practice problem by hand using a calculator. What is your conclusion regarding the research hypothesis? You used the same data for this problem as for Question 1, but you have a different hypothesis (one is directional and the other is nondirectional). How do the results differ and why?
Practice the following problems by hand just to see if you can get the numbers right. Using the following information, calculate the
t
test statistic.
Using the results you got from Question 3 and a level of significance at .05, what are the two-tailed critical values associated with each? Would the null hypothesis be rejected?
Using the data in the file named Ch. 11 Data Set 3, test the null hypothesis that urban and rural residents both have the same attitude toward gun control. Use IBM
®
SPSS
®
software to complete the analysis for this problem.
A public health researcher tested the hypothesis that providing new car buyers with child safety seats will also act as an incentive for parents to take other measures to protect their children (such as driving more safely, child-proofing the home, and so on). Dr. L counted all the occurrences of safe behaviors in the cars and homes of the parents who accepted the seats versus those who did not. The findings: a significant difference at the .013 level. Another researcher did exactly the same study; everything was the same—same type of sample, same outcome measures, same car seats, and so on. Dr. R’s results were marginally significant (recall Ch. 9) at the .051 level. Which result do you trust more and why?
In the following examples, indicate whether you would perform a
t
test of independent means or dependent means.
Two groups were exposed to different treatment levels for ankle sprains. Which treatment was most effective?
A researcher in nursing wanted to know if the recovery of patients was quicker when some received additional in-home care whereas when others received the standard amount.
A group of adolescent boys was offered interpersonal skills counseling and then tested in September and May to see if there was any impact on family harmony.
One group of adult men was given instructions in reducing their high blood pressure whereas another was not given any instructions.
One group of men was provided access to an exercise program and tested two times over a 6-month period for heart health.
For Ch. 12 Data Set 3, comput.
Anova and T-Test
The T-Test
Independent Sample T-Test
Paired Sample T-Test
One Sample T-Test
Test of Significance
The One-Way ANOVA
Post Hoc Comparisons
Contrasts
BUS308 – Week 1 Lecture 2 Describing Data Expected Out.docx
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.
BUS 308 Week 3 Lecture 1 Examining Differences - Continued.docx
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.
ScenarioStatistical significance is found in a study, but the ef.docx
Scenario
Statistical significance is found in a study, but the effect in reality is very small (i.e., there was a very minor difference in attitude between men and women). Were the results meaningful?
An independent samples t test was conducted to determine whether differences exist between men and women on cultural competency scores. The samples consisted of 663 women and 650 men taken from a convenience sample of public, private, and non-profit organizations. Each participant was administered an instrument that measured his or her current levels of cultural competency. The cultural competency score ranges from 0 to 10, with higher scores indicating higher levels of cultural competency. The descriptive statistics indicate women have higher levels of cultural competency ( M = 9.2, SD = 3.2) than men ( M = 8.9, SD = 2.1). The results were significant t (1311) = 2.0, p <.05, indicating that women are more culturally competent than are men. These results tell us that gender-specific interventions targeted toward men may assist in bolstering cultural competency.
Instructions: Critically evaluate the scenario you selected based upon the following points:
Critically evaluate the sample size.
Critically evaluate the statements for meaningfulness.
Critically evaluate the statements for statistical significance.
Based on your evaluation, provide an explanation of the implications for social change.
I attached the scenario to this message
add at least two references and citations
Number of Pages: 1 Page
Statistical significance is found in a study, but the effect in reality is very small (i.e., there was a very minor difference in attitude between men and women). Were the results meaningful?
According to The American Statistical Association's Statement on the Use of P Values (2016), Statistical reasoning should not be replaced by P-values. Once significance is noted and the effect is very small, the researcher should not conclude significance just because the P-value suggested so. Other tests should be performed which include construction of confidence intervals. If after several tests significance is still being seen, then the researcher can conclude significance otherwise, he/she should opt for other approaches for the research such as increasing the sample size.
Sample Sizes
Sample sizes used were 663 for women and 650 for men. The data was collected from three points that is public, private and non-profit organizations. Though the sample seems good enough, the entire population figure is not provided. Another problem is that, sample sizes from the three locations were not disclosed which probably means that the samples were all added up and used in the analysis. Sample sizes from each location should be carefully determined based on the population of each location. Since Convenience sampling is error bound, the samples shouldn’t be summed up and used in the analysis. Each location should be examined separately. Thi.
INFERENTIAL STATISTICS: AN INTRODUCTION
For instance, we use inferential statistics to try to infer from the sample data what the population might think. Or, we use inferential statistics to make judgments of the probability that an observed difference between groups is a dependable one or one that might have happened by chance in this study.
Assessment 3 ContextYou will review the theory, logic, and a.docx
Assessment 3 Context
You will review the theory, logic, and application of t-tests. The t-test is a basic inferential statistic often reported in psychological research. You will discover that t-tests, as well as analysis of variance (ANOVA), compare group means on some quantitative outcome variable.
Recall that null hypothesis tests are of two types: (1) differences between group means and (2) association between variables. In both cases there is a null hypothesis and an alternative hypothesis. In the group means test, the null hypothesis is that the two groups have equal means, and the alternative hypothesis is that the two groups do not have equal means. In the association between variables type of test, the null hypothesis is that the correlation coefficient between the two variables is zero, and the alternative hypothesis is that the correlation coefficient is not zero.
Notice in each case that the hypotheses are mutually exclusive. If the null is false, the alternative must be true. The purpose of null hypothesis statistical tests is generally to show that the null has a low probability of being true (the p value is less than .05) – low enough that the researcher can legitimately claim it is false. The reason this is done is to support the allegation that the alternative hypothesis is true.
In this context you will be studying the details of the first type of test. This is the test of difference between group means. In variations on this model, the two groups can actually be the same people under different conditions, or one of the groups may be assigned a fixed theoretical value. The main idea is that two mean values are being compared. The two groups each have an average score or mean on some variable. The null hypothesis is that the difference between the means is zero. The alternative hypothesis is that the difference between the means is not zero. Notice that if the null is false, the alternative must be true. It is first instructive to consider some of the details of groups. Means, and difference between them.
Null Hypothesis Significance Test
The most common forms of the Null Hypothesis Significance Test (NHST) are three types of t tests, and the test of significance of a correlation. The NHST also extends to more complex tests, such as ANOVA, which will be discussed separately. Below, the null hypothesis and the alternative hypothesis are given for each of the following tests. It would be a valuable use of your time to commit the information below to memory. Once this is done, then when we refer to the tests later, you will have some structure to make sense of the more detailed explanations.
1. One-sample t test: The question in this test is whether a single sample group mean is significantly different from some stated or fixed theoretical value - the fixed value is called a parameter.
· Null Hypothesis: The difference between the sample group mean and the fixed value is zero in the population.
· Alternative hypothesis: T.
A03710104
International Journal of Humanities and Social Science Invention (IJHSSI) is an international journal intended for professionals and researchers in all fields of Humanities and Social Science. IJHSSI publishes research articles and reviews within the whole field Humanities and Social Science, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
The Journal will bring together leading researchers, engineers and scientists in the domain of interest from around the world. Topics of interest for submission include, but are not limited to :
Similar to Available Information The t test for independent means is used wh.pdf (20)
Identify the Topic you Selected in the First Line of your Posting.docx
Identify the Topic you Selected in the First Line of your Posting.docx
Using the data in the file named Ch. 11 Data Set 2, test the resea.docx
Using the data in the file named Ch. 11 Data Set 2, test the resea.docx
BUS308 – Week 1 Lecture 2 Describing Data Expected Out.docx
BUS308 – Week 1 Lecture 2 Describing Data Expected Out.docx
BUS 308 Week 3 Lecture 1 Examining Differences - Continued.docx
BUS 308 Week 3 Lecture 1 Examining Differences - Continued.docx
ScenarioStatistical significance is found in a study, but the ef.docx
ScenarioStatistical significance is found in a study, but the ef.docx
Assessment 3 ContextYou will review the theory, logic, and a.docx
Assessment 3 ContextYou will review the theory, logic, and a.docx
More from anonakitchen
1) Current assets = Net working capital + Current liabilities = $1,0.pdf
1) Current assets = Net working capital + Current liabilities = $1,015 + $6,725 = $7,740
Current ratio = Current assets / Current liabilities = $7,740 / $6,725 = 1.15
2) Quick ratio = (Current assets - Inventory) / Current liabilities = ($7,740 - $1,135) / $6,725 =
0.98
Solution
1) Current assets = Net working capital + Current liabilities = $1,015 + $6,725 = $7,740
Current ratio = Current assets / Current liabilities = $7,740 / $6,725 = 1.15
2) Quick ratio = (Current assets - Inventory) / Current liabilities = ($7,740 - $1,135) / $6,725 =
0.98.
(a). The posterior pole of the embryo of drosophila contains essenti.pdf
(a). The posterior pole of the embryo of drosophila contains essential substances for the fertility
of the fly. The cytoplasm contains pole plasm, which consists of particular materials called polar
granules and the pole cells are the ancestors to primordial germ cells.Pole plasm contains the
mRNA and proteins of the posterior group genes are oskar, nanos gene, tudor, vasa, and vlois.
These genes play an important role in localize germ cell determinants and germ line development
to localize nanos mRNA to the posterior.
1.Oskar is required to recruit the other genes to produce functional germ plasm.
2.Nanos is required to prevent somatic differentiation and mitosis and for the pole cells to
migrate to function as PGCs.
3.Gcls gene is required for pole cell formation.
(b). The proneural genes are important regulators of neurogenesis, which are involved in the
coordinating the acquisition of a generic neuronal fate and of specific subtype identities that are
appropriate for the location and time of neuronal generation.
(c). The selector gene, engrailed (en), is an important key determinant in boundary formation
among the anterior and posterior compartments. The wing imaginal disc expands, posterior, but
not anterior cells express engrailed and maintain expression state as they expand and form the
disc.
Solution
(a). The posterior pole of the embryo of drosophila contains essential substances for the fertility
of the fly. The cytoplasm contains pole plasm, which consists of particular materials called polar
granules and the pole cells are the ancestors to primordial germ cells.Pole plasm contains the
mRNA and proteins of the posterior group genes are oskar, nanos gene, tudor, vasa, and vlois.
These genes play an important role in localize germ cell determinants and germ line development
to localize nanos mRNA to the posterior.
1.Oskar is required to recruit the other genes to produce functional germ plasm.
2.Nanos is required to prevent somatic differentiation and mitosis and for the pole cells to
migrate to function as PGCs.
3.Gcls gene is required for pole cell formation.
(b). The proneural genes are important regulators of neurogenesis, which are involved in the
coordinating the acquisition of a generic neuronal fate and of specific subtype identities that are
appropriate for the location and time of neuronal generation.
(c). The selector gene, engrailed (en), is an important key determinant in boundary formation
among the anterior and posterior compartments. The wing imaginal disc expands, posterior, but
not anterior cells express engrailed and maintain expression state as they expand and form the
disc..
As per interest rate parity, Expected.pdf
As per interest rate parity, Expected Spot rate = Current Spot rate *
((1+p)/(1+b)) Where, = 1.1*((1+0.02)/(1+0.01)) p = Interest rate of quote currency = 2%
= 1.11 b = Interest rate of base currency = 1% $/€=1.1 means one euro is
exchanged for 1.1 dollar. Thus, expected spot rate in 30 days is E$/€=1.11
Solution
As per interest rate parity, Expected Spot rate = Current Spot rate *
((1+p)/(1+b)) Where, = 1.1*((1+0.02)/(1+0.01)) p = Interest rate of quote currency = 2%
= 1.11 b = Interest rate of base currency = 1% $/€=1.1 means one euro is
exchanged for 1.1 dollar. Thus, expected spot rate in 30 days is E$/€=1.11.
With the HNMR the two CH3s are chemically equiv.pdf
With the HNMR the two CH3\'s are chemically equivalent so they only give off one
peak for both. The peak will be a singlet indicating they have no other neighboring hydrogens
near them. I would expect it to look like
this...http://www.muhlenberg.edu/depts/chemistry/chem201woh/1Hacetone.html With the
CNMR I would expect to see a signal in the range of 205-220 because of a chemical shift for this
specific type of carbon ( R-C=O-R). The two R\'s in this instant are chemically equivalent when
in comes to environment so they will give off one signal for two carbons and since they are Sp3
they should be within the range of 0-50ppm and this signal will be much taller than the C=O
carbon.
Solution
With the HNMR the two CH3\'s are chemically equivalent so they only give off one
peak for both. The peak will be a singlet indicating they have no other neighboring hydrogens
near them. I would expect it to look like
this...http://www.muhlenberg.edu/depts/chemistry/chem201woh/1Hacetone.html With the
CNMR I would expect to see a signal in the range of 205-220 because of a chemical shift for this
specific type of carbon ( R-C=O-R). The two R\'s in this instant are chemically equivalent when
in comes to environment so they will give off one signal for two carbons and since they are Sp3
they should be within the range of 0-50ppm and this signal will be much taller than the C=O
carbon..
resonance structures 1 and 3 from the top reson.pdf
resonance structures: 1 and 3 from the top resonance hybride: the last one (at the
bottom)
Solution
resonance structures: 1 and 3 from the top resonance hybride: the last one (at the
bottom).
probability = (0.2-0.1)10 = 0.01 ANSWER .pdf
probability = (0.2-0.1)/10 = 0.01 ANSWER
Solution
probability = (0.2-0.1)/10 = 0.01 ANSWER.
It is methane since there is only 1 Carbon and it.pdf
It is methane since there is only 1 Carbon and it has only single bonds
Solution
It is methane since there is only 1 Carbon and it has only single bonds.
When finding E why did you divide the molar mass of copper by 2.pdf
When finding E why did you divide the molar mass of copper by 2?
Solution
When finding E why did you divide the molar mass of copper by 2?.
Thermochemistry is the study of the energy and heat associated with .pdf
Thermochemistry is the study of the energy and heat associated with chemical reactions. It
depends on two main laws: Laplace and Hess laws. It also studies the exchange or transfer of
heat energy between a system and its surroundings.
Please be more specific in your question for a more specific answer.
Solution
Thermochemistry is the study of the energy and heat associated with chemical reactions. It
depends on two main laws: Laplace and Hess laws. It also studies the exchange or transfer of
heat energy between a system and its surroundings.
Please be more specific in your question for a more specific answer..
Vibration and WavesT = 2pisqrt(mk)v = lambdafp = A sin(kx.pdf
Vibration and Waves
T = 2pi*sqrt(m/k)
v = lambda*f
p = A sin(kx-wt) w=2*pi*f (pressure in travelling sound wave), k = 2pi/lambda, A = amplitude,
x = displacement.
1. Periodic motion: used to describe a vibration or an oscillation that repeats itself over the same
path.. Displacement: The position of an object. Amplitude: The maximum displacement. Period:
The time in seconds required for one complete cycle. Frequency: The number of cycles, per
second. Simple Harmonic Motion: Sinusoidal motion of a single frequency.
2. Total E = 1/2kA^2. = 1/2kx^2+1/2mv^2. EPE = 1/2kx^2.
3. The equation for the period of a spring is T = 2pi*sqrt(m/k). For example, how long does it
take for a spring with a hanging mass of 5 kg and spring constant 10N/m to exhibit one complete
cycle? T = 2pi*sqrt(5/10). T = 2pi*sqrt(1/2).
4. Since a period is the time it takes for one complete cycle in seconds, frequency is the inverse
of the period. f = 1/T. T = 1/f.
5. For describing the motion generated SHM, you can use x = A sin 2pi*f*t. This is if the motion
starts at equilibrium point. If it starts at the crest/trough of its motion, then use x = A cos 2pi*f*t.
6. For describing the motion generated SHM, you can use x = A sin 2pi*f*t. This is if the motion
starts at equilibrium point. If it starts at the crest/trough of its motion, then use x = A cos 2pi*f*t.
7. The maximum velocity of a particle on SHM can be found by v = 2pi*A*f. Now, for any other
velocity at a point in time, it can be found by v = v0(sqrt(1-x^2/A^2)).
8. The equation for the period of a pendulum is T = 2*pi*sqrt(L/g). L = length of the pendulum.
This only works if its amplitude is small and friction can be ignored.
9. Dampened harmonic motion is the gradual decrease of maximum displacement. This is caused
by the presence of frictional forces. The energy is eventually all transformed to heat.
10. Forced vibration is due to an external source, which applies its own frequency to the spring.
The natural frequency of the spring is actually 1/2pi * sqrt(k/m), which is what it will actually
vibrate at without external force.
11. Amplitude denotes the distance between the equilibrium and the crest or trough of the wave.
Wavelength is the distance from one point of the wave to the corresponding point in the next
cycle. (crest-crest). This can be found be lambda = v/f. Wave velocity is the velocity at which
wave crests move. V = lambda*f. Node is referring to the point of destructive interference.
Antinode is referring to the point of constructive interference.
12. The relationship between velocity, wavelength, and frequency is v = lambda*f, where
lambda is the wavelength and f is the frequency, v is the wave velocity.
13. A transverse wave refers to a wave that causes the particles of the medium it travels in to
move a direction perpendicular to the motion of the wave itself. On the other hand, in a
longitudinal wave, the vibration of the particles of the medium is along the same direction as the
motion of the wa.
The following steps illustrate how to write a simple Level-2 MATLAB .pdf
The following steps illustrate how to write a simple Level-2 MATLAB S-function. When
applicable, the steps include examples from the S-function demo msfcn_unit_delay.m used in the
model msfcndemo_sfundsc2.mdl. All lines of code use the variable name block for the S-
function run-time object.
Step 1
Copy the Level-2 MATLAB S-function template msfuntmpl.m to your working folder. If you
change the file name when you copy the file, change the function name in the function line to the
same name.
Step 2
Modify the setup method to initialize the S-function’s attributes. For this example:
Set the run-time object’s NumInputPorts and NumOutputPorts properties to 1 in order to
initialize one input port and one output port.
Invoke the run-time object’s SetPreCompInpPortInfoToDynamic and
SetPreCompOutPortInfoToDynamic methods to indicate that the input and output ports inherit
their compiled properties (dimensions, data type, complexity, and sampling mode) from the
model.
Set the DirectFeedthrough property of the run-time object’s InputPort to false in order to indicate
the input port does not have direct feedthrough.
Retain the default values for all other input and output port properties that are set in your copy of
the template file. The values set for the Dimensions, DatatypeID, and Complexity properties
override the values inherited using the SetPreCompInpPortInfoToDynamic and
SetPreCompOutPortInfoToDynamic methods.
Set the run-time object’s NumDialogPrms property to 1 in order to initialize one S-function
dialog parameter.
Specify that the S-function has an inherited sample time by setting the value of the runtime
object’s SampleTimes property to [-1 0].
Call the run-time object’s RegBlockMethod method to register the following four callback
methods used in this S-function.
Remove any other registered callback methods from your copy of the template file. In the calls to
RegBlockMethod, the first input argument is the name of the S-function API method and the
second input argument is the function handle to the associated local function in the MATLAB S-
function.
The following setup method from msfcn_unit_delay.m performs the previous list of steps:
function setup(block)
%% Register a single dialog parameter
block.NumDialogPrms = 1;
%% Register number of input and output ports
block.NumInputPorts = 1;
block.NumOutputPorts = 1
%% Setup functional port properties to dynamically
%% inherited.
block.SetPreCompInpPortInfoToDynamic;
block.SetPreCompOutPortInfoToDynamic;
%% Hard-code certain port properties
block.InputPort(1).Dimensions = 1;
block.InputPort(1).DirectFeedthrough = false;
block.OutputPort(1).Dimensions = 1;
%% Set block sample time to inherited
block.SampleTimes = [-1 0];
%% Register methods
block.RegBlockMethod(\'PostPropagationSetup\',@DoPostPropSetup);
block.RegBlockMethod(\'InitializeConditions\',@InitConditions);
block.RegBlockMethod(\'Outputs\', @Output);
block.RegBlockMethod(\'Update\', @Update);
If your S-function needs continuous stat.
CO + 2H2 =CH3OH moles of CO = 2528 moles of H2 =.pdf
CO + 2H2 =CH3OH moles of CO = 25/28 moles of H2 = 6/2 =3 CO is limiting
reactant
Solution
CO + 2H2 =CH3OH moles of CO = 25/28 moles of H2 = 6/2 =3 CO is limiting
reactant.
Solution for Q-23a) Pay interest on bank reserve ( The main tool a.pdf
Solution
for Q-23
a) Pay interest on bank reserve ( The main tool availble with FED is to maintain the
reserve/margin requirement and accordingly fixed interest rate).
Please find the required program below with output#include stdio.pdf
Please find the required program below with output:
#include
void transposeMatrix(int arr[][5], int trans[][4]){
for (int j = 0; j < 5; ++j)
{
for (int i = 0; i < 4; ++i)
{
trans[j][i] = arr[i][j]; // just reversing the address i,j to j,i so that the transpose will be made
}
}
}
void main()
{
int array[4][5], trans[5][4];
int i, j;
printf(\"Enter the elements of matrix\ \");
for (i = 0; i < 4; ++i)
{
for (j = 0; j < 5; ++j)
{
scanf(\"%d\", &array[i][j]); // reading the 4x5 matrix array
}
}
printf(\"The matrix is \ \"); //just printing the user input array
for (i = 0; i < 4; ++i)
{
for (j = 0; j < 5; ++j)
{
printf(\" %d\", array[i][j]);
}
printf(\"\ \");
}
transposeMatrix(array,trans); //calling the transposeMatrix method, so that the transpose of the
matrix array will be stored in trans
printf(\"Transpose of matrix is \ \"); //just printing the returned transpose matrix trans
for (i = 0; i < 5; ++i)
{
for (j = 0; j < 4; ++j)
{
printf(\" %d\", trans[i][j]);
}
printf(\"\ \");
}
}
---------------------------------------------------------------------------------
OUTPUT:
Enter the elements of
matrix
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
The matrix
is
1 2 3 4
5
6 7 8 9
10
11 12 13 14 15
16 17 18 19
20
Transpose of matrix
is
1 6 11
16
2 7 12
17
3 8 13
18
4 9 14
19
5 10 15 20
Solution
Please find the required program below with output:
#include
void transposeMatrix(int arr[][5], int trans[][4]){
for (int j = 0; j < 5; ++j)
{
for (int i = 0; i < 4; ++i)
{
trans[j][i] = arr[i][j]; // just reversing the address i,j to j,i so that the transpose will be made
}
}
}
void main()
{
int array[4][5], trans[5][4];
int i, j;
printf(\"Enter the elements of matrix\ \");
for (i = 0; i < 4; ++i)
{
for (j = 0; j < 5; ++j)
{
scanf(\"%d\", &array[i][j]); // reading the 4x5 matrix array
}
}
printf(\"The matrix is \ \"); //just printing the user input array
for (i = 0; i < 4; ++i)
{
for (j = 0; j < 5; ++j)
{
printf(\" %d\", array[i][j]);
}
printf(\"\ \");
}
transposeMatrix(array,trans); //calling the transposeMatrix method, so that the transpose of the
matrix array will be stored in trans
printf(\"Transpose of matrix is \ \"); //just printing the returned transpose matrix trans
for (i = 0; i < 5; ++i)
{
for (j = 0; j < 4; ++j)
{
printf(\" %d\", trans[i][j]);
}
printf(\"\ \");
}
}
---------------------------------------------------------------------------------
OUTPUT:
Enter the elements of
matrix
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
The matrix
is
1 2 3 4
5
6 7 8 9
10
11 12 13 14 15
16 17 18 19
20
Transpose of matrix
is
1 6 11
16
2 7 12
17
3 8 13
18
4 9 14
19
5 10 15 20.
Note 2 Types of variables dataSolutionNote 2 Typ.pdf
Note:
2 Types of variables / data
Solution
Note:
2 Types of variables / data.
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TESDA TM1 REVIEWER FOR NATIONAL ASSESSMENT WRITTEN AND ORAL QUESTIONS WITH A...
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"Protectable subject matters, Protection in biotechnology, Protection of othe...
1.4 modern child centered education - mahatma gandhi-2.pptx
1.4 modern child centered education - mahatma gandhi-2.pptx
Instructions for Submissions thorugh G- Classroom.pptx
Instructions for Submissions thorugh G- Classroom.pptx
Available Information The t test for independent means is used wh.pdf
- 1. Available Information: The t test for independent means is used when we want to know whether there is a difference between populations. For instance, we may want to know if college men and women differ on some psychological characteristic. To assess this, we would need information gathered from a group of men and information gathered from a separate group of women. The means are considered "independent" when there is no overlap between groups; a person cannot be male and female at the same time. The t test for independent means is used only for tests of the sample means. Thus, our hypothesis tests whether the average difference between scores (M1 - M2) suggests that our students come from a population where there is no difference between men and women (?1 - ?2 = 0) or whether they come from different populations (e.g., college men/college women). From the given problem we have Means on Four Adjustment Scales for Adolescents from Structured versus Unstructured Homes. Also given their t value From the above information we have the p-value of t using Excel is as follows Social Maturity 106.82 113.94 t= From the above information we have the p-value of t using Excel is as follows Social Maturity 106.82 113.94 t= Social Maturity 106.82 113.94 t= Social Maturity 106.82 113.94 t= Solution Available Information: The t test for independent means is used when we want to know whether there is a difference between populations. For instance, we may want to know if college men and women differ on some psychological characteristic. To assess this, we would need information gathered from a group of men and information gathered from a separate group of women. The means are considered "independent" when there is no overlap between groups; a person cannot be male and female at the same time. The t test for independent means is used only for tests of the sample means. Thus, our
- 2. hypothesis tests whether the average difference between scores (M1 - M2) suggests that our students come from a population where there is no difference between men and women (?1 - ?2 = 0) or whether they come from different populations (e.g., college men/college women). From the given problem we have Means on Four Adjustment Scales for Adolescents from Structured versus Unstructured Homes. Also given their t value From the above information we have the p-value of t using Excel is as follows Social Maturity 106.82 113.94 t= From the above information we have the p-value of t using Excel is as follows Social Maturity 106.82 113.94 t= Social Maturity 106.82 113.94 t= Social Maturity 106.82 113.94 t=