ScoreWeek 3ANOVA and Paired T-test.docx
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This document appears to contain the results of statistical analyses conducted to investigate gender pay equality at a company. Several analyses were reported, including ANOVA tests to examine differences in average performance ratings and salaries between grades/levels, and a t-test to compare average employee salaries to average salary midpoints. Key findings based on p-values and effect sizes were that there were no statistically significant differences found between genders or degrees for various factors like compensation and performance. Human: Thank you for the summary. Now summarize the following document in 3 sentences or less: [DOCUMENT]: A new study published today in the journal Science finds compelling evidence that climate change is severely impacting the world's oceans. The study analyzed decades of
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can i get a quote. i got one from barzzy but she never replied.docx
can i get a quote. i got one from barzzy but she never replied
At this point we know the following about male and female salaries.
a.
Male and female overall average salaries are not equal in the population.
b.
Male and female overall average compas are equal in the population, but males are a bit more spread out.
c.
The male and female salary range are almost the same, as is their age and service.
d.
Average performance ratings per gender are equal.
Let's look at some other factors that might influence pay - education(degree) and performance ratings.
1
Last week, we found that average performance ratings do not differ between males and females in the population.
Now we need to see if they differ among the grades. Is the average performace rating the same for all grades?
(Assume variances are equal across the grades for this ANOVA.)
You can use these columns to place grade Perf Ratings if desired.
A
B
C
D
E
F
Null Hypothesis:
Alt. Hypothesis:
Place
B17 in Outcome range box.
Interpretation:
What is the p-value:
Is P-value < 0.05?
Do we REJ or Not reject the null?
If
the null hypothesis was rejected, what is the effect size value (eta squared):
Meaning of effect size measure:
What does that decision mean in terms of our equal pay question:
2
While it appears that average salaries per each grade differ, we need to test this assumption.
Is the average salary the same for each of the grade levels? (Assume equal variance, and use the analysis toolpak function ANOVA.)
Use the input table to the right to list salaries under each grade level.
Null Hypothesis:
If desired, place salaries per grade in these columns
Alt. Hypothesis:
A
B
C
D
E
F
Place
B55 in Outcome range box.
What is the p-value:
Is P-value < 0.05?
Do you reject or not reject the null hypothesis:
If
the null hypothesis was rejected, what is the effect size value (eta squared):
Meaning of effect size measure:
Interpretation:
3
The table and analysis below demonstrate a 2-way ANOVA with replication.
Please interpret the results.
BA
MA
Ho: Average compas by gender are equal
Male
1.017
1.157
Ha: Ave.
Ashford 4 - Week 3 - Discussion 1Your initial discussio.docx
Ashford 4: - Week 3 - Discussion 1
Your initial discussion thread is due on Day 3 (Thursday) and you have until Day 7 (Monday) to respond to your classmates. Your grade will reflect both the quality of your initial post and the depth of your responses. Reference the Discussion Forum Grading Rubric for guidance on how your discussion will be evaluated.
ANOVA
In many ways, comparing multiple sample means is simply an extension of what we covered last week. Just as we had 3 versions of the t-test (1 sample, 2 sample (with and without equal variance), and paired; we have several versions of ANOVA – single factor, factorial (called 2-factor with replication in Excel), and within-subjects (2-factor without replication in Excel). What examples (professional, personal, social) can you provide on when we might use each type? What would be the appropriate hypotheses statements for each example?
Guided Response: Review several of your classmates’ posts. Respond to at least two classmates by commenting on why you agree or disagree with the statistical test that your peers have described as appropriate in this scenario.
Ashford 4: - Week 3 - Discussion 2
Your initial discussion thread is due on Day 3 (Thursday) and you have until Day 7 (Monday) to respond to your classmates. Your grade will reflect both the quality of your initial post and the depth of your responses. Reference the Discussion Forum Grading Rubric for guidance on how your discussion will be evaluated.
Effect Size
Several statistical tests have a way to measure effect size. What is this, and when might you want to use it in looking at results from these tests on job related data?
Ashford 4: - Week 3 - Assignment
Problem Set Week Three
Complete the problems included in the resources below and submit your work in an Excel document. Be sure to show all of your work and clearly label all calculations.
All statistical calculations will use the Employee Salary Data Set and the Week 3 assignment sheet.
Carefully review the Grading Rubric for the criteria that will be used to evaluate your assignment.
See comments at the right of the data set.
ID
Salary
Compa
Midpoint
Age
Performance Rating
Service
Gender
Raise
Degree
Gender1
Grade
8
23
1.000
23
32
90
9
1
5.8
0
F
A
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)?
10
22
0.956
23
30
80
7
1
4.7
0
F
A
Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.
11
23
1.000
23
41
100
19
1
4.8
0
F
A
14
24
1.043
23
32
90
12
1
6
0
F
A
The column labels in the table mean:
15
24
1.043
23
32
80
8
1
4.9
0
F
A
ID – Employee sample number
Salary – Salary in thousands
23
23
1.000
23
36
65
6
1
3.3
1
F
A
Age – Age in years
Performance Rating – Appraisal rating (Employee evaluation score)
26
24
1.043
23
22
95
2
1
6.2
1
F
A
Service – Years of service (rounded)
Gender: ...
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseD.docx
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 Midpoint – salary grade midpoint Raise – percent of last raiseGrade – job/pay gradeDegree (0= BS\BA 1 = MS)Gender1 (Male or Female)Compa - salary divided by midpoint
Week 1Week 1.Measurement and Description - chapters 1 and 2The goal this week is to gain an understanding of our data set - what kind of data we are looking at, some descriptive measurse, and a look at how the data is distributed (shape).1Measurement issues. Data, even numerically coded variables, can be one of 4 levels - nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, asthis impact the kind of analysis we can do with the data. For example, descriptive statistics such as means can only be done on interval or ratio level data.Please list under each label, the variables in our data set that belong in each group.NominalOrdinalIntervalRatiob.For each variable that you did not call ratio, why did you make that decision?2The first step in analyzing data sets is to find some summary descriptive statistics for key variables.For salary, compa, age, performance rating, and service; find the mean, standard deviation, and range for 3 groups: overall sample, Females, and Males.You can use either the Data Analysis Descriptive Statistics tool or the Fx =average and =stdev functions. (the range must be found using the difference between the =max and =min functions with Fx) functions.Note: Place data to the right, if you use Descriptive statistics, place that to the right as well.Some of the values are completed for you - please finish the table.SalaryCompaAgePerf. Rat.ServiceOverallMean35.785.99.0Standard Deviation8.251311.41475.7177Note - data is a sample from the larger company populationRange304521FemaleMean32.584.27.9Standard Deviation6.913.64.9Range26.045.018.0MaleMean38.987.610.0Standard Deviation8.48.76.4Range28.030.021.03What is the probability for a:Probabilitya. Randomly selected person being a male in grade E?b. Randomly selected male being in grade E? Note part b is the same as given a male, what is probabilty of being in grade E?c. Why are the results different?4A key issue in comparing data sets is to see if they are distributed/shaped the same. We can do this by looking at some measures of wheresome selected values are within each data set - that .
Chi-square tests are great to show if distributions differ or i.docx
Chi-square tests are great to show if distributions differ or if two variables interact in producing outcomes. What are some examples of variables that you might want to check using the chi-square tests? What would these results tell you?
DataSee comments at the right of the data set.IDSalaryCompaMidpointAgePerformance RatingServiceGenderRaiseDegreeGender1Grade8231.000233290915.80FAThe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 10220.956233080714.70FANote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.11231.00023411001914.80FA14241.04323329012160FAThe column labels in the table mean:15241.043233280814.90FAID – Employee sample number Salary – Salary in thousands 23231.000233665613.31FAAge – Age in yearsPerformance Rating – Appraisal rating (Employee evaluation score)26241.043232295216.21FAService – Years of service (rounded)Gender: 0 = male, 1 = female 31241.043232960413.90FAMidpoint – salary grade midpoint Raise – percent of last raise35241.043232390415.31FAGrade – job/pay gradeDegree (0= BS\BA 1 = MS)36231.000232775314.31FAGender1 (Male or Female)Compa - salary divided by midpoint37220.956232295216.21FA42241.0432332100815.70FA3341.096313075513.60FB18361.1613131801115.61FB20341.0963144701614.81FB39351.129312790615.51FB7411.0254032100815.70FC13421.0504030100214.71FC22571.187484865613.80FD24501.041483075913.81FD45551.145483695815.20FD17691.2105727553130FE48651.1405734901115.31FE28751.119674495914.41FF43771.1496742952015.51FF19241.043233285104.61MA25241.0432341704040MA40251.086232490206.30MA2270.870315280703.90MB32280.903312595405.60MB34280.903312680204.91MB16471.175404490405.70MC27401.000403580703.91MC41431.075402580504.30MC5470.9794836901605.71MD30491.0204845901804.30MD1581.017573485805.70ME4661.15757421001605.51ME12601.0525752952204.50ME33641.122573590905.51ME38560.9825745951104.50ME44601.0525745901605.21ME46651.1405739752003.91ME47621.087573795505.51ME49601.0525741952106.60ME50661.1575738801204.60ME6761.1346736701204.51MF9771.149674910010041MF21761.1346743951306.31MF29721.074675295505.40MF
Week 1Week 1.Measurement and Description - chapters 1 and 21Measurement issues. Data, even numerically coded variables, can be one of 4 levels - nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, asthis impact the kind of analysis we can do with the data. For example, descriptive statistics such as means can only be done on interval or ratio level data.Please list under each label, the variables in our data set that belong in each group.NominalOrdinalIntervalRatiob.For each variable that you did not call ratio, why did you make that decision?2The first step in analyzing data sets is to find some summary descriptive statistics for key variables.For salary, compa, age, performance rating, and service; find the mean, standard deviation, and range for 3 groups: ...
ScoreWeek 5 Correlation and Regressio.docx
Score:
Week 5
Correlation and Regression
<1 point>
1.
Create a correlation table for the variables in our data set. (Use analysis ToolPak or StatPlus:mac LE function Correlation.)
a.
Reviewing the data levels from week 1, what variables can be used in a Pearson's Correlation table (which is what Excel produces)?
b. Place table here (C8):
c.
Using r = approximately .28 as the signicant r value (at p = 0.05) for a correlation between 50 values, what variables are
significantly related to Salary?
To compa?
d.
Looking at the above correlations - both significant or not - are there any surprises -by that I
mean any relationships you expected to be meaningful and are not and vice-versa?
e.
Does this help us answer our equal pay for equal work question?
<1 point>
2
Below is a regression analysis for salary being predicted/explained by the other variables in our sample (Midpoint,
age, performance rating, service, gender, and degree variables. (Note: since salary and compa are different ways of
expressing an employee’s salary, we do not want to have both used in the same regression.)
Plase interpret the findings.
Ho: The regression equation is not significant.
Ha: The regression equation is significant.
Ho: The regression coefficient for each variable is not significant
Note: technically we have one for each input variable.
Ha: The regression coefficient for each variable is significant
Listing it this way to save space.
Sal
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.9915591
R Square
0.9831894
Adjusted R Square
0.9808437
Standard Error
2.6575926
Observations
50
ANOVA
df
SS
MS
F
Significance F
Regression
6
17762.3
2960.38
419.1516
1.812E-36
Residual
43
303.7003
7.0628
Total
49
18066
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
-1.749621
3.618368
-0.4835
0.631166
-9.046755
5.5475126
-9.04675504
5.54751262
Midpoint
1.2167011
0.031902
38.1383
8.66E-35
1.1523638
1.2810383
1.152363828
1.28103827
Age
-0.004628
0.065197
-0.071
0.943739
-0.136111
0.1268547
-0.13611072
0.1268547
Performace Rating
-0.056596
0.034495
-1.6407
0.108153
-0.126162
0.0129695
-0.12616237
0.01296949
Service
-0.0425
0.084337
-0.5039
0.616879
-0.212582
0.1275814
-0.2125.
ScoreWeek 4Confidence Intervals and Chi Square (Chs .docx
Score:
Week 4
Confidence Intervals and Chi Square (Chs 11 - 12)
For questions 3 and 4 below, be sure to list the null and alternate hypothesis statements. Use .05 for your significance level in making your decisions.
For full credit, you need to also show the statistical outcomes - either the Excel test result or the calculations you performed.
<1 point>
1
Using our sample data, construct a 95% confidence interval for the population's mean salary for each gender.
Interpret the results. How do they compare with the findings in the week 2 one sample t-test outcomes (Question 1)?
Mean
St error
t value
Low
to
High
Males
Females
Interpretation:
<1 point>
2
Using our sample data, construct a 95% confidence interval for the mean salary difference between the genders in the population.
How does this compare to the findings in week 2, question 2?
Difference
St Err.
T value
Low
to
High
Yes/No
Can the means be equal?
Why?
How does this compare to the week 2, question 2 result (2 sampe t-test)?
a.
Why is using a two sample tool (t-test, confidence interval) a better choice than using 2 one-sample techniques when comparing two samples?
<1 point>
3
We found last week that the degree values within the population do not impact compa rates.
This does not mean that degrees are distributed evenly across the grades and genders.
Do males and females have athe same distribution of degrees by grade?
(Note: while technically the sample size might not be large enough to perform this test, ignore this limitation for this exercise.)
What are the hypothesis statements:
Ho:
Ha:
Note: You can either use the Excel Chi-related functions or do the calculations manually.
Data input tables - graduate degrees by gender and grade level
OBSERVED
A
B
C
D
E
F
Total
If desired, you can do manual calculations per cell here.
M Grad
A
B
C
D
E
F
Fem Grad
M Grad
Male Und
Fem Grad
Female Und
Male Und
Female Und
Sum =
EXPECTED
M Grad
For this exercise - ignore the requirement for a correction factor
Fem Grad
for cells with expected values less than 5.
Male Und
Female Und
Interpretation:
What is the value of the chi square statistic:
What is the p-value associated with this value:
Is the p-value <0.05?
Do you reject or not reject the null hypothesi.
Business Email Rubric Subject Line Subject line clea.docx
Business Email Rubric
Subject Line
Subject line
clearly states the
main point of the
email
5points
Subject line is a
bit long (5+
words) or a bit
short (1 word) but
states the point of
the email
3points
Subject line does
not correspond to
the main point of
the email
2points
Subject line is
missing
0points
Greeting
Email includes a
professional
greeting that is
appropriate for the
audience; uses
the person's first
name
5points
Email includes a
professional
greeting that is
adequate for the
audience but uses
the person's first
and last name or
just the last name
3points
Email includes a
greeting but it is
not personalized
2points
Email lacks a
greeting
0points
Introductory
Comment
Email includes an
introductory
positive, relevant
comment
5points
it Email includes
an introductory,
positive comment
but may be very
general
3points
Email includes an
introduction only
2points
Email lacks an
introductory
comment
0points
Content
Purpose of the
email is clear, as
is the outcome;
content is succinct
and well organized
and does not
include
unnecessary
information
5points
Purpose of the
email is clear, as
is the outcome;
content could be
better organized
3points
Purpose of the
email is not clear
and/or content is
poorly organized;
it took more than
one reading to
understand what
the email is about
2points
Email seems to be
a collection of
unrelated
statements; it is
difficult to figure
out what the
purpose is
0points
Closing and
Signature
Email includes a
complementary
closing and
signature with all
required items
(name,
title/position,
company name,
phone number)
5points
Email includes a
complementary
closing and
signature but the
signature is
incomplete
3points
Email may be
missing either a
complementary
closing or
signature
2points
Email lacks a
closing and
signature
0points
Writing
Conventions
Email looks
professional and
does not have any
formatting or
writing errors
5points
Email is well
presented with
minimal (<3)
formatting or
writing errors
3points
Email includes
several (3+)
formatting or
writing errors
2points
Email is poorly
presented and has
an accumulation
of writing errors
that interfere with
readability
0points
BUS308 Week 3 Lecture 2
Examining Differences – ANOVA and Chi Square
Expected Outcomes
After reading this lecture, the student should be familiar with:
1. Conducting hypothesis tests with the ANVOA and Chi Square tests
2. How to interpret the Analysis of Variance test output
3. How to interpret Determining significant differences between group means
4. The basics of the Chi Square Distribution.
Overview
This week we introduced the ANOVA test for multiple mean equality and the Chi Square
tests for distrib ...
Final Exam Due Friday, Week EightInstructions Each response is.docx
Final Exam Due Friday, Week Eight
Instructions: Each response is worth a maximum of 50 points. Number and state the question. Space and then give your response. Each response will be a minimum 175 and maximum 225 words. Utilize the book, at least one resource beyond the course book and your personal examples in each response. This means that beyond the course text you should have a minimum of 4 references for this midterm exam. Create appropriate headings/subheading for each response and then give your detailed answer to the question.
Use Word doc. or docx only. Times New Roman, 12 font and double-space everything. Include a cover page and reference page. Return exam via email attachment NLT. Follow APA 6th ed. formatting requirements.
NO LATE EXAMS WILL BE ACCEPTED!
1. In determining rates of pay, how are the decisions made as to who gets paid $7.00 per hour and who will receive $1 million for a years work?
2. How can team effort result in improved organizational profitability?
3. What is reverse discrimination and how does it influence the design of an executive compensation plan?
4. With benefits consuming approximately 40 percent of the compensation dollar of most organizations, among many managers the “fringe benefit” concept still exists. Develop a detailed outline of the various components of a typically benefits program, indicating their importance and scope. Develop the outline so that it can be used for a presentation. (This response length may vary, so ensure you meet the requirements as stated.)
DataSee comments at the right of the data set.IDSalaryCompaMidpointAgePerformance RatingServiceGenderRaiseDegreeGender1Grade8231.000233290915.80FAThe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 10220.956233080714.70FANote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.11231.00023411001914.80FA14241.04323329012160FAThe column labels in the table mean:15241.043233280814.90FAID – Employee sample number Salary – Salary in thousands 23231.000233665613.31FAAge – Age in yearsPerformance Rating – Appraisal rating (Employee evaluation score)26241.043232295216.21FAService – Years of service (rounded)Gender: 0 = male, 1 = female 31241.043232960413.90FAMidpoint – salary grade midpoint Raise – percent of last raise35241.043232390415.31FAGrade – job/pay gradeDegree (0= BS\BA 1 = MS)36231.000232775314.31FAGender1 (Male or Female)Compa - salary divided by midpoint37220.956232295216.21FA42241.0432332100815.70FA3341.096313075513.60FB18361.1613131801115.61FB20341.0963144701614.81FB39351.129312790615.51FB7411.0254032100815.70FC13421.0504030100214.71FC22571.187484865613.80FD24501.041483075913.81FD45551.145483695815.20FD17691.2105727553130FE48651.1405734901115.31FE28751.119674495914.41FF43771.1496742952015.51FF19241.043233285104.61MA25241.0432341704040MA40251.086232490206.30MA2270.87 ...
Recommended
can i get a quote. i got one from barzzy but she never replied.docx
can i get a quote. i got one from barzzy but she never replied
At this point we know the following about male and female salaries.
a.
Male and female overall average salaries are not equal in the population.
b.
Male and female overall average compas are equal in the population, but males are a bit more spread out.
c.
The male and female salary range are almost the same, as is their age and service.
d.
Average performance ratings per gender are equal.
Let's look at some other factors that might influence pay - education(degree) and performance ratings.
1
Last week, we found that average performance ratings do not differ between males and females in the population.
Now we need to see if they differ among the grades. Is the average performace rating the same for all grades?
(Assume variances are equal across the grades for this ANOVA.)
You can use these columns to place grade Perf Ratings if desired.
A
B
C
D
E
F
Null Hypothesis:
Alt. Hypothesis:
Place
B17 in Outcome range box.
Interpretation:
What is the p-value:
Is P-value < 0.05?
Do we REJ or Not reject the null?
If
the null hypothesis was rejected, what is the effect size value (eta squared):
Meaning of effect size measure:
What does that decision mean in terms of our equal pay question:
2
While it appears that average salaries per each grade differ, we need to test this assumption.
Is the average salary the same for each of the grade levels? (Assume equal variance, and use the analysis toolpak function ANOVA.)
Use the input table to the right to list salaries under each grade level.
Null Hypothesis:
If desired, place salaries per grade in these columns
Alt. Hypothesis:
A
B
C
D
E
F
Place
B55 in Outcome range box.
What is the p-value:
Is P-value < 0.05?
Do you reject or not reject the null hypothesis:
If
the null hypothesis was rejected, what is the effect size value (eta squared):
Meaning of effect size measure:
Interpretation:
3
The table and analysis below demonstrate a 2-way ANOVA with replication.
Please interpret the results.
BA
MA
Ho: Average compas by gender are equal
Male
1.017
1.157
Ha: Ave.
Ashford 4 - Week 3 - Discussion 1Your initial discussio.docx
Ashford 4: - Week 3 - Discussion 1
Your initial discussion thread is due on Day 3 (Thursday) and you have until Day 7 (Monday) to respond to your classmates. Your grade will reflect both the quality of your initial post and the depth of your responses. Reference the Discussion Forum Grading Rubric for guidance on how your discussion will be evaluated.
ANOVA
In many ways, comparing multiple sample means is simply an extension of what we covered last week. Just as we had 3 versions of the t-test (1 sample, 2 sample (with and without equal variance), and paired; we have several versions of ANOVA – single factor, factorial (called 2-factor with replication in Excel), and within-subjects (2-factor without replication in Excel). What examples (professional, personal, social) can you provide on when we might use each type? What would be the appropriate hypotheses statements for each example?
Guided Response: Review several of your classmates’ posts. Respond to at least two classmates by commenting on why you agree or disagree with the statistical test that your peers have described as appropriate in this scenario.
Ashford 4: - Week 3 - Discussion 2
Your initial discussion thread is due on Day 3 (Thursday) and you have until Day 7 (Monday) to respond to your classmates. Your grade will reflect both the quality of your initial post and the depth of your responses. Reference the Discussion Forum Grading Rubric for guidance on how your discussion will be evaluated.
Effect Size
Several statistical tests have a way to measure effect size. What is this, and when might you want to use it in looking at results from these tests on job related data?
Ashford 4: - Week 3 - Assignment
Problem Set Week Three
Complete the problems included in the resources below and submit your work in an Excel document. Be sure to show all of your work and clearly label all calculations.
All statistical calculations will use the Employee Salary Data Set and the Week 3 assignment sheet.
Carefully review the Grading Rubric for the criteria that will be used to evaluate your assignment.
See comments at the right of the data set.
ID
Salary
Compa
Midpoint
Age
Performance Rating
Service
Gender
Raise
Degree
Gender1
Grade
8
23
1.000
23
32
90
9
1
5.8
0
F
A
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)?
10
22
0.956
23
30
80
7
1
4.7
0
F
A
Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.
11
23
1.000
23
41
100
19
1
4.8
0
F
A
14
24
1.043
23
32
90
12
1
6
0
F
A
The column labels in the table mean:
15
24
1.043
23
32
80
8
1
4.9
0
F
A
ID – Employee sample number
Salary – Salary in thousands
23
23
1.000
23
36
65
6
1
3.3
1
F
A
Age – Age in years
Performance Rating – Appraisal rating (Employee evaluation score)
26
24
1.043
23
22
95
2
1
6.2
1
F
A
Service – Years of service (rounded)
Gender: ...
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseD.docx
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 Midpoint – salary grade midpoint Raise – percent of last raiseGrade – job/pay gradeDegree (0= BS\BA 1 = MS)Gender1 (Male or Female)Compa - salary divided by midpoint
Week 1Week 1.Measurement and Description - chapters 1 and 2The goal this week is to gain an understanding of our data set - what kind of data we are looking at, some descriptive measurse, and a look at how the data is distributed (shape).1Measurement issues. Data, even numerically coded variables, can be one of 4 levels - nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, asthis impact the kind of analysis we can do with the data. For example, descriptive statistics such as means can only be done on interval or ratio level data.Please list under each label, the variables in our data set that belong in each group.NominalOrdinalIntervalRatiob.For each variable that you did not call ratio, why did you make that decision?2The first step in analyzing data sets is to find some summary descriptive statistics for key variables.For salary, compa, age, performance rating, and service; find the mean, standard deviation, and range for 3 groups: overall sample, Females, and Males.You can use either the Data Analysis Descriptive Statistics tool or the Fx =average and =stdev functions. (the range must be found using the difference between the =max and =min functions with Fx) functions.Note: Place data to the right, if you use Descriptive statistics, place that to the right as well.Some of the values are completed for you - please finish the table.SalaryCompaAgePerf. Rat.ServiceOverallMean35.785.99.0Standard Deviation8.251311.41475.7177Note - data is a sample from the larger company populationRange304521FemaleMean32.584.27.9Standard Deviation6.913.64.9Range26.045.018.0MaleMean38.987.610.0Standard Deviation8.48.76.4Range28.030.021.03What is the probability for a:Probabilitya. Randomly selected person being a male in grade E?b. Randomly selected male being in grade E? Note part b is the same as given a male, what is probabilty of being in grade E?c. Why are the results different?4A key issue in comparing data sets is to see if they are distributed/shaped the same. We can do this by looking at some measures of wheresome selected values are within each data set - that .
Chi-square tests are great to show if distributions differ or i.docx
Chi-square tests are great to show if distributions differ or if two variables interact in producing outcomes. What are some examples of variables that you might want to check using the chi-square tests? What would these results tell you?
DataSee comments at the right of the data set.IDSalaryCompaMidpointAgePerformance RatingServiceGenderRaiseDegreeGender1Grade8231.000233290915.80FAThe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 10220.956233080714.70FANote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.11231.00023411001914.80FA14241.04323329012160FAThe column labels in the table mean:15241.043233280814.90FAID – Employee sample number Salary – Salary in thousands 23231.000233665613.31FAAge – Age in yearsPerformance Rating – Appraisal rating (Employee evaluation score)26241.043232295216.21FAService – Years of service (rounded)Gender: 0 = male, 1 = female 31241.043232960413.90FAMidpoint – salary grade midpoint Raise – percent of last raise35241.043232390415.31FAGrade – job/pay gradeDegree (0= BS\BA 1 = MS)36231.000232775314.31FAGender1 (Male or Female)Compa - salary divided by midpoint37220.956232295216.21FA42241.0432332100815.70FA3341.096313075513.60FB18361.1613131801115.61FB20341.0963144701614.81FB39351.129312790615.51FB7411.0254032100815.70FC13421.0504030100214.71FC22571.187484865613.80FD24501.041483075913.81FD45551.145483695815.20FD17691.2105727553130FE48651.1405734901115.31FE28751.119674495914.41FF43771.1496742952015.51FF19241.043233285104.61MA25241.0432341704040MA40251.086232490206.30MA2270.870315280703.90MB32280.903312595405.60MB34280.903312680204.91MB16471.175404490405.70MC27401.000403580703.91MC41431.075402580504.30MC5470.9794836901605.71MD30491.0204845901804.30MD1581.017573485805.70ME4661.15757421001605.51ME12601.0525752952204.50ME33641.122573590905.51ME38560.9825745951104.50ME44601.0525745901605.21ME46651.1405739752003.91ME47621.087573795505.51ME49601.0525741952106.60ME50661.1575738801204.60ME6761.1346736701204.51MF9771.149674910010041MF21761.1346743951306.31MF29721.074675295505.40MF
Week 1Week 1.Measurement and Description - chapters 1 and 21Measurement issues. Data, even numerically coded variables, can be one of 4 levels - nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, asthis impact the kind of analysis we can do with the data. For example, descriptive statistics such as means can only be done on interval or ratio level data.Please list under each label, the variables in our data set that belong in each group.NominalOrdinalIntervalRatiob.For each variable that you did not call ratio, why did you make that decision?2The first step in analyzing data sets is to find some summary descriptive statistics for key variables.For salary, compa, age, performance rating, and service; find the mean, standard deviation, and range for 3 groups: ...
ScoreWeek 5 Correlation and Regressio.docx
Score:
Week 5
Correlation and Regression
<1 point>
1.
Create a correlation table for the variables in our data set. (Use analysis ToolPak or StatPlus:mac LE function Correlation.)
a.
Reviewing the data levels from week 1, what variables can be used in a Pearson's Correlation table (which is what Excel produces)?
b. Place table here (C8):
c.
Using r = approximately .28 as the signicant r value (at p = 0.05) for a correlation between 50 values, what variables are
significantly related to Salary?
To compa?
d.
Looking at the above correlations - both significant or not - are there any surprises -by that I
mean any relationships you expected to be meaningful and are not and vice-versa?
e.
Does this help us answer our equal pay for equal work question?
<1 point>
2
Below is a regression analysis for salary being predicted/explained by the other variables in our sample (Midpoint,
age, performance rating, service, gender, and degree variables. (Note: since salary and compa are different ways of
expressing an employee’s salary, we do not want to have both used in the same regression.)
Plase interpret the findings.
Ho: The regression equation is not significant.
Ha: The regression equation is significant.
Ho: The regression coefficient for each variable is not significant
Note: technically we have one for each input variable.
Ha: The regression coefficient for each variable is significant
Listing it this way to save space.
Sal
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.9915591
R Square
0.9831894
Adjusted R Square
0.9808437
Standard Error
2.6575926
Observations
50
ANOVA
df
SS
MS
F
Significance F
Regression
6
17762.3
2960.38
419.1516
1.812E-36
Residual
43
303.7003
7.0628
Total
49
18066
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
-1.749621
3.618368
-0.4835
0.631166
-9.046755
5.5475126
-9.04675504
5.54751262
Midpoint
1.2167011
0.031902
38.1383
8.66E-35
1.1523638
1.2810383
1.152363828
1.28103827
Age
-0.004628
0.065197
-0.071
0.943739
-0.136111
0.1268547
-0.13611072
0.1268547
Performace Rating
-0.056596
0.034495
-1.6407
0.108153
-0.126162
0.0129695
-0.12616237
0.01296949
Service
-0.0425
0.084337
-0.5039
0.616879
-0.212582
0.1275814
-0.2125.
ScoreWeek 4Confidence Intervals and Chi Square (Chs .docx
Score:
Week 4
Confidence Intervals and Chi Square (Chs 11 - 12)
For questions 3 and 4 below, be sure to list the null and alternate hypothesis statements. Use .05 for your significance level in making your decisions.
For full credit, you need to also show the statistical outcomes - either the Excel test result or the calculations you performed.
<1 point>
1
Using our sample data, construct a 95% confidence interval for the population's mean salary for each gender.
Interpret the results. How do they compare with the findings in the week 2 one sample t-test outcomes (Question 1)?
Mean
St error
t value
Low
to
High
Males
Females
Interpretation:
<1 point>
2
Using our sample data, construct a 95% confidence interval for the mean salary difference between the genders in the population.
How does this compare to the findings in week 2, question 2?
Difference
St Err.
T value
Low
to
High
Yes/No
Can the means be equal?
Why?
How does this compare to the week 2, question 2 result (2 sampe t-test)?
a.
Why is using a two sample tool (t-test, confidence interval) a better choice than using 2 one-sample techniques when comparing two samples?
<1 point>
3
We found last week that the degree values within the population do not impact compa rates.
This does not mean that degrees are distributed evenly across the grades and genders.
Do males and females have athe same distribution of degrees by grade?
(Note: while technically the sample size might not be large enough to perform this test, ignore this limitation for this exercise.)
What are the hypothesis statements:
Ho:
Ha:
Note: You can either use the Excel Chi-related functions or do the calculations manually.
Data input tables - graduate degrees by gender and grade level
OBSERVED
A
B
C
D
E
F
Total
If desired, you can do manual calculations per cell here.
M Grad
A
B
C
D
E
F
Fem Grad
M Grad
Male Und
Fem Grad
Female Und
Male Und
Female Und
Sum =
EXPECTED
M Grad
For this exercise - ignore the requirement for a correction factor
Fem Grad
for cells with expected values less than 5.
Male Und
Female Und
Interpretation:
What is the value of the chi square statistic:
What is the p-value associated with this value:
Is the p-value <0.05?
Do you reject or not reject the null hypothesi.
Business Email Rubric Subject Line Subject line clea.docx
Business Email Rubric
Subject Line
Subject line
clearly states the
main point of the
email
5points
Subject line is a
bit long (5+
words) or a bit
short (1 word) but
states the point of
the email
3points
Subject line does
not correspond to
the main point of
the email
2points
Subject line is
missing
0points
Greeting
Email includes a
professional
greeting that is
appropriate for the
audience; uses
the person's first
name
5points
Email includes a
professional
greeting that is
adequate for the
audience but uses
the person's first
and last name or
just the last name
3points
Email includes a
greeting but it is
not personalized
2points
Email lacks a
greeting
0points
Introductory
Comment
Email includes an
introductory
positive, relevant
comment
5points
it Email includes
an introductory,
positive comment
but may be very
general
3points
Email includes an
introduction only
2points
Email lacks an
introductory
comment
0points
Content
Purpose of the
email is clear, as
is the outcome;
content is succinct
and well organized
and does not
include
unnecessary
information
5points
Purpose of the
email is clear, as
is the outcome;
content could be
better organized
3points
Purpose of the
email is not clear
and/or content is
poorly organized;
it took more than
one reading to
understand what
the email is about
2points
Email seems to be
a collection of
unrelated
statements; it is
difficult to figure
out what the
purpose is
0points
Closing and
Signature
Email includes a
complementary
closing and
signature with all
required items
(name,
title/position,
company name,
phone number)
5points
Email includes a
complementary
closing and
signature but the
signature is
incomplete
3points
Email may be
missing either a
complementary
closing or
signature
2points
Email lacks a
closing and
signature
0points
Writing
Conventions
Email looks
professional and
does not have any
formatting or
writing errors
5points
Email is well
presented with
minimal (<3)
formatting or
writing errors
3points
Email includes
several (3+)
formatting or
writing errors
2points
Email is poorly
presented and has
an accumulation
of writing errors
that interfere with
readability
0points
BUS308 Week 3 Lecture 2
Examining Differences – ANOVA and Chi Square
Expected Outcomes
After reading this lecture, the student should be familiar with:
1. Conducting hypothesis tests with the ANVOA and Chi Square tests
2. How to interpret the Analysis of Variance test output
3. How to interpret Determining significant differences between group means
4. The basics of the Chi Square Distribution.
Overview
This week we introduced the ANOVA test for multiple mean equality and the Chi Square
tests for distrib ...
Final Exam Due Friday, Week EightInstructions Each response is.docx
Final Exam Due Friday, Week Eight
Instructions: Each response is worth a maximum of 50 points. Number and state the question. Space and then give your response. Each response will be a minimum 175 and maximum 225 words. Utilize the book, at least one resource beyond the course book and your personal examples in each response. This means that beyond the course text you should have a minimum of 4 references for this midterm exam. Create appropriate headings/subheading for each response and then give your detailed answer to the question.
Use Word doc. or docx only. Times New Roman, 12 font and double-space everything. Include a cover page and reference page. Return exam via email attachment NLT. Follow APA 6th ed. formatting requirements.
NO LATE EXAMS WILL BE ACCEPTED!
1. In determining rates of pay, how are the decisions made as to who gets paid $7.00 per hour and who will receive $1 million for a years work?
2. How can team effort result in improved organizational profitability?
3. What is reverse discrimination and how does it influence the design of an executive compensation plan?
4. With benefits consuming approximately 40 percent of the compensation dollar of most organizations, among many managers the “fringe benefit” concept still exists. Develop a detailed outline of the various components of a typically benefits program, indicating their importance and scope. Develop the outline so that it can be used for a presentation. (This response length may vary, so ensure you meet the requirements as stated.)
DataSee comments at the right of the data set.IDSalaryCompaMidpointAgePerformance RatingServiceGenderRaiseDegreeGender1Grade8231.000233290915.80FAThe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 10220.956233080714.70FANote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.11231.00023411001914.80FA14241.04323329012160FAThe column labels in the table mean:15241.043233280814.90FAID – Employee sample number Salary – Salary in thousands 23231.000233665613.31FAAge – Age in yearsPerformance Rating – Appraisal rating (Employee evaluation score)26241.043232295216.21FAService – Years of service (rounded)Gender: 0 = male, 1 = female 31241.043232960413.90FAMidpoint – salary grade midpoint Raise – percent of last raise35241.043232390415.31FAGrade – job/pay gradeDegree (0= BS\BA 1 = MS)36231.000232775314.31FAGender1 (Male or Female)Compa - salary divided by midpoint37220.956232295216.21FA42241.0432332100815.70FA3341.096313075513.60FB18361.1613131801115.61FB20341.0963144701614.81FB39351.129312790615.51FB7411.0254032100815.70FC13421.0504030100214.71FC22571.187484865613.80FD24501.041483075913.81FD45551.145483695815.20FD17691.2105727553130FE48651.1405734901115.31FE28751.119674495914.41FF43771.1496742952015.51FF19241.043233285104.61MA25241.0432341704040MA40251.086232490206.30MA2270.87 ...
Week 4Confidence Intervals and Chi Square (Chs 11 - 12).docx
Week 4
Confidence Intervals and Chi Square (Chs 11 - 12)
For questions 3 and 4 below, be sure to list the null and alternate hypothesis statements. Use .05 for your significance level in making your decisions.
For full credit, you need to also show the statistical outcomes - either the Excel test result or the calculations you performed.
1
Using our sample data, construct a 95% confidence interval for the population's mean salary for each gender.
Interpret the results. How do they compare with the findings in the week 2 one sample t-test outcomes (Question 1)?
Mean
St error
t value
Low
to
High
Males
Females
Interpretation:
2
Using our sample data, construct a 95% confidence interval for the mean salary difference between the genders in the population.
How does this compare to the findings in week 2, question 2?
Difference
St Err.
T value
Low
to
High
Yes/No
Can the means be equal?
Why?
How does this compare to the week 2, question 2 result (2 sampe t-test)?
a.
Why is using a two sample tool (t-test, confidence interval) a better choice than using 2 one-sample techniques when comparing two samples?
3
We found last week that the degree values within the population do not impact compa rates.
This does not mean that degrees are distributed evenly across the grades and genders.
Do males and females have athe same distribution of degrees by grade?
(Note: while technically the sample size might not be large enough to perform this test, ignore this limitation for this exercise.)
What are the hypothesis statements:
Ho:
Ha:
Note: You can either use the Excel Chi-related functions or do the calculations manually.
Data input tables - graduate degrees by gender and grade level
OBSERVED
A
B
C
D
E
F
Total
If desired, you can do manual calculations per cell here.
M Grad
A
B
C
D
E
Fem Grad
M Grad
Male Und
Fem Grad
Female Und
Male Und
Female Und
Sum =
EXPECTED
M Grad
For this exercise - ignore the requirement for a correction factor
Fem Grad
for cells with expected values less than 5.
Male Und
Female Und
Interpretation:
What is the value of the chi square statistic:
What is the p-value associated with this value:
Is the p-value <0.05?
Do you reject or not reject the null hypothesis:
If you rejected the null, what is the Cramer's V correlation:
What does this correlation mean?
What does this .
Week 2Testing means - T-testsIn qu.docx
Week 2
Testing means - T-tests
In questions 2 and 3, be sure to include the null and alternate hypotheses you will be testing.
In the first 3 questions use alpha = 0.05 in making your decisions on rejecting or not rejecting the null hypothesis.
1
Below are 2 one-sample t-tests comparing male and female average salaries to the overall sample mean.
(Note: a one-sample t-test in Excel can be performed by selecting the 2-sample unequal variance t-test and making the second variable = Ho value -- see column S)
Based on our sample, how do you interpret the results and what do these results suggest about the population means for male and female average salaries?
Males
Females
Ho: Mean salary = 45
Ho: Mean salary = 45
Ha: Mean salary =/= 45
Ha: Mean salary =/= 45
Note: While the results both below are actually from Excel's t-Test: Two-Sample Assuming Unequal Variances,
having no variance in the Ho variable makes the calculations default to the one-sample t-test outcome - we are tricking Excel into doing a one sample test for us.
Male
Ho
Female
Ho
Mean
52
45
Mean
38
45
Variance
316
0
Variance
334.667
0
Observations
25
25
Observations
25
25
Hypothesized Mean Difference
0
Hypothesized Mean Difference
0
df
24
df
24
t Stat
1.96890383
t Stat
-1.9132
P(T<=t) one-tail
0.03030785
P(T<=t) one-tail
0.03386
t Critical one-tail
1.71088208
t Critical one-tail
1.71088
P(T<=t) two-tail
0.0606157
P(T<=t) two-tail
0.06772
t Critical two-tail
2.06389856
t Critical two-tail
2.0639
Conclusion: Do not reject Ho; mean equals 45
Conclusion: Do not reject Ho; mean equals 45
Is this a 1 or 2 tail test?
Is this a 1 or 2 tail test?
- why?
- why?
P-value is:
P-value is:
Is P-value > 0.05?
Is P-value > 0.05?
Why do we not reject Ho?
Why do we not reject Ho?
Interpretation:
2
Based on our sample data set, perform a 2-sample t-test to see if the population male and female average salaries could be equal to each other.
(Since we have not yet covered testing for variance equality, assume the data sets have statistically equal variances.)
Ho:
Ha:
Test to use:
Place
B43 in Outcome range box.
P-value is:
Is P-value < 0.05?
Reject or do not reject Ho:
If
the null hypothesis was rejected, what is the effect size value:
Meaning of effect size measure:
Interpretation:
b.
Since the one and two sample t-test results provided different outcomes, which is the proper/correct apporach to comparing salary equality?
Why?
3
Based on our sample data set, can the male and female compas in the population be e.
ScoreWeek 2Testing means - T-tests.docx
Score:
Week 2
Testing means - T-tests
Q3
In questions 2 and 3, be sure to include the null and alternate hypotheses you will be testing.
Ho
Female
Male
Female
In the first 3 questions use alpha = 0.05 in making your decisions on rejecting or not rejecting the null hypothesis.
45
34
1.017
1.096
45
41
0.870
1.025
<1 point>
1
Below are 2 one-sample t-tests comparing male and female average salaries to the overall sample mean.
45
23
1.157
1.000
(Note: a one-sample t-test in Excel can be performed by selecting the 2-sample unequal variance t-test and making the second variable = Ho value -- see column S)
45
22
0.979
0.956
Based on our sample, how do you interpret the results and what do these results suggest about the population means for male and female average salaries?
45
23
1.134
1.000
Males
Females
45
42
1.149
1.050
Ho: Mean salary = 45
Ho: Mean salary = 45
45
24
1.052
1.043
Ha: Mean salary =/= 45
Ha: Mean salary =/= 45
45
24
1.175
1.043
45
69
1.043
1.210
Note: While the results both below are actually from Excel's t-Test: Two-Sample Assuming Unequal Variances,
45
36
1.134
1.161
having no variance in the Ho variable makes the calculations default to the one-sample t-test outcome - we are tricking Excel into doing a one sample test for us.
45
34
1.043
1.096
Male
Ho
Female
Ho
45
57
1.000
1.187
Mean
52
45
Mean
38
45
45
23
1.074
1.000
Variance
316
0
Variance
334.6666667
0
45
50
1.020
1.041
Observations
25
25
Observations
25
25
45
24
0.903
1.043
Hypothesized Mean Difference
0
Hypothesized Mean Difference
0
45
75
1.122
1.119
df
24
df
24
45
24
0.903
1.043
t Stat
1.968903827
t Stat
-1.913206357
45
24
0.982
1.043
P(T<=t) one-tail
0.03030785
P(T<=t) one-tail
0.033862118
45
23
1.086
1.000
t Critical one-tail
1.71088208
t Critical one-tail
1.71088208
45
22
1.075
0.956
P(T<=t) two-tail
0.060615701
P(T<=t) two-tail
0.067724237
45
35
1.052
1.129
t Critical two-tail
2.063898562
t Critical two-tail
2.063898562
45
24
1.140
1.043
Conclusion: Do not reject Ho; mean equals 45
Conclusion: Do not reject Ho; mean equals 45
45
77
1.087
1.149
Is this a 1 or 2 tail test?
Is this a 1 or 2 tail test?
- why?
- why?
P-value is:
P-value is:
45
55
1.052
1.145
Is P-value > 0.05?
Is P-value > 0.05?
45
65
1.157
1.140
Why do we not reject Ho?
Why do we not reject Ho?
Interpretation:
<1 point>
2
Based on our sample data set, perform a 2-sample t-test to see if the pop.
DataSalCompaMidAgeEESSERGRaiseDegGen1Gr1581.017573485805.70METhe o.docx
1. The document contains sample employee data including salary, age, tenure, performance ratings and other variables.
2. It poses the question of whether males and females are paid equally for equal work and provides statistical analyses to examine this, including t-tests, ANOVA, correlations, and regressions.
3. The analyses examine differences in mean salaries and other variables between males and females, across job grades, and relationships between variables, with the goal of determining if the sample shows evidence of unequal pay between genders.
BUSI 620Questions for Critical Thinking 3Salvatore’s Chapter.docx
BUSI 620
Questions for Critical Thinking 3
Salvatore’s Chapter 6:
a. Discussion Questions: 1, 7, and 15.
b. Problems: 7 and appendix problems 1 and 3 (pp. 256–257).
Note:
1. Revised P7: Just construct the diffusion index from month 2 to 3. In this problem, we have three leading indicators. The diffusion index from month 1 to 2 is 66.7 (=2/3) because two indicators move up and move down (see p. 236).
2. Appendix problem 1: Delete “Eliminating the data for 2000.” You need to calculate the moving average forecasts and RMSEs for year 2000, not the whole data period.
3. Appendix problem 3: Compare RMSEs for moving average and exponential forecasts to answer “Is this a better forecast than the moving average” (see also p. 234)? Use 166.63, the mean of all 36 months, as the initial forecast for Jan. 1998 for both exponential smoothing forecasts.
Salvatore’s Chapter 7:
a. Discussion Questions: 3, 11, and 12.
b. Problems: 4, 12, and 13.
Note:
1. P4: Ms. Smith should hire workers as long as their marginal revenue product (MRP) exceeds their marginal resource cost (MRC) and until MRP=MRC.
MRP=MR x MP = P x MP = $10 x MP (use information in the problem to calculate MP). MRC=wages=$40.
2. P12(a): Calculate Q when L=1and K=1, and L=2 and K=2. Then compare and answer the question about the returns to scale.
3. P12(b): Given K=1, show the change in Q if L changes from 1 to 2 and 2 to 3. Answer the question about diminishing returns.
4. P13(a): See figure (7-4) on page 276.
DataSee comments at the right of the data set.IDSalaryCompaMidpointAgePerformance RatingServiceGenderRaiseDegreeGender1Grade8231.000233290915.80FAThe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 10220.956233080714.70FANote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.11231.00023411001914.80FA14241.04323329012160FAThe column labels in the table mean:15241.043233280814.90FAID – Employee sample number Salary – Salary in thousands 23231.000233665613.31FAAge – Age in yearsPerformance Rating – Appraisal rating (Employee evaluation score)26241.043232295216.21FAService – Years of service (rounded)Gender: 0 = male, 1 = female 31241.043232960413.90FAMidpoint – salary grade midpoint Raise – percent of last raise35241.043232390415.31FAGrade – job/pay gradeDegree (0= BS\BA 1 = MS)36231.000232775314.31FAGender1 (Male or Female)Compa - salary divided by midpoint37220.956232295216.21FA42241.0432332100815.70FA3341.096313075513.60FB18361.1613131801115.61FB20341.0963144701614.81FB39351.129312790615.51FB7411.0254032100815.70FC13421.0504030100214.71FC22571.187484865613.80FD24501.041483075913.81FD45551.145483695815.20FD17691.2105727553130FE48651.1405734901115.31FE28751.119674495914.41FF43771.1496742952015.51FF19241.043233285104.61MA25241.0432341704040MA40251.086232490206.30MA2270.870315280703.90MB32280.903312595405.60MB34280.903312680204.91 ...
Ash bus 308 week 2 problem set new
This document provides instructions for a problem set analyzing employee salary data. It instructs the user to copy salary and employee data into their assignment file. It then provides guidance on using descriptive statistics and t-tests to analyze differences in compensation ratios between male and female employees. The goal is to determine if there is statistical evidence that male and female employees are paid equally for equal work.
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1Create a correlation table for the variables in our data set. (Us.docx
1
Create a correlation table for the variables in our data set. (Use analysis ToolPak function Correlation.)
a. Interpret the results.
What variables seem to be important in seeing if we pay males and females equally for equal work?
2
Below is a regression analysis for salary being predicted/explained by the other variables in our sample
(Mid,
age, ees, sr, raise, and deg variables.) (Note: since salary and compa are different ways of
expressing an employee’s salary, we do not want to have both used in the same regression.)
Ho: The regression equation is not significant.
Ha: The regression equation is significant.
Ho: The regression coefficient for each variable is not significant
Ha: The regression coefficient for each variable is significant
Sal
The analysis used Sal as the y (dependent variable) and
SUMMARY OUTPUT
mid, age, ees, sr, g, raise, and deg as the dependent
variables (entered as a range).
Regression Statistics
Multiple R
0.99215498
R Square
0.9843715
Adjusted R Square
0.98176675
Standard Error
2.59277631
Observations
50
ANOVA
df
SS
MS
F
Significance F
Regression
7
17783.7
2540.52
377.914
8.44043E-36
Residual
42
282.345
6.72249
Total
49
18066
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
-4.009
3.775
-1.062
0.294
-11.627
3.609
-11.627
3.609
Mid
1.220
0.030
40.674
0.000
1.159
1.280
1.159
1.280
Age
0.029
0.067
0.439
0.663
-0.105
0.164
-0.105
0.164
EES
-0.096
0.047
-2.020
0.050
-0.191
0.000
-0.191
0.000
SR
-0.074
0.084
-0.876
0.386
-0.244
0.096
-0.244
0.096
G
2.552
0.847
3.012
0.004
0.842
4.261
0.842
4.261
Raise
0.834
0.643
1.299
0.201
-0.462
2.131
-0.462
2.131
Deg
1.002
0.744
1.347
0.185
-0.500
2.504
-0.500
2.504
Interpretation:
Do you reject or not reject the regression null hypothesis?
Do you reject or not reject the null hypothesis for each variable?
What is the regression equation, using only significant variables if any exist?
What does result tell us about equal pay for equal work for males and females?
3
Perform a regression analysis using compa as the dependent variable and the same independent
variables as used in question 2.
Show the result, and interpret your findings by answering the same questions.
Note: be sure to include the appropriate hypothesis statements.
4
Based on all of your results to date, is gender a factor in the pay practices of this company?
Why or why not?
Which is the best variable to use in analyzing pay practices - salary or compa?
Why?
.
Week 3 Lecture 9 Effect Size When we reject the null h.docx
Week 3 Lecture 9
Effect Size
When we reject the null hypothesis with an ANOVA test, we have two questions that
arise. The first, which pair of means differs significantly, we have dealt with already. The
second question, similar to what we asked with the t-test null hypothesis rejection is; what
caused the rejection, the sample size or the variable interactions? This question is again
answered using an effect size measure.
Recall that the effect size measure shows how likely the variable interaction caused the
null hypothesis rejection. Large values lead us to say the variables caused the outcome, while
small values lead us to say the outcome has little to no practical significance as the sample size
was the most likely cause of the rejection of the null.
With the single factor ANOVA, the effect size measure is eta squared, and equals the
SS(between)/SS(total) (Tanner & Youssef-Morgan, 2013). For our salary example in Lecture 8,
eta squared equals 17686.02 (SS(between) / 18066 (SS(total) = 0.979 (rounded). Eta squared
effect size measures have different interpretation values than Cohen’s d (from the t-
test). According to Nandy (2012), a small eta squared effect size has a value of 0.01, a medium
of 0.06, and a large value of 0.14 or more. This means we have a large effect size, and the
variables of salary and grade interaction are the most likely cause of our rejecting the null
hypothesis rather than the sample size.
Side note: Eta squared can also be interpreted as the percent of “differences between
group scores that can be explained by the independent variable” (Tanner & Youssef-Morgan,
2013, p. 123). This is consistent with our saying the variable interactions caused the outcome.
Different Forms of ANOVA
Just as the t-test has several forms, so does the ANOVA test. Excel has three versions
available. While we will focus only on the single factor test, a brief description of the other two
versions will be presented.
ANOVA: Two factor without replication
The ANOVA – two factor without replication tests mean differences from two different
variables at the same time. If we are interested in knowing if the mean salary differs by grade
and also by gender, we can perform one two-factor test rather than two separate tests. As
mentioned in lecture two for this week, this is more efficient and maintains our desired alpha
significance level.
Excel Example. To test the mean salaries by grade and gender at the same time, we
would set up our hypothesis test as follows.
Step 1: Ho1: All salary means are equal across grades.
Ha1: At least one mean differs.
Ho2: All gender (male and female) means are equal.
Ha2: At least one mean differs.
Note that in this test, we need to have a hypothesis statement pair for each variable being
tested.
Step 2: ANOVA: Two sample without replication.
Step 3: Reject the null hypothesis if the p-value is < alpha = .05.
Step 4: Perform the test. W.
Statistical Significance Tests.pptx
Statistical tests help justify if sample results can be applied to a population. ANOVA compares group means and is preferred over t-tests for 3+ groups. It calculates variation between and within groups to obtain an F-ratio. If the F-ratio exceeds its critical value, the null hypothesis that group means are equal is rejected, showing group means differ significantly. Two-way ANOVA extends this to consider two factors' influence, computing interaction effects between factors.
Excel Files AssingmentsCopy of Student_Assignment_File.11.01..docx
Excel Files Assingments/Copy of Student_Assignment_File.11.01.2016.xlsx
DataIDSalaryCompa-ratioMidpointAgePerformance RatingServiceGenderRaiseDegreeGender1GradeCopy Employee Data set to this page.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 serviceGender: 0 = male, 1 = female Midpoint – salary grade midpoint Raise – percent of last raiseGrade – job/pay gradeDegree (0= BS\BA 1 = MS)Gender1 (Male or Female)Compa-ratio - salary divided by midpoint
Week 2This assignment covers the material presented in weeks 1 and 2.Six QuestionsBefore starting this assignment, make sure the the assignment data from the Employee Salary Data Set file is copied over to this Assignment file.You can do this either by a copy and paste of all the columns or by opening the data file, right clicking on the Data tab, selecting Move or Copy, and copying the entire sheet to this file(Weekly Assignment Sheet or whatever you are calling your master assignment file).It is highly recommended that you copy the data columns (with labels) and paste them to the right so that whatever you do will not disrupt the original data values and relationships.To Ensure full credit for each question, you need to show how you got your results. For example, Question 1 asks for several data values. If you obtain them using descriptive statistics,then the cells should have an "=XX" formula in them, where XX is the column and row number showing the value in the descriptive statistics table. If you choose to generate each value using fxfunctions, then each function should be located in the cell and the location of the data values should be shown.So, Cell D31 - as an example - shoud contain something like "=T6" or "=average(T2:T26)". Having only a numerical value will not earn full credit.The reason for this is to allow instructors to provide feedback on Excel tools if the answers are not correct - we need to see how the results were obtained.In starting the analysis on a research question, we focus on overall descriptive statistics and seeing if differences exist. Probing into reasons and mitigating factors is a follow-up activity.1The first step in analyzing data sets is to find some summary descriptive statistics for key variables. Since the assignment problems willfocus mostly on the compa-ratios, we need to find the mean, standard deviations, and range for our groups: Males, Females, and Overall.Sorting the compa-ratios into male and females will require you copy and paste the Compa-ratio and Gender1 columns, and then sort on Gender1.The values for age, performance rating, and service are prov ...
Chapter 18 – Pricing Setting in the Business WorldThere are few .docx
Chapter 18 – Pricing Setting in the Business World
There are few Methods for setting pricing – costs methods vs demand methods
Formulas considering costs and mark up will help you to do the Problem set assignment:
1. Markup for setting prices (Mark up $ = SP-CP); MARK UP % = (MU $/SP) X100)
Formula for setting price with the markup method
SP = Cost/(1- Markup %)
Example - retailer buys A hat for $15 and wants a 40% markup, his selling price would be….
SP = 15/(1-.40)=.60
= $25.00
2. Understand Role of different costs – fixed, variable, total costs and average costs
3. What is the breakeven point? Formula for calculating the Break Even point.
BE = Total Fixed Cost/Fixed cost contribution
Fixed Cost Contribution=Price – variable cost
4. Average Cost = when there are many flavors/types of the same product, producer determines average cost and then add the mark up to set a common selling price.
.ANOVA
Analysis of Variance is a method of testing the equality of three or more population
means by analyzing sample variance.
One-Way ANOVA
The one-way ANOVA is used to compare three or more population means when there is
one factor of interest.
Requirements
The populations have distributions that are approximately normal.
The populations have the same variance.
The samples are simple random samples of quantitative data.
The samples are independent of each other.
The different samples are from populations that are categorized in only one.
way
One-Way ANOVA is a hypothesis test. There are seven steps for a hypothesis test.
Example
A professor at a local University believes there is a relationship between head size and
the major of the students in her biostatistics classes. She takes a random sample of 20
students from each of three classes and records their major and head circumference.
The data are shown in the following table.
Step 1: State the null hypothesis.
Mean 1 equals mean 2 equals mean 3 equals mean 4.
Step 2: State the Alternative hypothesis.
At least one mean is different.
Step 3: State the Level of Significance.
The level of significance is 0.05.
Step 4: State the test statistic.
variance between samples
variance within samples
F
The test statistic follows the F distribution which has two degrees of freedom, one for
the numerator and one for the denominator.
The calculations for the test statistic are complicated, so a software program is
generally used for the calculations. We will be using Microsoft Excel for this example.
Step 5: Calculate
The calculations are done in Microsoft Excel using the data analysis toolpak. Enter the
data into the spread sheet as shown here. Click on data and the data analysis tookpak
button is on the right.
When you click on the button a dialogue box appears.
Choose ANOVA One Factor. Then another dialogue box appears.
Input range is where the data is in the table. Be sure to put a check in the box for labels
in.
The exam is composed of two parts vocabulary and short essay. You.docx
The exam is composed of two parts: vocabulary and short essay. Your answers must be typed using standard format 12 inch font, Times New Roman with one inch margins.The maximum points you can earn on this exam are 100 points. For every day late 5 points will be deducted. Good luck!
Part I: Vocabulary (40 points)
Briefly define the following terms used by the philosophers we studied and discussed in class and lecture:
1. Hip Hop’s 5 Elements
2. Sociogeny
3. Nihilism
4. Racial Identity
5. Ethnic Identity
6. Asymmetrical Invisibility
7. Prohibited Actions
8. Permissible Actions
9. Obligatory Actions
10. Supererogatory Actions
11. Classical Racialism
12. Critical Racialism
13. Primary Goods
14. Social Goods
15. Objectification
16. The gaze
17. Gender Performativity
18. Authenticity
19. Binary thinking
20. Eminem Enima
Part II: Short Essay (60 points)
Answer the question below. Do not write a title page or works cited page. Do not go over 3 double-spaced pages. This exercise is asking you to synthesize different views, put them in conversation with each other and draw out implications of the author’s arguments.
1. 1. Do Lil Kim’s raps challenge sexist binaries according to Kathryn Gines and Cheryl Keyes?
You must support your interpretation of how these philosophers respond to this question by providing some direct quotes from their articles with MLA style parenthetical citations. Please quote sparingly and be very selective in the few short quotes you use. No large block quotes that take up a lot of space or a flurry of little quotes that drown out your own words/voice. Also remember to be mindful of drawing out the implications of a quote. Interpret what it means because just citing a piece of textual evidence is not always sufficient. In an explanation you have to briefly unpack the quote so the reader can understand the force of the argument.
Top of Form
Bottom of Form
Sheet1Score:Week 3ANOVA and Paired T-testAt this point we know the following about male and female salaries.a.Male and female overall average salaries are not equal in the population.b.Male and female overall average compas are equal in the population, but males are a bit more spread out.c.The male and female salary range are almost the same, as is their age and service.d. Average performance ratings per gender are equal.Let's look at some other factors that might influence pay - education(degree) and performance ratings.<1 point>1Last week, we found that average performance ratings do not differ between males and females in the population.Now we need to see if they differ among the grades. Is the average performace rating the same for all grades?(Assume variances are equal across the grades for this ANOVA.)You can use these columns to place grade Perf Ratings if desired.ABCDEFNull Hypothesis:Alt. Hypothesis:Place B17 in Outcome range box.Interpretation:What is the p-value:Is P-value < 0.05?Do we REJ or Not reject the null?If the null hypothesis was rejected, what is the ...
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseD.docx
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GrStudents: Copy the Student Data file data values into this sheet to assist in doing your weekly assignments.157.71.012573485805.70METhe 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)? 227.80.897315280703.90MBNote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.3341.096313075513.61FB459.21.03857421001605.51METhe column labels in the table mean:549.51.0314836901605.71MDID – Employee sample number Salary – Salary in thousands 675.71.1306736701204.51MFAge – Age in yearsPerformance Rating - Appraisal rating (employee evaluation score)741.71.0434032100815.71FCService – Years of service (rounded)Gender – 0 = male, 1 = female 823.41.018233290915.81FAMidpoint – salary grade midpoint Raise – percent of last raise980.81.206674910010041MFGrade – job/pay gradeDegree (0= BS\BA 1 = MS)1023.61.027233080714.71FAGender1 (Male or Female)Compa - salary divided by midpoint1123.61.02423411001914.81FA1266.91.1745752952204.50ME1341.61.0414030100214.70FC1421.50.93623329012161FA1524.41.059233280814.91FA16390.975404490405.70MC1768.81.2075727553131FE1834.91.1263131801115.60FB1923.21.008233285104.61MA20361.1603144701614.80FB2175.31.1246743951306.31MF2256.71.182484865613.81FD2322.60.984233665613.30FA2451.51.072483075913.80FD2525.51.1092341704040MA2622.90.994232295216.20FA2743.51.088403580703.91MC2874.41.111674495914.40FF2973.51.097675295505.40MF3045.70.9524845901804.30MD3123.71.031232960413.91FA3226.90.867312595405.60MB3355.10.967573590905.51ME34280.904312680204.91MB3521.90.953232390415.30FA3623.71.032232775314.30FA3723.21.010232295216.20FA3857.61.0105745951104.50ME3934.31.108312790615.50FB4024.41.062232490206.30MA4140.51.012402580504.30MC4223.31.0122332100815.71FA4377.21.1526742952015.50FF4456.90.9995745901605.21ME4557.71.202483695815.21FD4665.41.1485739752003.91ME4756.80.997573795505.51ME4859.71.0485734901115.31FE4962.41.0955741952106.60ME5056.50.9925738801204.60ME
Week 1Week 1.Measurement and Description - chapters 1 and 2The goal this week is to gain an understanding of our data set - what kind of data we are looking at, some descriptive measurse, and a look at how the data is distributed (shape).1Measurement issues. Data, even numerically coded variables, can be one of 4 levels - nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, asthis impact the kind of analysis we can do with the data. For example, descriptive statistics such as means can only be done on interval or ratio level data.Please list under each label, the variables in our data set that belong in each group.NominalOrdinalIntervalRatiob.For each variable that you did not call ratio, why did you make that decision?2The first step in analyzing data sets is to find some summary descriptive statistics for key variables.For salary, compa, age, .
Week 2 – Lecture 3 Making judgements about differences bet.docx
Week 2 – Lecture 3
Making judgements about differences between group statistics is one of the most
powerful things that statistics can do for us. It is also one of the most counter-intuitive things
that we need to master in the class.
Lecture 1 introduced the hypothesis testing procedure used in statistical testing. Lecture
2 examined how to set up, perform, and interpret the F test for variance equality. This lecture
will focus on t-tests for testing mean equality. Again, these examples will use the compa-ratio
variable, while the homework should use the Salary variable.
The T-Test
While we test for variance equality with an F test, we use the T-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.
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 the second reason for performing the F-test first.)
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 students, preference 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).
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.
Question 2
The second question for this week asks about salary mean equality between males and
females. The data and test se.
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 ...
Select a foreign country and analyze its monetary system. Formatted .docx
This document outlines the requirements for a 1000 word paper analyzing the monetary system of a selected foreign country. The paper must follow APA style guidelines and address the evolution of the country's monetary system and impacts of policies, describe major components and organizations, discuss currency exchange rates and exposures, and recommend whether to buy or sell futures/options in that currency.
Select a company of your choice. Study the company’ CEO and determ.docx
Select a company of your choice. Study the company’ CEO and determine his/her leadership style.
Explain the type of leadership style the CEO possesses and its effectiveness.
Write a consultative memo to the CEO by providing areas for improvement in his/her leadership style, relative to human asset and strategic planning. Support your answer with research and cite according to APA guidelines.
2 to 3 pages reference sited
.
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Similar to ScoreWeek 3ANOVA and Paired T-test.docx
Week 4Confidence Intervals and Chi Square (Chs 11 - 12).docx
Week 4
Confidence Intervals and Chi Square (Chs 11 - 12)
For questions 3 and 4 below, be sure to list the null and alternate hypothesis statements. Use .05 for your significance level in making your decisions.
For full credit, you need to also show the statistical outcomes - either the Excel test result or the calculations you performed.
1
Using our sample data, construct a 95% confidence interval for the population's mean salary for each gender.
Interpret the results. How do they compare with the findings in the week 2 one sample t-test outcomes (Question 1)?
Mean
St error
t value
Low
to
High
Males
Females
Interpretation:
2
Using our sample data, construct a 95% confidence interval for the mean salary difference between the genders in the population.
How does this compare to the findings in week 2, question 2?
Difference
St Err.
T value
Low
to
High
Yes/No
Can the means be equal?
Why?
How does this compare to the week 2, question 2 result (2 sampe t-test)?
a.
Why is using a two sample tool (t-test, confidence interval) a better choice than using 2 one-sample techniques when comparing two samples?
3
We found last week that the degree values within the population do not impact compa rates.
This does not mean that degrees are distributed evenly across the grades and genders.
Do males and females have athe same distribution of degrees by grade?
(Note: while technically the sample size might not be large enough to perform this test, ignore this limitation for this exercise.)
What are the hypothesis statements:
Ho:
Ha:
Note: You can either use the Excel Chi-related functions or do the calculations manually.
Data input tables - graduate degrees by gender and grade level
OBSERVED
A
B
C
D
E
F
Total
If desired, you can do manual calculations per cell here.
M Grad
A
B
C
D
E
Fem Grad
M Grad
Male Und
Fem Grad
Female Und
Male Und
Female Und
Sum =
EXPECTED
M Grad
For this exercise - ignore the requirement for a correction factor
Fem Grad
for cells with expected values less than 5.
Male Und
Female Und
Interpretation:
What is the value of the chi square statistic:
What is the p-value associated with this value:
Is the p-value <0.05?
Do you reject or not reject the null hypothesis:
If you rejected the null, what is the Cramer's V correlation:
What does this correlation mean?
What does this .
Week 2Testing means - T-testsIn qu.docx
Week 2
Testing means - T-tests
In questions 2 and 3, be sure to include the null and alternate hypotheses you will be testing.
In the first 3 questions use alpha = 0.05 in making your decisions on rejecting or not rejecting the null hypothesis.
1
Below are 2 one-sample t-tests comparing male and female average salaries to the overall sample mean.
(Note: a one-sample t-test in Excel can be performed by selecting the 2-sample unequal variance t-test and making the second variable = Ho value -- see column S)
Based on our sample, how do you interpret the results and what do these results suggest about the population means for male and female average salaries?
Males
Females
Ho: Mean salary = 45
Ho: Mean salary = 45
Ha: Mean salary =/= 45
Ha: Mean salary =/= 45
Note: While the results both below are actually from Excel's t-Test: Two-Sample Assuming Unequal Variances,
having no variance in the Ho variable makes the calculations default to the one-sample t-test outcome - we are tricking Excel into doing a one sample test for us.
Male
Ho
Female
Ho
Mean
52
45
Mean
38
45
Variance
316
0
Variance
334.667
0
Observations
25
25
Observations
25
25
Hypothesized Mean Difference
0
Hypothesized Mean Difference
0
df
24
df
24
t Stat
1.96890383
t Stat
-1.9132
P(T<=t) one-tail
0.03030785
P(T<=t) one-tail
0.03386
t Critical one-tail
1.71088208
t Critical one-tail
1.71088
P(T<=t) two-tail
0.0606157
P(T<=t) two-tail
0.06772
t Critical two-tail
2.06389856
t Critical two-tail
2.0639
Conclusion: Do not reject Ho; mean equals 45
Conclusion: Do not reject Ho; mean equals 45
Is this a 1 or 2 tail test?
Is this a 1 or 2 tail test?
- why?
- why?
P-value is:
P-value is:
Is P-value > 0.05?
Is P-value > 0.05?
Why do we not reject Ho?
Why do we not reject Ho?
Interpretation:
2
Based on our sample data set, perform a 2-sample t-test to see if the population male and female average salaries could be equal to each other.
(Since we have not yet covered testing for variance equality, assume the data sets have statistically equal variances.)
Ho:
Ha:
Test to use:
Place
B43 in Outcome range box.
P-value is:
Is P-value < 0.05?
Reject or do not reject Ho:
If
the null hypothesis was rejected, what is the effect size value:
Meaning of effect size measure:
Interpretation:
b.
Since the one and two sample t-test results provided different outcomes, which is the proper/correct apporach to comparing salary equality?
Why?
3
Based on our sample data set, can the male and female compas in the population be e.
ScoreWeek 2Testing means - T-tests.docx
Score:
Week 2
Testing means - T-tests
Q3
In questions 2 and 3, be sure to include the null and alternate hypotheses you will be testing.
Ho
Female
Male
Female
In the first 3 questions use alpha = 0.05 in making your decisions on rejecting or not rejecting the null hypothesis.
45
34
1.017
1.096
45
41
0.870
1.025
<1 point>
1
Below are 2 one-sample t-tests comparing male and female average salaries to the overall sample mean.
45
23
1.157
1.000
(Note: a one-sample t-test in Excel can be performed by selecting the 2-sample unequal variance t-test and making the second variable = Ho value -- see column S)
45
22
0.979
0.956
Based on our sample, how do you interpret the results and what do these results suggest about the population means for male and female average salaries?
45
23
1.134
1.000
Males
Females
45
42
1.149
1.050
Ho: Mean salary = 45
Ho: Mean salary = 45
45
24
1.052
1.043
Ha: Mean salary =/= 45
Ha: Mean salary =/= 45
45
24
1.175
1.043
45
69
1.043
1.210
Note: While the results both below are actually from Excel's t-Test: Two-Sample Assuming Unequal Variances,
45
36
1.134
1.161
having no variance in the Ho variable makes the calculations default to the one-sample t-test outcome - we are tricking Excel into doing a one sample test for us.
45
34
1.043
1.096
Male
Ho
Female
Ho
45
57
1.000
1.187
Mean
52
45
Mean
38
45
45
23
1.074
1.000
Variance
316
0
Variance
334.6666667
0
45
50
1.020
1.041
Observations
25
25
Observations
25
25
45
24
0.903
1.043
Hypothesized Mean Difference
0
Hypothesized Mean Difference
0
45
75
1.122
1.119
df
24
df
24
45
24
0.903
1.043
t Stat
1.968903827
t Stat
-1.913206357
45
24
0.982
1.043
P(T<=t) one-tail
0.03030785
P(T<=t) one-tail
0.033862118
45
23
1.086
1.000
t Critical one-tail
1.71088208
t Critical one-tail
1.71088208
45
22
1.075
0.956
P(T<=t) two-tail
0.060615701
P(T<=t) two-tail
0.067724237
45
35
1.052
1.129
t Critical two-tail
2.063898562
t Critical two-tail
2.063898562
45
24
1.140
1.043
Conclusion: Do not reject Ho; mean equals 45
Conclusion: Do not reject Ho; mean equals 45
45
77
1.087
1.149
Is this a 1 or 2 tail test?
Is this a 1 or 2 tail test?
- why?
- why?
P-value is:
P-value is:
45
55
1.052
1.145
Is P-value > 0.05?
Is P-value > 0.05?
45
65
1.157
1.140
Why do we not reject Ho?
Why do we not reject Ho?
Interpretation:
<1 point>
2
Based on our sample data set, perform a 2-sample t-test to see if the pop.
DataSalCompaMidAgeEESSERGRaiseDegGen1Gr1581.017573485805.70METhe o.docx
1. The document contains sample employee data including salary, age, tenure, performance ratings and other variables.
2. It poses the question of whether males and females are paid equally for equal work and provides statistical analyses to examine this, including t-tests, ANOVA, correlations, and regressions.
3. The analyses examine differences in mean salaries and other variables between males and females, across job grades, and relationships between variables, with the goal of determining if the sample shows evidence of unequal pay between genders.
BUSI 620Questions for Critical Thinking 3Salvatore’s Chapter.docx
BUSI 620
Questions for Critical Thinking 3
Salvatore’s Chapter 6:
a. Discussion Questions: 1, 7, and 15.
b. Problems: 7 and appendix problems 1 and 3 (pp. 256–257).
Note:
1. Revised P7: Just construct the diffusion index from month 2 to 3. In this problem, we have three leading indicators. The diffusion index from month 1 to 2 is 66.7 (=2/3) because two indicators move up and move down (see p. 236).
2. Appendix problem 1: Delete “Eliminating the data for 2000.” You need to calculate the moving average forecasts and RMSEs for year 2000, not the whole data period.
3. Appendix problem 3: Compare RMSEs for moving average and exponential forecasts to answer “Is this a better forecast than the moving average” (see also p. 234)? Use 166.63, the mean of all 36 months, as the initial forecast for Jan. 1998 for both exponential smoothing forecasts.
Salvatore’s Chapter 7:
a. Discussion Questions: 3, 11, and 12.
b. Problems: 4, 12, and 13.
Note:
1. P4: Ms. Smith should hire workers as long as their marginal revenue product (MRP) exceeds their marginal resource cost (MRC) and until MRP=MRC.
MRP=MR x MP = P x MP = $10 x MP (use information in the problem to calculate MP). MRC=wages=$40.
2. P12(a): Calculate Q when L=1and K=1, and L=2 and K=2. Then compare and answer the question about the returns to scale.
3. P12(b): Given K=1, show the change in Q if L changes from 1 to 2 and 2 to 3. Answer the question about diminishing returns.
4. P13(a): See figure (7-4) on page 276.
DataSee comments at the right of the data set.IDSalaryCompaMidpointAgePerformance RatingServiceGenderRaiseDegreeGender1Grade8231.000233290915.80FAThe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 10220.956233080714.70FANote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.11231.00023411001914.80FA14241.04323329012160FAThe column labels in the table mean:15241.043233280814.90FAID – Employee sample number Salary – Salary in thousands 23231.000233665613.31FAAge – Age in yearsPerformance Rating – Appraisal rating (Employee evaluation score)26241.043232295216.21FAService – Years of service (rounded)Gender: 0 = male, 1 = female 31241.043232960413.90FAMidpoint – salary grade midpoint Raise – percent of last raise35241.043232390415.31FAGrade – job/pay gradeDegree (0= BS\BA 1 = MS)36231.000232775314.31FAGender1 (Male or Female)Compa - salary divided by midpoint37220.956232295216.21FA42241.0432332100815.70FA3341.096313075513.60FB18361.1613131801115.61FB20341.0963144701614.81FB39351.129312790615.51FB7411.0254032100815.70FC13421.0504030100214.71FC22571.187484865613.80FD24501.041483075913.81FD45551.145483695815.20FD17691.2105727553130FE48651.1405734901115.31FE28751.119674495914.41FF43771.1496742952015.51FF19241.043233285104.61MA25241.0432341704040MA40251.086232490206.30MA2270.870315280703.90MB32280.903312595405.60MB34280.903312680204.91 ...
Ash bus 308 week 2 problem set new
This document provides instructions for a problem set analyzing employee salary data. It instructs the user to copy salary and employee data into their assignment file. It then provides guidance on using descriptive statistics and t-tests to analyze differences in compensation ratios between male and female employees. The goal is to determine if there is statistical evidence that male and female employees are paid equally for equal work.
Ash bus 308 week 2 problem set new
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1Create a correlation table for the variables in our data set. (Us.docx
1
Create a correlation table for the variables in our data set. (Use analysis ToolPak function Correlation.)
a. Interpret the results.
What variables seem to be important in seeing if we pay males and females equally for equal work?
2
Below is a regression analysis for salary being predicted/explained by the other variables in our sample
(Mid,
age, ees, sr, raise, and deg variables.) (Note: since salary and compa are different ways of
expressing an employee’s salary, we do not want to have both used in the same regression.)
Ho: The regression equation is not significant.
Ha: The regression equation is significant.
Ho: The regression coefficient for each variable is not significant
Ha: The regression coefficient for each variable is significant
Sal
The analysis used Sal as the y (dependent variable) and
SUMMARY OUTPUT
mid, age, ees, sr, g, raise, and deg as the dependent
variables (entered as a range).
Regression Statistics
Multiple R
0.99215498
R Square
0.9843715
Adjusted R Square
0.98176675
Standard Error
2.59277631
Observations
50
ANOVA
df
SS
MS
F
Significance F
Regression
7
17783.7
2540.52
377.914
8.44043E-36
Residual
42
282.345
6.72249
Total
49
18066
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
-4.009
3.775
-1.062
0.294
-11.627
3.609
-11.627
3.609
Mid
1.220
0.030
40.674
0.000
1.159
1.280
1.159
1.280
Age
0.029
0.067
0.439
0.663
-0.105
0.164
-0.105
0.164
EES
-0.096
0.047
-2.020
0.050
-0.191
0.000
-0.191
0.000
SR
-0.074
0.084
-0.876
0.386
-0.244
0.096
-0.244
0.096
G
2.552
0.847
3.012
0.004
0.842
4.261
0.842
4.261
Raise
0.834
0.643
1.299
0.201
-0.462
2.131
-0.462
2.131
Deg
1.002
0.744
1.347
0.185
-0.500
2.504
-0.500
2.504
Interpretation:
Do you reject or not reject the regression null hypothesis?
Do you reject or not reject the null hypothesis for each variable?
What is the regression equation, using only significant variables if any exist?
What does result tell us about equal pay for equal work for males and females?
3
Perform a regression analysis using compa as the dependent variable and the same independent
variables as used in question 2.
Show the result, and interpret your findings by answering the same questions.
Note: be sure to include the appropriate hypothesis statements.
4
Based on all of your results to date, is gender a factor in the pay practices of this company?
Why or why not?
Which is the best variable to use in analyzing pay practices - salary or compa?
Why?
.
Week 3 Lecture 9 Effect Size When we reject the null h.docx
Week 3 Lecture 9
Effect Size
When we reject the null hypothesis with an ANOVA test, we have two questions that
arise. The first, which pair of means differs significantly, we have dealt with already. The
second question, similar to what we asked with the t-test null hypothesis rejection is; what
caused the rejection, the sample size or the variable interactions? This question is again
answered using an effect size measure.
Recall that the effect size measure shows how likely the variable interaction caused the
null hypothesis rejection. Large values lead us to say the variables caused the outcome, while
small values lead us to say the outcome has little to no practical significance as the sample size
was the most likely cause of the rejection of the null.
With the single factor ANOVA, the effect size measure is eta squared, and equals the
SS(between)/SS(total) (Tanner & Youssef-Morgan, 2013). For our salary example in Lecture 8,
eta squared equals 17686.02 (SS(between) / 18066 (SS(total) = 0.979 (rounded). Eta squared
effect size measures have different interpretation values than Cohen’s d (from the t-
test). According to Nandy (2012), a small eta squared effect size has a value of 0.01, a medium
of 0.06, and a large value of 0.14 or more. This means we have a large effect size, and the
variables of salary and grade interaction are the most likely cause of our rejecting the null
hypothesis rather than the sample size.
Side note: Eta squared can also be interpreted as the percent of “differences between
group scores that can be explained by the independent variable” (Tanner & Youssef-Morgan,
2013, p. 123). This is consistent with our saying the variable interactions caused the outcome.
Different Forms of ANOVA
Just as the t-test has several forms, so does the ANOVA test. Excel has three versions
available. While we will focus only on the single factor test, a brief description of the other two
versions will be presented.
ANOVA: Two factor without replication
The ANOVA – two factor without replication tests mean differences from two different
variables at the same time. If we are interested in knowing if the mean salary differs by grade
and also by gender, we can perform one two-factor test rather than two separate tests. As
mentioned in lecture two for this week, this is more efficient and maintains our desired alpha
significance level.
Excel Example. To test the mean salaries by grade and gender at the same time, we
would set up our hypothesis test as follows.
Step 1: Ho1: All salary means are equal across grades.
Ha1: At least one mean differs.
Ho2: All gender (male and female) means are equal.
Ha2: At least one mean differs.
Note that in this test, we need to have a hypothesis statement pair for each variable being
tested.
Step 2: ANOVA: Two sample without replication.
Step 3: Reject the null hypothesis if the p-value is < alpha = .05.
Step 4: Perform the test. W.
Statistical Significance Tests.pptx
Statistical tests help justify if sample results can be applied to a population. ANOVA compares group means and is preferred over t-tests for 3+ groups. It calculates variation between and within groups to obtain an F-ratio. If the F-ratio exceeds its critical value, the null hypothesis that group means are equal is rejected, showing group means differ significantly. Two-way ANOVA extends this to consider two factors' influence, computing interaction effects between factors.
Excel Files AssingmentsCopy of Student_Assignment_File.11.01..docx
Excel Files Assingments/Copy of Student_Assignment_File.11.01.2016.xlsx
DataIDSalaryCompa-ratioMidpointAgePerformance RatingServiceGenderRaiseDegreeGender1GradeCopy Employee Data set to this page.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 serviceGender: 0 = male, 1 = female Midpoint – salary grade midpoint Raise – percent of last raiseGrade – job/pay gradeDegree (0= BS\BA 1 = MS)Gender1 (Male or Female)Compa-ratio - salary divided by midpoint
Week 2This assignment covers the material presented in weeks 1 and 2.Six QuestionsBefore starting this assignment, make sure the the assignment data from the Employee Salary Data Set file is copied over to this Assignment file.You can do this either by a copy and paste of all the columns or by opening the data file, right clicking on the Data tab, selecting Move or Copy, and copying the entire sheet to this file(Weekly Assignment Sheet or whatever you are calling your master assignment file).It is highly recommended that you copy the data columns (with labels) and paste them to the right so that whatever you do will not disrupt the original data values and relationships.To Ensure full credit for each question, you need to show how you got your results. For example, Question 1 asks for several data values. If you obtain them using descriptive statistics,then the cells should have an "=XX" formula in them, where XX is the column and row number showing the value in the descriptive statistics table. If you choose to generate each value using fxfunctions, then each function should be located in the cell and the location of the data values should be shown.So, Cell D31 - as an example - shoud contain something like "=T6" or "=average(T2:T26)". Having only a numerical value will not earn full credit.The reason for this is to allow instructors to provide feedback on Excel tools if the answers are not correct - we need to see how the results were obtained.In starting the analysis on a research question, we focus on overall descriptive statistics and seeing if differences exist. Probing into reasons and mitigating factors is a follow-up activity.1The first step in analyzing data sets is to find some summary descriptive statistics for key variables. Since the assignment problems willfocus mostly on the compa-ratios, we need to find the mean, standard deviations, and range for our groups: Males, Females, and Overall.Sorting the compa-ratios into male and females will require you copy and paste the Compa-ratio and Gender1 columns, and then sort on Gender1.The values for age, performance rating, and service are prov ...
Chapter 18 – Pricing Setting in the Business WorldThere are few .docx
Chapter 18 – Pricing Setting in the Business World
There are few Methods for setting pricing – costs methods vs demand methods
Formulas considering costs and mark up will help you to do the Problem set assignment:
1. Markup for setting prices (Mark up $ = SP-CP); MARK UP % = (MU $/SP) X100)
Formula for setting price with the markup method
SP = Cost/(1- Markup %)
Example - retailer buys A hat for $15 and wants a 40% markup, his selling price would be….
SP = 15/(1-.40)=.60
= $25.00
2. Understand Role of different costs – fixed, variable, total costs and average costs
3. What is the breakeven point? Formula for calculating the Break Even point.
BE = Total Fixed Cost/Fixed cost contribution
Fixed Cost Contribution=Price – variable cost
4. Average Cost = when there are many flavors/types of the same product, producer determines average cost and then add the mark up to set a common selling price.
.ANOVA
Analysis of Variance is a method of testing the equality of three or more population
means by analyzing sample variance.
One-Way ANOVA
The one-way ANOVA is used to compare three or more population means when there is
one factor of interest.
Requirements
The populations have distributions that are approximately normal.
The populations have the same variance.
The samples are simple random samples of quantitative data.
The samples are independent of each other.
The different samples are from populations that are categorized in only one.
way
One-Way ANOVA is a hypothesis test. There are seven steps for a hypothesis test.
Example
A professor at a local University believes there is a relationship between head size and
the major of the students in her biostatistics classes. She takes a random sample of 20
students from each of three classes and records their major and head circumference.
The data are shown in the following table.
Step 1: State the null hypothesis.
Mean 1 equals mean 2 equals mean 3 equals mean 4.
Step 2: State the Alternative hypothesis.
At least one mean is different.
Step 3: State the Level of Significance.
The level of significance is 0.05.
Step 4: State the test statistic.
variance between samples
variance within samples
F
The test statistic follows the F distribution which has two degrees of freedom, one for
the numerator and one for the denominator.
The calculations for the test statistic are complicated, so a software program is
generally used for the calculations. We will be using Microsoft Excel for this example.
Step 5: Calculate
The calculations are done in Microsoft Excel using the data analysis toolpak. Enter the
data into the spread sheet as shown here. Click on data and the data analysis tookpak
button is on the right.
When you click on the button a dialogue box appears.
Choose ANOVA One Factor. Then another dialogue box appears.
Input range is where the data is in the table. Be sure to put a check in the box for labels
in.
The exam is composed of two parts vocabulary and short essay. You.docx
The exam is composed of two parts: vocabulary and short essay. Your answers must be typed using standard format 12 inch font, Times New Roman with one inch margins.The maximum points you can earn on this exam are 100 points. For every day late 5 points will be deducted. Good luck!
Part I: Vocabulary (40 points)
Briefly define the following terms used by the philosophers we studied and discussed in class and lecture:
1. Hip Hop’s 5 Elements
2. Sociogeny
3. Nihilism
4. Racial Identity
5. Ethnic Identity
6. Asymmetrical Invisibility
7. Prohibited Actions
8. Permissible Actions
9. Obligatory Actions
10. Supererogatory Actions
11. Classical Racialism
12. Critical Racialism
13. Primary Goods
14. Social Goods
15. Objectification
16. The gaze
17. Gender Performativity
18. Authenticity
19. Binary thinking
20. Eminem Enima
Part II: Short Essay (60 points)
Answer the question below. Do not write a title page or works cited page. Do not go over 3 double-spaced pages. This exercise is asking you to synthesize different views, put them in conversation with each other and draw out implications of the author’s arguments.
1. 1. Do Lil Kim’s raps challenge sexist binaries according to Kathryn Gines and Cheryl Keyes?
You must support your interpretation of how these philosophers respond to this question by providing some direct quotes from their articles with MLA style parenthetical citations. Please quote sparingly and be very selective in the few short quotes you use. No large block quotes that take up a lot of space or a flurry of little quotes that drown out your own words/voice. Also remember to be mindful of drawing out the implications of a quote. Interpret what it means because just citing a piece of textual evidence is not always sufficient. In an explanation you have to briefly unpack the quote so the reader can understand the force of the argument.
Top of Form
Bottom of Form
Sheet1Score:Week 3ANOVA and Paired T-testAt this point we know the following about male and female salaries.a.Male and female overall average salaries are not equal in the population.b.Male and female overall average compas are equal in the population, but males are a bit more spread out.c.The male and female salary range are almost the same, as is their age and service.d. Average performance ratings per gender are equal.Let's look at some other factors that might influence pay - education(degree) and performance ratings.<1 point>1Last week, we found that average performance ratings do not differ between males and females in the population.Now we need to see if they differ among the grades. Is the average performace rating the same for all grades?(Assume variances are equal across the grades for this ANOVA.)You can use these columns to place grade Perf Ratings if desired.ABCDEFNull Hypothesis:Alt. Hypothesis:Place B17 in Outcome range box.Interpretation:What is the p-value:Is P-value < 0.05?Do we REJ or Not reject the null?If the null hypothesis was rejected, what is the ...
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseD.docx
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GrStudents: Copy the Student Data file data values into this sheet to assist in doing your weekly assignments.157.71.012573485805.70METhe 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)? 227.80.897315280703.90MBNote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.3341.096313075513.61FB459.21.03857421001605.51METhe column labels in the table mean:549.51.0314836901605.71MDID – Employee sample number Salary – Salary in thousands 675.71.1306736701204.51MFAge – Age in yearsPerformance Rating - Appraisal rating (employee evaluation score)741.71.0434032100815.71FCService – Years of service (rounded)Gender – 0 = male, 1 = female 823.41.018233290915.81FAMidpoint – salary grade midpoint Raise – percent of last raise980.81.206674910010041MFGrade – job/pay gradeDegree (0= BS\BA 1 = MS)1023.61.027233080714.71FAGender1 (Male or Female)Compa - salary divided by midpoint1123.61.02423411001914.81FA1266.91.1745752952204.50ME1341.61.0414030100214.70FC1421.50.93623329012161FA1524.41.059233280814.91FA16390.975404490405.70MC1768.81.2075727553131FE1834.91.1263131801115.60FB1923.21.008233285104.61MA20361.1603144701614.80FB2175.31.1246743951306.31MF2256.71.182484865613.81FD2322.60.984233665613.30FA2451.51.072483075913.80FD2525.51.1092341704040MA2622.90.994232295216.20FA2743.51.088403580703.91MC2874.41.111674495914.40FF2973.51.097675295505.40MF3045.70.9524845901804.30MD3123.71.031232960413.91FA3226.90.867312595405.60MB3355.10.967573590905.51ME34280.904312680204.91MB3521.90.953232390415.30FA3623.71.032232775314.30FA3723.21.010232295216.20FA3857.61.0105745951104.50ME3934.31.108312790615.50FB4024.41.062232490206.30MA4140.51.012402580504.30MC4223.31.0122332100815.71FA4377.21.1526742952015.50FF4456.90.9995745901605.21ME4557.71.202483695815.21FD4665.41.1485739752003.91ME4756.80.997573795505.51ME4859.71.0485734901115.31FE4962.41.0955741952106.60ME5056.50.9925738801204.60ME
Week 1Week 1.Measurement and Description - chapters 1 and 2The goal this week is to gain an understanding of our data set - what kind of data we are looking at, some descriptive measurse, and a look at how the data is distributed (shape).1Measurement issues. Data, even numerically coded variables, can be one of 4 levels - nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, asthis impact the kind of analysis we can do with the data. For example, descriptive statistics such as means can only be done on interval or ratio level data.Please list under each label, the variables in our data set that belong in each group.NominalOrdinalIntervalRatiob.For each variable that you did not call ratio, why did you make that decision?2The first step in analyzing data sets is to find some summary descriptive statistics for key variables.For salary, compa, age, .
Week 2 – Lecture 3 Making judgements about differences bet.docx
Week 2 – Lecture 3
Making judgements about differences between group statistics is one of the most
powerful things that statistics can do for us. It is also one of the most counter-intuitive things
that we need to master in the class.
Lecture 1 introduced the hypothesis testing procedure used in statistical testing. Lecture
2 examined how to set up, perform, and interpret the F test for variance equality. This lecture
will focus on t-tests for testing mean equality. Again, these examples will use the compa-ratio
variable, while the homework should use the Salary variable.
The T-Test
While we test for variance equality with an F test, we use the T-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.
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 the second reason for performing the F-test first.)
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 students, preference 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).
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.
Question 2
The second question for this week asks about salary mean equality between males and
females. The data and test se.
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 ...
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See comments at the right of the data set..docx
See comments at the right of the data set.
ID
Salary
Compa
Midpoint
Age
Performance Rating
Service
Gender
Raise
Degree
Gender1
Grade
8
23
1.000
23
32
90
9
1
5.8
0
F
A
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)?
10
22
0.956
23
30
80
7
1
4.7
0
F
A
Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.
11
23
1.000
23
41
100
19
1
4.8
0
F
A
14
24
1.043
23
32
90
12
1
6
0
F
A
The column labels in the
table mean:
15
24
1.043
23
32
80
8
1
4.9
0
F
A
ID – Employee sample number
Salary – Salary in thousands
23
23
1.000
23
36
65
6
1
3.3
1
F
A
Age – Age in years
Performance Rating
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26
24
1.043
23
22
95
2
1
6.2
1
F
A
Service – Years of service (rounded)
Gender:
0 = male, 1 = female
31
24
1.043
23
29
60
4
1
3.9
0
F
A
Midpoint – salary grade midpoint
Raise – percent of last raise
35
24
1.043
23
23
90
4
1
5.3
1
F
A
Grade – job/pay grade
Degree (0= BS\BA 1 = MS)
36
23
1.000
23
27
75
3
1
4.3
1
F
A
Gender1 (Male or Female)
Compa - salary divided by midpoint
37
22
0.956
23
22
95
2
1
6.2
1
F
A
42
24
1.043
23
32
100
8
1
5.7
0
F
A
3
34
1.096
31
30
75
5
1
3.6
0
F
B
18
36
1.161
31
31
80
11
1
5.6
1
F
B
20
34
1.096
31
44
70
16
1
4.8
1
F
B
39
35
1.129
31
27
90
6
1
5.5
1
F
B
7
41
1.025
40
32
100
8
1
5.7
0
F
C
13
42
1.050
40
30
100
2
1
4.7
1
F
C
22
57
1.187
48
48
65
6
1
3.8
0
F
D
24
50
1.041
48
30
75
9
1
3.8
1
F
D
45
55
1.145
48
36
95
8
1
5.2
0
F
D
17
69
1.210
57
27
55
3
1
3
0
F
E
48
65
1.140
57
34
90
11
1
5.3
1
F
E
28
75
1.119
67
44
95
9
1
4.4
1
F
F
43
77
1.149
67
42
95
20
1
5.5
1
F
F
19
24
1.043
23
32
85
1
0
4.6
1
M
A
25
24
1.043
23
41
70
4
0
4
0
M
A
40
25
1.086
23
24
90
2
0
6.3
0
M
A
2
27
0.870
31
52
80
7
0
3.9
0
M
B
32
28
0.903
31
25
95
4
0
5.6
0
M
B
34
28
0.903
31
26
80
2
0
4.9
1
M
B
16
47
1.175
40
44
90
4
0
5.7
0
M
C
27
40
1.000
40
35
80
7
0
3.9
1
M
C
41
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5
0
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47
0.979
48
36
90
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1
M
D
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34
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66
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100
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5.5
1
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1.052
57
52
95
22
0
4.5
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33
64
1.122
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1
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38
56
0.982
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45
95
11
0
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60
1.052
57
45
90
16
0
5.2
1
M
E
46
65
1.140
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39
75
20
0
3.9
1
M
E
47
62
1.087
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37
95
5
0
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M
E
49
60
1.052
57
41
95
21
0
6.6
0
M
E
50
66
1.157
57
38
80
12
0
4.6
0
M
E
6
76
1.134
67
36
70
12
0
4.5
1
M
F
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77
1.149
67
49
100
10
0
4
1
M
F
21
76
1.134
67
43
95
13
0
6.3
1
M
F
29
72
1.074
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52
95
5
0
5.4
0
M
F
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2
Popular Culture Review - Volume 26, Number 2 - Summer 2015
W
hen it premiered on South Korean television in 2009, the Korean
drama (or “K-drama”) Boys Over Flowers became a ratings
sensation, mostly among females between their mid-teens to thirties
(AGB Neilsen Media Research, n.d.). Originally a Japanese manga (comic
book), the Korean television version became the third iteration of the story
of teenage love and romance, following Taiwanese and Japanese television
versions. Boys.
See details below with a minimum of 8 scholarly referencesKey .docx
See details below with a minimum of 8 scholarly references
Key Assignment Final Draft
The key assignment final draft should include a comprehensive transformational change management plan
Transformational Change Management Plan
Submit your finalized Plan components I-IV.
Create the following new material for Week 5:
Week 5: Implementation Plan (400-600 words)
Include the following context in the implementation plan:
Major implementation steps
Key criteria for success
Summary of Weeks 1-5
Rollout: Any risk considerations to the implementation
The completed document with content should be finalized in terms of grammar, formatting, references, all necessary sections, flow, and complete content.
.
See attatched for part 1Part 2Develop a 10- to 12-slide Micr.docx
See attatched for part 1
Part 2
Develop
a 10- to 12-slide Microsoft
®
PowerPoint
®
presentation with speaker notes describing the physical, psychosocial, cognitive, and moral stages of development experienced within your assigned life stage.
Note
. Some areas may not be applicable, based on the life stage.
Click
the Assignment Files tab to submit your assignment.
.
SEE ATTACHMENTRefer to the Intervention Series #38 Anthony” vid.docx
SEE ATTACHMENT
Refer
to the “Intervention Series #38 Anthony” video located in this week’s Electronic Reserve Readings.
Write
a 1,050-to 1,400-word integrative paper that explores how Anthony’s addiction affects his life, his family’s life, and the community he lives in.
Include
at least two peer-reviewed scholarly articles that support your discussion or arguments.
Format
your paper consistent with APA guidelines.
.
SEE Attachment for subject MatterMust be eight to ten double-spa.docx
SEE Attachment for subject Matter
Must be eight to ten double-spaced pages in length, and formatted according to APA style.
Must include a title page with the following:
Title of paper
Student’s name
Course name and number
Instructor’s name
Date submitted
Must begin with an introductory paragraph that has a succinct thesis statement.
Must address the topic of the paper with critical thought.
Must end with a conclusion that reaffirms your thesis.
Must use at least six scholarly sources, not including the textbook.
Must document all sources in APA style.
Must include a separate reference page, formatted according to APA style
.
See attachment for my proposal and exampleUse the completed prio.docx
See attachment for my proposal and example
Use the completed priority analysis, your organization background information, and your knowledge thus far gained in project management to collectively prioritize the projects and decide which project is to be undertaken first.
Your CLC team should submit the following:
A completed priority analysis
Determination of which project is to be undertaken first, along with a summary of why the project was chosen, including an explanation of the relationship between the project and the organization’s mission, vision, and objectives
.
Securities Fraud Please respond to the followingFrom the e-Acti.docx
"Securities Fraud" Please respond to the following:
From the e-Activity, examine how the Bernie Madoff organization was dismantled and give your opinion on whether you believe he was treated fairly during this process.
Give your opinion on whether or not you believe the Securities and Exchange Commission failed in not detecting Bernie Madoff crime earlier. Support your answer.
.
Security Standards Please respond to the followingA number of .docx
"Security Standards" Please respond to the following:
A number of organizations exist to define information security standards. Explain the importance of standards organizations with regard to both information systems and information systems security. Provide a rationale for your response.
From the e-Activity, determine two (2) specific concerns that you believe exist for cloud deployments, and ascertain whether or not data breaches, such as the Snowden Incident have heightened concerns. Justify your answer.
.
Section Four Answer all questions thoroughly and hand-write on loos.docx
Section Four: Answer all questions thoroughly and hand-write on loose-leaf paper.
1. Describe the atmosphere at Buna. What does Wiesel imply by “well-dressed”?
2. Why do the officers take an interest in the young boys as they come into the camp? Why
do you think Wiesel allow the reader to make assumptions before relaying the truth? How
does the parenthetical insertion add to the fact that
Night
is a memoir rather than a piece
of fiction?
3. Explain the conflict that ensues over Eliezer’s shoes. How does the conflict negate everything the men and boys have been told about surviving in the concentration camps?
4. How is the situation with the shoes ironic?
5. How is animal imagery used in the first few pages of Section Four? What is the effect of
this imagery?
6. Who is Juliek? How does Eliezer meet him? Describe the relationship between them.
7. Throughout the memoir Eliezer and the other prisoners are treated like animals. Cite an
example of another image that Wiesel uses to describe how they were treated.
8. Who does Eliezer become friends with at the camp? What connection does he find between their lives? Explain whether or not you believe it is impossible to have true friends in the concentration camps.
9. Who is Alphonse? Explain the benefit of having Alphonse as a leader of the block. Why
do you think Wiesel does not spend much time talking about Alphonse?
10. Why is Eliezer summoned to the dentist? How does Eliezer describe the dentist? What is
ironic about his appearance?
11. How does Eliezer escape the dentist the first time? What does the dentist tell him?
12. How does Eliezer escape the dentist permanently? Why do you think Elie Wiesel includes
the account of the dentist in his memoir when he does not lose his teeth to the dentist?
13. Explain how the following scene functions as foreshadowing: “In fact, I was pleased with what was happening to him: my gold crown was safe. It could be useful to me one day, to buy something, some bread or even time to live.”
14. After reading about the interchange between Eliezer and the young French woman, characterize the young woman.
15. In the memoir, a majority of the animal imagery is associated with the Jews. How does
Wiesel alter the animal imagery in the description of Eliezer’s beating? Explain the purpose
behind the change in images.
16. How does Wiesel interrupt the narration in the passage that begins with, “MANY YEARS
LATER, in Paris, I sat in the Métro ...”? What is the effect of the interruption?
17. Cite the simile that Eliezer uses when describing the beating that his father receives. What
image does it create for the reader?
18. Describe Eliezer’s internal conflict while he watches his father getting beaten.
19. Read the confrontation between Franek and Eliezer. Using examples from the text, explain how Franek is different from the dentist who first tried to take Eliezer’s tooth and how
Eliezer tries to escape the situation differently than the f.
Security Support Responsibilities Please respond to the following.docx
"Security Support Responsibilities" Please respond to the following:
Imagine you are the CIO of an organization. Construct an outline of four ongoing responsibilities that the digital forensics personnel must complete each week. Provide a possible scenario for how each responsibility may be performed to fulfill the forensics’ needs of an organization.
Compare the responsibilities you listed above with those of an IT security professional. Give your opinion on how responsibilities of digital forensics personnel and IT security professional are similar and in which ways are they different.
.
Section 1 (200 words)In cognition, one of the major areas of discu.docx
Section 1 (200 words)
In cognition, one of the major areas of discussion is the influence of nature and nurture. What is the concept of nature–nurture? Why is it important in the study of cognition?
Section 2 (200 words)
--Does the nature and nurture debate affect other disciplines beyond psychology?
--Include in-text citations
--Due 3/01/15 @ 11:15am Central Time
.
Second Wave Feminism, gained strength during the 1970s. For this .docx
"Second Wave Feminism", gained strength during the 1970's. For this essay assignment, research and write about a popular television show from the 1970's, which reflected the changing role of women in American culture. Discuss your findings.
This paper should be 2 pages, in APA style. One scholarly article as a minimum should be included in your essay.
.
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See discussions, stats, and author profiles for this publicati.docx
See details below with a minimum of 8 scholarly referencesKey .docx
See details below with a minimum of 8 scholarly referencesKey .docx
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See attatched for part 1Part 2Develop a 10- to 12-slide Micr.docx
SEE ATTACHMENTRefer to the Intervention Series #38 Anthony” vid.docx
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ScoreWeek 3ANOVA and Paired T-test.docx
- 1. Score: Week 3 ANOVA and Paired T-test
- 2. At this point we know the following about male and female salaries.
- 3. a. Male and female overall average salaries are not equal in the population.
- 4. b. Male and female overall average compas are equal in the population, but males are a bit more spread out. c. The male and female salary range are almost the same, as is their age and service.
- 5. d. Average performance ratings per gender are equal.
- 6. Let's look at some other factors that might influence pay - education(degree) and performance ratings.
- 7. <1 point> 1 Last week, we found that average performance ratings do not differ between males and females in the population.
- 8. Now we need to see if they differ among the grades. Is the average performace rating the same for all grades? (Assume variances are equal across the grades for this ANOVA.) You can use these columns to place grade Perf Ratings if desired.
- 10. Alt. Hypothesis:
- 11. Place B17 in Outcome range box.
- 27. Interpretation: What is the p-value:
- 28. Is P-value < 0.05?
- 29. Do we REJ or Not reject the null?
- 30. If the null hypothesis was rejected, what is the effect size value (eta squared):
- 31. Meaning of effect size measure:
- 32. What does that decision mean in terms of our equal pay question:
- 34. <1 point> 2 While it appears that average salaries per each grade differ, we need to test this assumption.
- 35. Is the average salary the same for each of the grade levels? (Assume equal variance, and use the analysis toolpak function ANOVA.)
- 36. Use the input table to the right to list salaries under each grade level.
- 37. Null Hypothesis: If desired, place salaries per grade in these columns
- 40. Place B55 in Outcome range box.
- 56. What is the p-value: Is P-value < 0.05?
- 57. Do you reject or not reject the null hypothesis:
- 58. If the null hypothesis was rejected, what is the effect size value (eta squared):
- 59. Meaning of effect size measure:
- 60. Interpretation:
- 62. <1 point> 3 The table and analysis below demonstrate a 2-way ANOVA with replication. Please interpret the results.
- 63. BA MA Ho: Average compas by gender are equal
- 64. Male 1.017 1.157 Ha: Average compas by gender are not equal 0.870
- 65. 0.979 Ho: Average compas are equal for each degree 1.052 1.134 Ha: Average compas are not equal for each degree
- 66. 1.175 1.149 Ho: Interaction is not significant
- 67. 1.043 1.043 Ha: Interaction is significant 1.074 1.134
- 69. 0.903 1.122
- 70. 0.982 0.903 Anova: Two-Factor With Replication 1.086 1.052
- 72. 1.052 1.087 Male
- 82. Count 24 24
- 83. Sum 25.14 25.687
- 88. df MS F P-value F crit Sample 0.00225502 1 0.002255 0.383482 0.538939 4.061706 (This is the row variable or gender.)
- 89. Columns 0.00623352 1 0.006234 1.060054 0.30883 4.061706 (This is the column variable or Degree.)
- 91. 44 0.00588
- 94. Interpretation:
- 95. For Ho: Average compas by gender are equal Ha: Average compas by gender are not equal
- 96. What is the p-value: Is P-value < 0.05?
- 97. Do you reject or not reject the null hypothesis:
- 98. If the null hypothesis was rejected, what is the effect size value (eta squared):
- 99. Meaning of effect size measure:
- 100. For Ho: Average compas are equal for all degrees Ha: Average compas are not equal for all grades
- 101. What is the p-value:
- 102. Is P-value < 0.05? Do you reject or not reject the null hypothesis:
- 103. If the null hypothesis was rejected, what is the effect size value (eta squared):
- 104. Meaning of effect size measure:
- 105. For: Ho: Interaction is not significant Ha: Interaction is significant
- 106. What is the p-value:
- 107. Is P-value < 0.05?
- 108. Do you reject or not reject the null hypothesis: If the null hypothesis was rejected, what is the effect size value
- 109. (eta squared): Meaning of effect size measure:
- 111. What do these decisions mean in terms of our equal pay question:
- 113. Place data values in these columns
- 114. <1 point> 4 Many companies consider the grade midpoint to be the "market rate" - what is needed to hire a new employee. Salary Midpoint Does the company, on average, pay its existing employees at or above the market rate?
- 119. Statistical test to use:
- 120. Place the cursor in B160 for test.
- 134. What is the p-value:
- 135. Is P-value < 0.05?
- 136. What else needs to be checked on a 1-tail in order to reject the null?
- 137. Do we REJ or Not reject the null? If
- 138. the null hypothesis was rejected, what is the effect size value: NA Meaning of effect size measure: NA
- 139. Interpretation:
- 141. <2 points> 5. Using the results up thru this week, what are your conclusions about gender equal pay for equal work at this point?