DataIDSalCompaMidAgeEESSRGRaiseDegGen1Gr1581.017573485805.70METhe 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)?2270.870315280703.90MBNote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.3341.096313075513.61FB4661.15757421001605.51METhe column labels in the table mean:5470.9794836901605.71MDID – Employee sample numberSal – Salary in thousands6761.1346736701204.51MFAge – Age in yearsEES – Appraisal rating (Employee evaluation score)7411.0254032100815.71FCSER – Years of serviceG – Gender (0 = male, 1 = female)8231.000233290915.81FAMid – salary grade midpointRaise – percent of last raise9771.149674910010041MFGrade – job/pay gradeDeg (0= BS\BA 1 = MS)10220.956233080714.71FAGen1 (Male or Female)Compa - salary divided by midpoint11231.00023411001914.81FA12601.0525752952204.50ME13421.0504030100214.70FC14241.04323329012161FA15241.043233280814.91FA16471.175404490405.70MC17691.2105727553131FE18361.1613131801115.60FB19241.043233285104.61MA20341.0963144701614.80FB21761.1346743951306.31MF22571.187484865613.81FD23231.000233665613.30FA24501.041483075913.80FD25241.0432341704040MA26241.043232295216.20FA27401.000403580703.91MC28751.119674495914.40FF29721.074675295505.40MF30491.0204845901804.30MD31241.043232960413.91FA32280.903312595405.60MB33641.122573590905.51ME34280.903312680204.91MB35241.043232390415.30FA36231.000232775314.30FA37220.956232295216.20FA38560.9825745951104.50ME39351.129312790615.50FB40251.086232490206.30MA41431.075402580504.30MC42241.0432332100815.71FA43771.1496742952015.50FF44601.0525745901605.21ME45551.145483695815.21FD46651.1405739752003.91ME47621.087573795505.51ME48651.1405734901115.31FE49601.0525741952106.60ME50661.1575738801204.60ME Week 1Week 1.Describing the data.1. Using the Excel Analysis ToolPak function descriptive statistics, generate descriptive statistics for the salary data.Which variables does this function not work properly for, even though we have some excel generated results?2. Sort the data by Gen or Gen 1 (into males and females) and find the mean and standard deviation for each gender for the following variables:sal, compa, age, sr and raise.Use the descriptive stats function for one gender and the Fx functions (average and stdev) for the other.3.   What is the probability distribution table for a:a.       Randomly selected person being a male in a specific grade?b.      Randomly selected person being in a specific grade?4. Find:a. The z score for each male salary, based on only the male salaries.b. The z score for each female salary, based on only the female salaries.5. Repeat question 4 for compa for each gender.6.      What conclusions can you make about the issue of male and female pay equality? Are all of the results consistent? If not, why not? Week 2 Week 2Testing means1Is either the male or female salary equal to the overall mean salary?(Two hypotheses tests - 1 sample tests.

1. DataIDSalCompaMidAgeEESSRGRaiseDegGen1Gr1581.017573
485805.70METhe 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)?2270.870315280703.90MBNote: to simplfy the analysis,
we will assume that jobs within each grade comprise equal
work.3341.096313075513.61FB4661.15757421001605.51METh
e column labels in the table
mean:5470.9794836901605.71MDID – Employee sample
numberSal – Salary in thousands6761.1346736701204.51MFAge
– Age in yearsEES – Appraisal rating (Employee evaluation
score)7411.0254032100815.71FCSER – Years of serviceG –
Gender (0 = male, 1 = female)8231.000233290915.81FAMid –
salary grade midpointRaise – percent of last
raise9771.149674910010041MFGrade – job/pay gradeDeg (0=
BSBA 1 = MS)10220.956233080714.71FAGen1 (Male or
Female)Compa - salary divided by
midpoint11231.00023411001914.81FA12601.0525752952204.50
ME13421.0504030100214.70FC14241.04323329012161FA1524
1.043233280814.91FA16471.175404490405.70MC17691.21057
27553131FE18361.1613131801115.60FB19241.043233285104.6
1MA20341.0963144701614.80FB21761.1346743951306.31MF2
2571.187484865613.81FD23231.000233665613.30FA24501.041
483075913.80FD25241.0432341704040MA26241.04323229521
6.20FA27401.000403580703.91MC28751.119674495914.40FF2
9721.074675295505.40MF30491.0204845901804.30MD31241.0
43232960413.91FA32280.903312595405.60MB33641.12257359
0905.51ME34280.903312680204.91MB35241.043232390415.30
FA36231.000232775314.30FA37220.956232295216.20FA38560
.9825745951104.50ME39351.129312790615.50FB40251.086232
490206.30MA41431.075402580504.30MC42241.043233210081
5.71FA43771.1496742952015.50FF44601.0525745901605.21M
E45551.145483695815.21FD46651.1405739752003.91ME47621
.087573795505.51ME48651.1405734901115.31FE49601.052574

2. 1952106.60ME50661.1575738801204.60ME
Week 1Week 1.Describing the data.1. Using the Excel Analysis
ToolPak function descriptive statistics, generate descriptive
statistics for the salary data.Which variables does this function
not work properly for, even though we have some excel
generated results?2. Sort the data by Gen or Gen 1 (into males
and females) and find the mean and standard deviation for each
gender for the following variables:sal, compa, age, sr and
raise.Use the descriptive stats function for one gender and the
Fx functions (average and stdev) for the other.3. What is the
probability distribution table for a:a. Randomly selected
person being a male in a specific grade?b. Randomly
selected person being in a specific grade?4. Find:a. The z score
for each male salary, based on only the male salaries.b. The z
score for each female salary, based on only the female
salaries.5. Repeat question 4 for compa for each gender.6.
What conclusions can you make about the issue of male and
female pay equality? Are all of the results consistent? If not,
why not?
Week 2 Week 2Testing means1Is either the male or female
salary equal to the overall mean salary?(Two hypotheses tests -
1 sample tests)2Are the male and female salaries statistically
equal to each other?3Are the male and female compas equal to
each other?4. If the salary and compa mean tests in questions 3
and 4 provide different equality results,which would be more
appropriate to use in answering the question about salary
equity? Why?5. What other information would you like to
know to answer the question about salary equity between the
genders? Why?
Week 3Week 31. Is the average salary the same for each of
the grade levels? (Assume equal variance, and use the analysis
toolpak function ANOVA.)Set up the input table/range to use as
follows: Put all of the salary values for each grade under the
appropriate grade label.ABCDEF2. The factorial ANOVA
with only 2 variables can be done with the Analysis ToolPak
function 2-Way ANOVA with replication. Set up a data input

3. table like the following:GradeGenderABCDEFMFFor each
empty cell randomly pick a male or female salary from each
grade.Interpret the results. Are the average salaries for each
gender (listed as sample) equal?Are the average salaries for
each grade (listed as column) equal?3. Repeat question 2 for
the compa values.GradeGenderABCDEFMFFor each empty cell
randomly pick a male or female salary from each grade.Interpret
the results. Are the average compas for each gender (listed as
sample) equal?Are the average compas for each grade (listed as
column) equal?4. Pick any other variable you are interested in
and do a simple 2-way ANOVA without replication. Why did
you pick this variable and what do the results show?5. What
are your conclusions about salary equity now?
Week 4Week 4Confidence Intervals and Chi Square (CHs 11 -
12)Q1Q2Let's look at some other factors that might influence
pay.GrDegGen1SalA0F341. Is the probability of having a
graduate degree independent of the grade the employee is
in?A0F41C0F772. Construct a 95% confidence interval on
the mean service for each gender? Do they
intersect?C0F55D1M773. Are males and females distributed
across grades in a similar pattern?D1M604. Do 95%
confidence intervals on the mean length of service for each
gender intersect?5. How do you interpret these results in
light of our equity question?
Week 5Week 5 Correlation and Regression1. Create a
correlation table for the variables in our data set. (Use analysis
ToolPak function Correlation.)2. Create a multiple regression
equation (using the Analysis ToolPak function Regression) to
predict either salary or compa using the mid(a substitute
variable for grade level), age, ees, sr, raise, and deg variables.
(Note: since salary and compa are different ways ofexpressing
an employee’s salary, we do not want to have both used in the
same regression.)3. Based on all of your results to date, is
gender a factor in the pay practices of this company? Why or
why not?4. In looking at equal pay issues across an entire
company, which is a better variable to use – compa or salary?

4. Why?5. Why did the single factor tests and analysis (such as
t and single factor ANOVA tests on salary equality) not provide
a complete answer to our salary equality question?What
outcomes in your life or work might benefit from a multiple
regression examination rather than a simpler one varable test?
DataIDSalCompaMidAgeEESSRGRaiseDegGen1Gr1581.017573
485805.70METhe 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)?2270.870315280703.90MBNote: to simplfy the analysis,
we will assume that jobs within each grade comprise equal
work.3341.096313075513.61FB4661.15757421001605.51METh
e column labels in the table
mean:5470.9794836901605.71MDID – Employee sample
numberSal – Salary in thousands6761.1346736701204.51MFAge
– Age in yearsEES – Appraisal rating (Employee evaluation
score)7411.0254032100815.71FCSER – Years of serviceG –
Gender (0 = male, 1 = female)8231.000233290915.81FAMid –
salary grade midpointRaise – percent of last
raise9771.149674910010041MFGrade – job/pay gradeDeg (0=
BSBA 1 = MS)10220.956233080714.71FAGen1 (Male or
Female)Compa - salary divided by
midpoint11231.00023411001914.81FA12601.0525752952204.50
ME13421.0504030100214.70FC14241.04323329012161FA1524
1.043233280814.91FA16471.175404490405.70MC17691.21057
27553131FE18361.1613131801115.60FB19241.043233285104.6
1MA20341.0963144701614.80FB21761.1346743951306.31MF2
2571.187484865613.81FD23231.000233665613.30FA24501.041
483075913.80FD25241.0432341704040MA26241.04323229521
6.20FA27401.000403580703.91MC28751.119674495914.40FF2
9721.074675295505.40MF30491.0204845901804.30MD31241.0
43232960413.91FA32280.903312595405.60MB33641.12257359
0905.51ME34280.903312680204.91MB35241.043232390415.30
FA36231.000232775314.30FA37220.956232295216.20FA38560
.9825745951104.50ME39351.129312790615.50FB40251.086232

5. 490206.30MA41431.075402580504.30MC42241.043233210081
5.71FA43771.1496742952015.50FF44601.0525745901605.21M
E45551.145483695815.21FD46651.1405739752003.91ME47621
.087573795505.51ME48651.1405734901115.31FE49601.052574
1952106.60ME50661.1575738801204.60ME
Week 1Week 1.Describing the data.1. Using the Excel Analysis
ToolPak function descriptive statistics, generate descriptive
statistics for the salary data.Which variables does this function
not work properly for, even though we have some excel
generated results?2. Sort the data by Gen or Gen 1 (into males
and females) and find the mean and standard deviation for each
gender for the following variables:sal, compa, age, sr and
raise.Use the descriptive stats function for one gender and the
Fx functions (average and stdev) for the other.3. What is the
probability distribution table for a:a. Randomly selected
person being a male in a specific grade?b. Randomly
selected person being in a specific grade?4. Find:a. The z score
for each male salary, based on only the male salaries.b. The z
score for each female salary, based on only the female
salaries.5. Repeat question 4 for compa for each gender.6.
What conclusions can you make about the issue of male and
female pay equality? Are all of the results consistent? If not,
why not?
Week 2 Week 2Testing means1Is either the male or female
salary equal to the overall mean salary?(Two hypotheses tests -
1 sample tests)2Are the male and female salaries statistically
equal to each other?3Are the male and female compas equal to
each other?4. If the salary and compa mean tests in questions 3
and 4 provide different equality results,which would be more
appropriate to use in answering the question about salary
equity? Why?5. What other information would you like to
know to answer the question about salary equity between the
genders? Why?
Week 3Week 31. Is the average salary the same for each of
the grade levels? (Assume equal variance, and use the analysis
toolpak function ANOVA.)Set up the input table/range to use as

6. follows: Put all of the salary values for each grade under the
appropriate grade label.ABCDEF2. The factorial ANOVA
with only 2 variables can be done with the Analysis ToolPak
function 2-Way ANOVA with replication. Set up a data input
table like the following:GradeGenderABCDEFMFFor each
empty cell randomly pick a male or female salary from each
grade.Interpret the results. Are the average salaries for each
gender (listed as sample) equal?Are the average salaries for
each grade (listed as column) equal?3. Repeat question 2 for
the compa values.GradeGenderABCDEFMFFor each empty cell
randomly pick a male or female salary from each grade.Interpret
the results. Are the average compas for each gender (listed as
sample) equal?Are the average compas for each grade (listed as
column) equal?4. Pick any other variable you are interested in
and do a simple 2-way ANOVA without replication. Why did
you pick this variable and what do the results show?5. What
are your conclusions about salary equity now?
Week 4Week 4Confidence Intervals and Chi Square (CHs 11 -
12)Q1Q2Let's look at some other factors that might influence
pay.GrDegGen1SalA0F341. Is the probability of having a
graduate degree independent of the grade the employee is
in?A0F41C0F772. Construct a 95% confidence interval on
the mean service for each gender? Do they
intersect?C0F55D1M773. Are males and females distributed
across grades in a similar pattern?D1M604. Do 95%
confidence intervals on the mean length of service for each
gender intersect?5. How do you interpret these results in
light of our equity question?
Week 5Week 5 Correlation and Regression1. Create a
correlation table for the variables in our data set. (Use analysis
ToolPak function Correlation.)2. Create a multiple regression
equation (using the Analysis ToolPak function Regression) to
predict either salary or compa using the mid(a substitute
variable for grade level), age, ees, sr, raise, and deg variables.
(Note: since salary and compa are different ways ofexpressing
an employee’s salary, we do not want to have both used in the

7. same regression.)3. Based on all of your results to date, is
gender a factor in the pay practices of this company? Why or
why not?4. In looking at equal pay issues across an entire
company, which is a better variable to use – compa or salary?
Why?5. Why did the single factor tests and analysis (such as
t and single factor ANOVA tests on salary equality) not provide
a complete answer to our salary equality question?What
outcomes in your life or work might benefit from a multiple
regression examination rather than a simpler one varable test?