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.

1. ASH BUS 308 Week 2 Problem Set NEW
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Before 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.

2. 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.
1 The first step in analyzing data sets is to find some summary
descriptive statistics for key variables. Since the assignment
problems will focus 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
provided for you for future use, and - if desired - to test your
approach to the compa-ratio answers (see if you can replicate
the values).
You can use either the Data Analysis Descriptive Statistics tool
or the Fx =average and =stdev functions. The range can be
found using the difference between the =max and =min
functions with Fx functions or from Descriptive Statistics.
Suggestion: Copy and paste the compa-ratio data to the right
(Column T) and gender data in column U. If you use Descriptive
statistics, Place the output table in row 1 of a column to the
right. If you did not use Descriptive Statistics, make sure your
cells show the location of the data (Example: =average(T2:T51)
A key issue in comparing data sets is to see if they are
distributed/shaped the same.
At this point we can do this by looking at the probabilities that

3. males and females are distributed in the same way for a grade
levels.
2 Empirical Probability: What is the probability for a:
a. Randomly selected person being in grade E or above?
b. Randomly selected person being a male in grade E or
above?
c. Randomly selected male being in grade E or above?
d. Why are the results different?
3 Normal Curve based probability: For each group (overall,
females, males), what are the values for each question below?:
Make sure your answer cells show the Excel function and
cell location of the data used.
A The probability of being in the top 1/3 of the compa-ratio
distribution.
Note, we can find the cutoff value for the top 1/3 using the
fx Large function: =large(range, value).
Value is the number that identifies the x-largest value.
For the top 1/3 value would be the value that starts the top 1/3
of the range,
For the overall group, this would be the 50/3 or 17th
(rounded), for the gender groups, it would be the 25/3 = 8th
(rounded) value.
i. How nany salaries are in the top 1/3 (rounded to nearest
whole number) for each group?
ii What Compa-ratio value starts the top 1/3 of the range for
each group?

4. iii What is the z-score for this value?
iv. What is the normal curve probability of exceeding this
score?
B How do you interpret the relationship between the data sets?
What does this suggest about our equal pay for equal work
question?
4 Based on our sample data set, can the male and female
compa-ratios in the population be equal to each other?
A First, we need to determine if these two groups have equal
variances, in order to decide which t-test to use.
What is the data input ranged used for this question:
Step 1:
Ho:
Ha:
Step 2:
Decision Rule:
Step 3:
Statistical test:
Why?

5. Step 4: C
Conduct the test - place cell B77 in the output location box.
Step 5: Conclusion and Interpretation
What is the p-value:
Is the P-value < 0.05 (for a one tail test) or 0.025 (for a two tail
test)?
What is your decision:
REJ or NOT reject the null?
What does this result say about our question of variance
equality?
B Are male and female average compa-ratios equal?
(Regardless of the outcome of the above F-test, assume equal
variances for this test.)
What is the data input ranged used for this question:
Step 1:
Ho:
Ha:
Step 2: Decision Rule:
Step 3: Statistical test:

6. Why?
Step 4: Conduct the test - place cell B109 in the output location
box.
Step 5: Conclusion and Interpretation
What is the p-value: Is the P-value < 0.05 (for a one tail test) or
0.025 (for a two tail test)?
What is your decision:
REJ or NOT reject the null?
What does your decision on rejecting the null hypothesis mean?
If the null hypothesis was rejected, calculate the effect size
value:
If the effect size was calculated, what doe the result mean in
terms of why the null hypothesis was rejected?
What does the result of this test tell us about our question on
salary equality?
5 Is the Female average compa-ratio equal to or less than the
midpoint value of 1.00?
This question is the same as:
Does the company, pay its females - on average - at or below the
grade midpoint (which is considered the market rate)?

7. Suggestion: Use the data column T to the right for your null
hypothesis value.
What is the data input ranged used for this question:
Step 1:
Ho:
Ha:
Step 2: Decision Rule:
Step 3: Statistical test: Why?
Step 4: Conduct the test - place cell B162 in the output location
box.
Step 5: Conclusion and Interpretation
What is the p-value: Is the P-value < 0.05 (for a one tail test) or
0.025 (for a two tail test)?
What, besides the p-value, needs to be considered with a one
tail test?
Decision: Reject or do not reject Ho?
What does your decision on rejecting the null hypothesis mean?
If the null hypothesis was rejected, calculate the effect size
value:
If the effect size was calculated, what doe the result mean in

8. terms of why the null hypothesis was rejected?
What does the result of this test tell us about our question on
salary equality?
6 Considering both the salary information in the lectures and
your compa-ratio information, what conclusions can you reach
about equal pay for equal work?
Why - what statistical results support this conclusion?