Export SummaryThis document was exported from Numbers. Each table.docx

Export SummaryThis document was exported from Numbers.
Each table was converted to an Excel worksheet. All other
objects on each Numbers sheet were placed on separate
worksheets. Please be aware that formula calculations may
differ in Excel.Numbers Sheet NameNumbers Table NameExcel
Worksheet NameDataTable 1DataWeek 1Table 1Week 1Week
2Table 1Week 2Week 3Table 1Week 3Week 4Table 1Week 4
DataIDSalaryCompa-ratioMidpoint AgePerformance
RatingServiceGenderRaiseDegreeGender1GradeDo not
manipuilate Data set on this page, copy to another page to make
changes166.81.172573485805.70METhe 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)?
227.40.884315280703.90MBNote: to simplfy the analysis, we
will assume that jobs within each grade comprise equal
work.334.71.118313075513.61FB457.11.00157421001605.51M
EThe column labels in the table
mean:548.11.0024836901605.71MDID – Employee sample
number Salary – Salary in thousands
674.41.1106736701204.51MFAge – Age in yearsPerformance
Rating - Appraisal rating (employee evaluation
score)740.61.0154032100815.71FCService – Years of service
(rounded)Gender – 0 = male, 1 = female
822.90.997233290915.81FAMidpoint – salary grade midpoint
Raise – percent of last raise978.11.166674910010041MFGrade
– job/pay gradeDegree (0= BSBA 1 =
MS)10251.088233080714.71FAGender1 (Male or
Female)Compa-ratio - salary divided by
midpoint1122.40.97623411001914.81FA1258.31.022575295220
4.50ME1342.21.0554030100214.70FC1423.91.04123329012161
FA1524.61.071233280814.91FA1642.61.064404490405.70MC1
768.11.1945727553131FE1835.91.1573131801115.60FB1924.51
.067233285104.61MA20351.1283144701614.80FB21721.07467
43951306.31MF2254.81.142484865613.81FD2323.81.03623366

5613.30FA2452.21.087483075913.80FD2524.41.063234170404
0MA2623.91.037232295216.20FA2737.70.942403580703.91MC
2877.11.150674495914.40FF2976.41.141675295505.40MF3048
1.0014845901804.30MD31241.045232960413.91FA3227.30.881
312595405.60MB3363.41.112573590905.51ME3427.90.899312
680204.91MB3523.61.027232390415.30FA3623.41.0162327753
14.30FA37230.998232295216.20FA38601.0525745951104.50M
E39351.128312790615.50FB40241.045232490206.30MA4144.2
1.106402580504.30MC4222.60.9812332100815.71FA4378.41.1
706742952015.50FF4462.91.1035745901605.21ME4552.51.093
483695815.21FD4657.21.0045739752003.91ME47631.1055737
95505.51ME4863.41.1135734901115.31FE4963.71.1185741952
106.60ME5058.11.0195738801204.60ME
&"Helvetica Neue,Regular"&12&K000000&P
Week 1Week 1: Descriptive Statistics, including
ProbabilityWhile the lectures will examine our equal pay
question from the compa-ratio viewpoint, our weekly
assignments will focus onexamining the issue using the salary
measure.The purpose of this assignmnent is two fold:1.
Demonstrate mastery with Excel tools.2. Develop descriptive
statistics to help examine the question.3. Interpret descriptive
outcomesThe first issue in examining salary data to determine if
we - as a company - are paying males and females equally for
doing equal work is to develop somedescriptive statistics to
give us something to make a preliminary decision on whether
we have an issue or not.1Descriptive Statistics: Develop basic
descriptive statistics for SalaryThe first step in analyzing data
sets is to find some summary descriptive statistics for key
variables. Suggestion: Copy the gender1 and salary columns
from the Data tab to columns T and U at the right.Then use Data
Sort (by gender1) to get all the male and female salary values
grouped together.a. Use the Descriptive Statistics function in
the Data Analysis tab Place Excel outcome in Cell K19to
develop the descriptive statistics summary for the overall
group's overall salary. (Place K19 in output range.)Highlight

the mean, sample standard deviation, and range.b.Using Fx (or
formula) functions find the following (be sure to show the
formula and not just the value in each cell) asked for salary
statistics for each gender:MaleFemaleMean:Sample Standard
Deviation:Range:2Develop a 5-number summary for the overall,
male, and female SALARY variable.For full credit, show the
excel formulas in each cell rather than simply the numerical
answer.OverallMalesFemalesMax3rd QMidpoint1st
QMin3Location Measures: comparing Male and Female
midpoints to the overall Salary data range.For full credit, show
the excel formulas in each cell rather than simply the numerical
answer.Using the entire Salary range and the M and F midpoints
found in Q2MaleFemalea. What would each midpoint's
percentile rank be in the overall range?Use Excel's
=PERCENTRANK.EXC functionb. What is the normal curve z
value for each midpoint within overall range?Use Excel's
=STANDARDIZE function 4Probability Measures: comparing
Male and Female midpoints to the overall Salary data rangeFor
full credit, show the excel formulas in each cell rather than
simply the numerical answer.Using the entire Salary range and
the M and F midpoints found in Q2, findMaleFemalea. The
Empirical Probability of equaling or exceeding (=>) that value
forShow the calculation formula = value/50 or
=countif(range,">="&cell)/50b. The Normal curve Prob of =>
that value for each groupUse "=1-NORM.S.DIST"
function5Conclusions: What do you make of these results?Be
sure to include findings from this week's lectures as well.In
comparing the overall, male, and female outcomes, what
relationship(s) see, to exist between the data sets? What does
this suggest about our equal pay for equal work question?
Week 2Week 2: Identifying Significant Differences - part 1To
Ensure full credit for each question, you need to show how you
got your results. This involves either showing where the data
you used is located or showing the excel formula in each cell.Be

sure to copy the appropriate data columns from the data tab to
the right for your use this week.As with our examination of
compa-ratio in the lecture, the first question we have about
salary between the genders involves equality - are they the same
or different?What we do, depends upon our findings.1As with
the compa-ratio lecture example, we want to examine salary
variation within the groups - are they equal?Use Cell K10 for
the Excel test outcome location.aWhat is the data input ranged
used for this question:b Which is needed for this question: a
one- or two-tail hypothesis statement and test ?Answer:Why:c.
Step 1:Ho:Ha:Step 2:Significance (Alpha):Step 3:Test Statistic
and test:Why this test?Step 4:Decision rule:Step 5:Conduct the
test - place test function in cell k10Step 6:Conclusion and
InterpretationWhat is the p-value:What is your decision: REJ or
NOT reject the null?Why?What is your conclusion about the
variance in the population for male and female salaries?2Once
we know about variance quality, we can move on to means: Are
male and female average salaries equal?Use Cell K35 for the
Excel test outcome location.(Regardless of the outcome of the
above F-test, assume equal variances for this test.)aWhat is the
data input ranged used for this question:b Does this question
need a one or two-tail hypothesis statement and test?Why:c.
and test:Why this test?Step 4:Decision rule:Step 5:Conduct the
test - place test function in cell K35Step 6:Conclusion and
InterpretationWhat is the p-value:What is your decision: REJ or
NOT reject the null?Why?What is your conclusion about the
means in the population for male and female
salaries?3Education is often a factor in pay differences.Do
employees with an advanced degree (degree = 1) have higher
average salaries?Use Cell K60 for the Excel test outcome
location.Note: assume equal variance for the salaries in each
degree for this question.aWhat is the data input ranged used for
this question:b Does this question need a one or two-tail
hypothesis statement and test?Why:c. Step 1:Ho:Ha:Step
2:Significance (Alpha):Step 3:Test Statistic and test:Why this

test?Step 4:Decision rule:Step 5:Conduct the test - place test
function in cell K60Step 6:Conclusion and InterpretationWhat is
the p-value:Is the t value in the t-distribution tail indicated by
the arrow in the Ha claim?What is your decision: REJ or NOT
reject the null?Why?What is your conclusion about the impact
of education on average salaries?4Considering both the compa-
ratio information from the lectures and your salary information,
what conclusions can you reach about equal pay for equal
work?Why - what statistical results support this conclusion?
Week 3Week 3: Identifying Significant Differences - part 2Data
Input Table:Salary Range GroupsGroup name:ABCDEFTo
Ensure full credit for each question, you need to show how you
got your results. This involves either showing where the data
you used is located List salaries within each gradeor showing
the excel formula in each cell.Be sure to copy the appropriate
data columns from the data tab to the right for your use this
week.1A good pay program will have different average salaries
by grade. Is this the case for our company?aWhat is the data
input ranged used for this question:Use Cell K08 for the Excel
test outcome location.Note: assume equal variances for each
grade, even though this may not be accurate, for purposes of
this question.b. Step 1:Ho:Ha:Step 2:Significance
(Alpha):Step 3:Test Statistic and test:Why this test?Step
4:Decision rule:Step 5:Conduct the test - place test function in
cell K08Step 6:Conclusion and InterpretationWhat is the p-
value:What is your decision: REJ or NOT reject the
null?Why?What is your conclusion about the means in the
population for grade salaries?2If the null hypothesis in question
1 was rejected, which pairs of means differ?(Use the values
from the ANOVA table to complete the follow table.)Groups
ComparedMean Diff.T value used+/- TermLowto
HighDifference Significant?Why?A-BA-CA-DA-EA-FB-CB-
DB-EB-EC-DC-EC-FD-ED-FE-F3One issue in salary is the
grade an employee is in - higher grades have higher

salaries.This suggests that one question to ask is if males and
females are distributed in a similar pattern across the salary
grades?aWhat is the data input ranged used for this
question:Use Cell K54 for the Excel test outcome location.b.
and test:Place the actual distribution in the table below.Why
this test?ABCDEFStep 4:Decision rule:MaleStep 5:Conduct the
test - place test function in cell K54FemaleStep 6:Conclusion
and InterpretationPlace the expected distribution in the table
below.What is the p-value:ABCDEFWhat is your decision: REJ
or NOT reject the null?MaleWhy?FemaleWhat is your
conclusion about the means in the population for male and
female salaries?4What implications do this week's analysis have
for our equal pay question?Why - what statistical results
support this conclusion?
Week 4Week 4: Identifying relationships - correlations and
regressionTo Ensure full credit for each question, you need to
show how you got your results. This involves either showing
where the data you used is located or showing the excel formula
in each cell.Be sure to copy the appropriate data columns from
the data tab to the right for your use this week.1What is the
correlation between and among the interval/ratio level variables
with salary? (Do not include compa-ratio in this question.)a.
Create the correlation table.Use Cell K08 for the Excel test
outcome location.i.What is the data input ranged used for this
question:ii. Create a correlation table in cell K08.b.
Technically, we should perform a hypothesis testing on each
correlation to determine if it is significant or not. However, we
can be faithful to the process and save some time by finding the
minimum correlation that would result in a two tail rejection of
the null.We can then compare each correlation to this value, and
those exceeding it (in either a positive or negative direction)
can be considered statistically significant. i. What is the t-
value we would use to cut off the two tails?T = ii. What is the

associated correlation value related to this t-value? r =c. What
variable(s) is(are) significantly correlated to salary?d. Are there
any surprises - correlations you though would be significant and
are not, or non significant correlations you thought would be?e.
Why does or does not this information help answer our equal
pay question?2Perform a regression analysis using salary as the
dependent variable and the variables used in Q1 along withour
two dummy variables - gender and education. Show the result,
and interpret your findings by answering the following
questions.Suggestion: Add the dummy variables values to the
right of the last data columns used for Q1.What is the multiple
regression equation predicting/explaining salary using all of our
possible variables except compa-ratio?a.What is the data input
ranged used for this question:b. Step 1: State the appropriate
hypothesis statements:Use Cell M34 for the Excel test outcome
location.Ho:Ha:Step 2:Significance (Alpha):Step 3:Test
Statistic and test:Why this test?Step 4:Decision rule:Step
5:Conduct the test - place test function in cell M34Step
6:Conclusion and InterpretationWhat is the p-value:What is
your decision: REJ or NOT reject the null?Why?What is your
conclusion about the factors influencing the population salary
values?c.If we rejected the null hypothesis, we need to test the
significance of each of the variable coefficients.Step 1: State
the appropriate coefficient hypothesis statements:(Write a
single pair, we will use it for each variable
separately.)Ho:Ha:Step 2:Significance (Alpha):Step 3:Test
Statistic and test:Why this test?Step 4:Decision rule:Step
5:Conduct the testNote, in this case the test has been performed
and is part of the Regression output above.Step 6:Conclusion
and InterpretationPlace the t and p-values in the following
tableIdentify your decision on rejecting the null for each
variable. If you reject the null, place the coefficient in the
table.MidpointAgePerf. Rat.SeniorityRaiseGenderDegreet-
value:P-value:Rejection Decision:If Null is rejected, what is the
variable's coefficient value?Using the intercept coefficient and
only the significant variables, what is the equation?Salary =

d.Is gender a significant factor in salary?e.Regardless of
statistical significance, who gets paid more with all other things
being equal?f.How do we know? 3After considering the compa-
ratio based results in the lectures and your salary based results,
what else would you like to knowbefore answering our question
on equal pay? Why?4Between the lecture results and your
results, what is your answer to the questionof equal pay for
equal work for males and females? Why?5What does regression
analysis show us about analyzing complex measures?

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