LITR221 Quiz Rubric Exemplary Level Accomplished Level Developing Level Beginning Level Points Earned Analysis 20-16: Student provides significant, well focused analysis. At least 75% of the response is original analysis. 15-11: Student offers original interpretation of the work and makes strong connections that support a clear and focused thesis. At least 50% of the response is original analysis. 10-6: Student provides some insight into the work, but ideas do not delve into a deeper understanding of the various aspects of the question. 5-0: Analysis is lacking. Ideas may be underdeveloped or supported by inaccuracies. /20 Support 20-16: Student supports ideas with cited evidence from the text. There are at least three pieces of support, appropriate in length and content. 15-11: Student maintains a balance of analysis and support. At least two cited examples are used to strengthen the response. 10-6: Student has provided some support, but it may not be clearly linked to the thesis. 5-0: There is little or no evidence from the text. General summary is the only means of support. /20 Proofreading 5: Response shows careful proofreading. 4: There is evidence of proofreading. However, some minor errors exist. 3-2: Some errors impede reading. Ideas are occasionally unclear due to errors. 1-0: There is no evidence of proofreading. Ideas are unclear or incomplete due to proofreading issues. /5 Style 5: Response shows careful proofreading. All sources are properly cited, both in-text and in a work cited. 4: Sources are cited, but there may be errors of style in either the in- text citation or work cited. 3-2: Sources are cited, but inconsistently. In-text citations or works cited may be missing. 1-0: There is no attempt to adequately credit sources. /5 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 midpoint37220.956232295216.21FA42241.0432332100815.70FA3341.096313075513.60FB1836 ...

1. LITR221 Quiz
Rubric
Exemplary
Level
Accomplished
Level
Developing
Level
Beginning
Level
Points Earned
Analysis 20-16: Student
provides
significant, well
focused
analysis. At
least 75% of the
response is
original
analysis.
15-11: Student
offers original
interpretation
of the work and
makes strong

2. connections
that support a
clear and
focused thesis.
At least 50% of
the response is
original
analysis.
10-6: Student
provides some
insight into the
work, but ideas
do not delve
into a deeper
understanding
of the various
aspects of the
question.
5-0: Analysis is
lacking. Ideas
may be
underdeveloped
or supported by
inaccuracies.
/20
Support 20-16: Student
supports ideas
with cited
evidence from
the text. There
are at least
three pieces of

3. support,
appropriate in
length and
content.
15-11: Student
maintains a
balance of
analysis and
support. At
least two cited
examples are
used to
strengthen the
response.
10-6: Student
has provided
some support,
but it may not
be clearly
linked to the
thesis.
5-0: There is
little or no
evidence from
the text.
General
summary is the
only means of
support.
/20
Proofreading 5: Response

4. shows careful
proofreading.
4: There is
evidence of
proofreading.
However, some
minor errors
exist.
3-2: Some
errors impede
reading. Ideas
are occasionally
unclear due to
errors.
1-0: There is no
evidence of
proofreading.
Ideas are
unclear or
incomplete due
to proofreading
issues.
/5
Style 5: Response
shows careful
proofreading.
All sources are
properly cited,
both in-text and
in a work cited.

5. 4: Sources are
cited, but there
may be errors
of style in
either the in-
text citation or
work cited.
3-2: Sources
are cited, but
inconsistently.
In-text citations
or works cited
may be
missing.
1-0: There is no
attempt to
adequately
credit sources.
/5
DataSee comments at the right of the data
set.IDSalaryCompaMidpointAgePerformance
RatingServiceGenderRaiseDegreeGender1Grade8231.000233290
915.80FAThe 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.11231.00023411001914.80FA14241.04323329012160FAT
he column labels in the table
mean:15241.043233280814.90FAID – Employee sample number
Salary – Salary in thousands 23231.000233665613.31FAAge

6. – 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= BSBA 1 =
MS)36231.000232775314.31FAGender1 (Male or
Female)Compa - salary divided by
midpoint37220.956232295216.21FA42241.0432332100815.70F
A3341.096313075513.60FB18361.1613131801115.61FB20341.0
963144701614.81FB39351.129312790615.51FB7411.025403210
0815.70FC13421.0504030100214.71FC22571.187484865613.80
FD24501.041483075913.81FD45551.145483695815.20FD17691
.2105727553130FE48651.1405734901115.31FE28751.11967449
5914.41FF43771.1496742952015.51FF19241.043233285104.61
MA25241.0432341704040MA40251.086232490206.30MA2270.
870315280703.90MB32280.903312595405.60MB34280.903312
680204.91MB16471.175404490405.70MC27401.000403580703.
91MC41431.075402580504.30MC5470.9794836901605.71MD3
0491.0204845901804.30MD1581.017573485805.70ME4661.157
57421001605.51ME12601.0525752952204.50ME33641.1225735
90905.51ME38560.9825745951104.50ME44601.0525745901605
.21ME46651.1405739752003.91ME47621.087573795505.51ME
49601.0525741952106.60ME50661.1575738801204.60ME6761.
1346736701204.51MF9771.149674910010041MF21761.134674
3951306.31MF29721.074675295505.40MF
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

7. 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.SalaryCompaAgePerf.
Rat.ServiceOverallMeanStandard
DeviationRangeFemaleMeanStandard
DeviationRangeMaleMeanStandard DeviationRange3What 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?4For each group (overall, females, and males)
find:OverallFemaleMalea.The value that cuts off the top 1/3
salary in each group.b.The z score for each value:c.The normal
curve probability of exceeding this score:d.What is the
empirical probability of being at or exceeding this salary
value?e.The value that cuts off the top 1/3 compa in each
group.f.The z score for each value:g.The normal curve
probability of exceeding this score:h.What is the empirical
probability of being at or exceeding this compa value?i.How do
you interpret the relationship between the data sets? What do
they mean about our equal pay for equal work question?5.
What conclusions can you make about the issue of male and
female pay equality? Are all of the results consistent? What is
the difference between the sal and compa measures of
pay?Conclusions from looking at salary results:Conclusions
from looking at compa results:Do both salary measures show
the same results?Can we make any conclusions about equal pay
for equal work yet?
Week 2 Week 2Testing meansQ3In questions 2 and 3, be sure to
include the null and alternate hypotheses you will be testing.

8. HoFemaleMaleFemaleIn the first 3 questions use alpha = 0.05 in
making your decisions on rejecting or not rejecting the null
hypothesis.45341.0171.09645410.8701.0251Below are 2 one-
sample t-tests comparing male and female average salaries to
the overall sample mean. 45231.1571.000(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)45220.9790.956Based 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?45231.1341.000MalesFemalesFemaleMean38.01.05903
2.584.27.945421.1491.050Ho: Mean salary = 45Ho: Mean salary
= 45Standard
Deviation14.9510.07036.88113.5924.90745241.0521.043Ha:
Mean salary =/= 45Ha: Mean salary =/=
45Range550.23118351845241.1751.043MaleMean48.01.069038.
987.610.045691.0431.210Standard
Deviation14.6880.08378.2518.6756.357530.196283021Note:
While the results both below are actually from Excel's t-Test:
Two-Sample Assuming Unequal Variances,
45361.1341.161having 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.45341.0431.096MaleHoFemaleHo45571.0001.187Mean5245
Mean384545231.0741.000Variance3160Variance334.666666666
7045501.0201.041Observations2525Observations2525223.5324
0145240.9031.043Hypothesized Mean Difference0Hypothesized
Mean
Difference0215.73734445751.1221.119df24df2445240.9031.043
t Stat1.9689038266t Stat-1.913206357345240.9821.043P(T<=t)
one-tail0.0303078503P(T<=t) one-
tail0.033862118445231.0861.000t Critical one-
tail1.7108820799t Critical one-
tail1.710882079945221.0750.956P(T<=t) two-
tail0.0606157006P(T<=t) two-
tail0.067724236945351.0521.129t Critical two-

9. tail2.0638985616t Critical two-
tail2.063898561645241.1401.043Conclusion: Do not reject Ho;
mean equals 45Conclusion: Do not reject Ho; mean equals
4545771.0871.149Is this a 1 or 2 tail test? two tailedIs this a 1
or 2 tail test?two tailed- why? alternative hypothesis is bearning
not equal sign- why?Its alternative hypothesis is bearing and not
a equal signP-value is:0.485P-value is:0.454645551.0521.145Is
P-value > 0.05?yesIs P-value > 0.05?yes 45651.1571.140Why
do we not reject Ho?Why do we not reject Ho?Interpretation:the
anticipated mean is far from the actual Its anticipated mean is
far from the actual mean of 48. so, it is reject null hypothesis
the null hypothesis is rejected2Based 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: Mean salary =
45Ho: Mean salary = 45Ha: Mean salary =/= 45Ha: Mean salary
=/= 45in the two tail test, α/2 =0.025 while it is given that
α=0.05 in one tail testTest to use:the level of significance is -
1.96 to +1.96Place B43 in Outcome range
box.MaleHoFemaleHoMean5245Mean3845Variance3160Varianc
e334.66666666670Observations2525Observations2525Hypothes
ized Mean Difference0Hypothesized Mean
Difference0df24df24t Stat1.9689038266t Stat-
1.9132063573P(T<=t) one-tail0.0303078503P(T<=t) one-
tail0.0338621184t Critical one-tail1.7108820799t Critical one-
tail1.7108820799P(T<=t) two-tail0.0606157006P(T<=t) two-
tail0.0677242369t Critical two-tail2.0638985616t Critical two-
tail2.0638985616Conclusion: not reject Ho; mean equals
45Conclusion: Do not reject Ho; mean equals 45P-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:the level of significance is the effective size
measurez = (M-μ)/σInterpretation:b.Since the one and two tail t-
test results provided different outcomes, which is the
proper/correct apporach to comparing salary equality? Why?

10. the one tailed test is less detailed than the two tailed test. one
tailed test by itself is a failure and may not accept the null
hypothesis. So, automatically twoone way may be to increase
the level of significance. 3Based on our sample data set, can the
male and female compas in the population be equal to each
other? (Another 2-sample t-test.)Male employees Female
EmployeesHo:Means salary = 47Means salary = 40Ha: Means
salary =/= 47Means salary =/= 40Statistical test to use:Place
B75 in Outcome range
box.MaleHoFemaleHoMean5247Mean3840Variance215.7470Va
riance223.5320Observations2525Observations2525Hypothesize
d Mean Difference0Hypothesized Mean Difference0df24df24t
Stat0.0231752933t Stat-0.0089472648P(T<=t) one-
tail0.0303078503P(T<=t) one-tail0.0338621184t Critical one-
tail0.85544104t Critical one-tail0.85544104P(T<=t) two-
tail0.0115876466P(T<=t) two-tail-0.0044736324t Critical two-
tail1.0319492808t Critical two-tail1.0319492808Conclusion: Do
not reject Ho; mean equals 47Conclusion: Do not reject Ho;
mean equals 40What is the p-value:0.235Is P-value <
0.05?yesReject or do not reject Ho:reject If the null hypothesis
was rejected, what is the effect size value:z = (M-
μ)/σ=0.2Meaning of effect size measure:Its population size
affects the significance level Interpretation: we check whether
there is equal pay for the same amount work for both male and
female employees or not.4Since performance is often a factor in
pay levels, is the average Performance Rating the same for both
genders?Ho:The performance rating = 85.9performance rating =
85.9performance rating = 85.9Ha:The performance rating =/=
85.9Test to use:Its level of significance is α=0.05 suitable for
the test Place B106 in Outcome range
box.MaleHoFemaleHoMean87.685.9Mean84.285.9Variance75.2
5560Variance184.7420Observations2525Observations2525Hypo
thesized Mean Difference0Hypothesized Mean
Difference0df24df24t Stat0.022589681t Stat-
0.0092020223P(T<=t) one-tail0.0303078503P(T<=t) one-
tail0.0338621184t Critical one-tail0.85544104t Critical one-

11. tail0.85544104P(T<=t) two-tail0.0112948405P(T<=t) two-tail-
0.0046010111t Critical two-tail1.0319492808t Critical two-
tail1.0319492808Conclusion: Do not reject Ho; mean equals
85.9Conclusion: Do not reject Ho; mean equals 85.9What is the
p-value:0.025590.0092Is P-value < 0.05?yesyesDo we REJ or
Not reject the null?no noIf the null hypothesis was rejected,
what is the effect size value:Meaning of effect size measure:(M-
μ)/σ=0.02599-0.092Interpretation:The hypothesis is in its
limits. We are going to accepts the null hypothesis for equal
pay5If the salary and compa mean tests in questions 2 and 3
provide different results about male and female salary equality,
which would be more appropriate to use in answering the
question about salary equity? Why?The salary and the compa
test are not within the important limits set and the null
hypothsis is equal pay for equal workWhat are your conclusions
about equal pay at this point?The equity of the salary and its
performance rating is better to accept a null hypothsis
Week 3Week 3At 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.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.)ABCDEF9075100655595Null Hypothesis:In each
ofthe six grade levels the average performance range is the
same8080100759095Alt. Hypothesis:In the six grade levels
there are two grade levels that are not the
same1007090958570Place B17 in Outcome range
box.90908090100100808080909595Anova: Single

12. Factor65959095SUMMARY958095GroupsCountSumAverageVa
riance6090A15126584.3333333333153.09523809529075B75708
1.428571428672.6190476197595C5450901009595D541583157.
510080E12104587.0833333333152.083333333385F655091.6666
666667116.66666666677090Interpretation:What is the p-
value:0.5702Is P-value < 0.05?NoDo we REJ or Not reject the
null? not reject the null hypothesisIf the null hypothesis was
rejected, what is the effect size value (eta squared):cMeaning of
effect size measure:What does that decision mean in terms of
our equal pay question:The results of the ANOVA shows that
the performance rating for each of the six grades in its
population does not show a difference significantly.2While 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:The average
salary is the same for each of the 6 grade levels Alt.
Hypothesis:The average salary is not the same for at least two
of the 6 grade levelsABCDEF232741475876223442576677Place
B55 in Outcome range box.233647506076Anova: Single
Factor243440496975SUMMARY242843556472GroupsCountSu
mAverageVariance24285677A1535323.53333333330.69523809
52233560B722231.714285714314.90476190482465C521342.67.
32462D525851.617.82465E1275162.583333333314.8106060606
2460F645375.53.523662225What is the p-value:0.0000Is P-
value < 0.05?YesDo you reject or not reject the null
hypothesis:Reject the null hypothesisIf the null hypothesis was
rejected, what is the effect size value (eta
squared):0.9790Meaning of effect size measure: eta squared >
0.5, the sample draws a conclusions about the null
hypothesis.Interpretation:ANOVA shows that the salaries of at
least two grades in the population differ significantly3The table
and analysis below demonstrate a 2-way ANOVA with
replication. Please interpret the results.BAMAHo: Average
compas by gender are equalMale1.0171.157Ha: Average compas

13. by gender are not equal0.8700.979Ho: Average compas are
equal for each degree1.0521.134Ho: Average compas are not
equal for each degree1.1751.149Ho: Interaction is not
significant1.0431.043Ha: Interaction is
significant1.0741.1341.0201.000Perform
analysis:0.9031.1220.9820.903Anova: Two-Factor With
Replication1.0861.0521.0751.140SUMMARYBAMATotal1.052
1.087MaleFemale1.0961.050Count1212241.0251.161Sum12.349
12.925.2491.0001.096Average1.02908333331.0751.0520416667
0.9561.000Variance0.0066864470.00651981820.00686604171.0
001.0411.0431.043Female1.0431.119Count1212241.2101.043Su
m12.79112.78725.5781.1871.000Average1.06591666671.06558
333331.065751.0430.956Variance0.0061024470.00421281060.0
049334131.0431.1291.1451.149TotalCount2424Sum25.1425.68
7Average1.04751.0702916667Variance0.00647034780.0051561
286ANOVASource of VariationSSdfMSFP-valueF
critSample0.002255020810.00225502080.38348211710.5389389
5074.0617064601 (This is the row variable or
gender.)Columns0.006233520810.00623352081.06005396090.3
0882956334.0617064601 (This is the column variable or
Degree.)Interaction0.006417187510.00641718751.09128776640
.30189150624.0617064601Within0.25873675440.0058803807To
tal0.273642479247Interpretation:For Ho: Average compas by
gender are equalHa: Average compas by gender are not
equalWhat is the p-value:0.5389Is P-value < 0.05?NoDo you
reject or not reject the null hypothesis:Not reject the null
hypothesisIf the null hypothesis was rejected, what is the effect
size value (eta squared):Meaning of effect size measure:For Ho:
Average salaries are equal for all grades Ha: Average salaries
are not equal for all gradesWhat is the p-value:0.3088Is P-value
< 0.05?NoDo you reject or not reject the null hypothesis: Not
reject the null hypothesisIf the null hypothesis was rejected,
what is the effect size value (eta squared):Meaning of effect
size measure:For: Ho: Interaction is not significantHa:
Interaction is significantWhat is the p-value:0.3018Do you
reject or not reject the null hypothesis:NoIf the null hypothesis

14. was rejected, what is the effect size value (eta squared): Not
reject the null hypothesisMeaning of effect size measure:What
do these decisions mean in terms of our equal pay
question:There is not a large difference between the average
compas for females and males4Many companies consider the
grade midpoint to be the "market rate" - what is needed to hire a
new employee.MidpointSalaryDoes the company, on average,
pay its existing employees at or above the market
rate?232323222323Null Hypothesis:The employees average
salary is the same as the market2324Alt. Hypothesis:The
employees average salary is higher than the
market23242323Statistical test to use:Took Paired T-Test that
had the two dependants23242324Place the cursor in B160 for
correl.2324T-Test: Paired Two Sample for
Means23232322SalaryMidpoint2324Mean4541.763134Variance
368.693877551263.45142857143136Observations50503134Pear
son Correlation0.98897178273135Hypothesized Mean
Difference04041df494042t Stat5.78270449814857P(T<=t) one-
tail0.00000025254850t Critical one-
tail1.67655089264855P(T<=t) two-tail0.0000005055769t
Critical two-tail2.00957523715765677567772324What is the p-
value:0.00002324Is P-value < 0.05?Yes2325Do we REJ or Not
reject the null?Reject the null hypothesis3127If the null
hypothesis was rejected, what is the effect size
value:3128Meaning of effect size
measure:NA31284047Interpretation:The T-Test shows that the
average salary of the employees is higher than the
market4040404348475. Using the results up thru this week,
what are your conclusions about gender equal pay for equal
work at this point?4849There is no no big difference between
the salaries for the females and the males. Both females and
males have equal5758pay for equal work that is done.
5766576057645756576057655762576057666776677767766772
Week 4Week 4Confidence 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

15. 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.1Using 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)?MeanSt error t valueLow to
HighMales523.55527776692.063898561644.662267330759.337
7Females383.65877939572.063898561630.448650467845.5513<
Reminder: standard error is the sample standard deviation
divided by the square root of the sample size.>Interpretation:It
seems that the salary of males in between (44.6623, 59.3377).
This is based on only if 25 males were taken from this it would
mean that 95% of the male population would be the first
number. 25 females were taken from this population and the
salary of the females were in between (30.4487,
45.5513).2Using 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?DifferenceSt Err.T valueLow to
High145.10163372532.01063475763.742524.2575Yes/NoCan
the means be equal?NoWhy? The confidence interval seems to
not contain the value 0 and it means that the means are not
equal.How does this compare to the week 2, question 2 result (2
sampe t-test)?The results of question 2, Week 2 does show the
same that these results showa.Why is using a two sample tool
(t-test, confidence interval) a better choice than using 2 one-
sample techniques when comparing two samples? the two
sample t-test or confidence interval deals with the diffrences
between the two sample means that we are looking at, those
procedures are more convincing than using 2 one-sample t
techniques.3We found last week that the degrees compa 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

16. not be large enough to perform this test, ignore this limitation
for this exercise.)What are the hypothesis statements:Ho: Both
males and females have the same distribution of degrees by
gradeHa:Males and females do not have the same distribution of
degrees by gradeNote: You can either use the Excel Chi-related
functions or do the calculations manually.Data input tables -
graduate degrees by gender and grade levelOBSERVEDA
BCDEFTotalDo manual calculations per cell here (if desired)M
Grad11115312A BCDEFFem Grad53111213M
Grad1.87777777780.27523809520.03333333330.03333333331.5
6055555561.69Male Und22215113Fem
Grad0.31025641030.76505494510.06923076920.06923076921.4
4051282050.1241025641Female Und71121012Male
Und0.92564102560.01780219780.37692307690.06923076921.1
3282051280.2010256411575512650Female
Und3.21111111110.27523809520.03333333330.53333333331.2
2722222221.44Sum =17.6923076923EXPECTEDTotalM
Grad3.61.681.21.22.881.4412For this exercise - ignore the
requirement for a correctionFem
Grad3.91.821.31.33.121.5613for expected values less than
5.Male Und3.91.821.31.33.121.5613Female
Und3.61.681.21.22.881.44121575512650Interpretation:What is
the value of the chi square statistic: 17.6923What is the p-value
associated with this value: 0.279187598Is the p-value
<0.05?NoDo you reject or not reject the null hypothesis: Do not
reject the null hypothesisIf you rejected the null, what is the
Cramer's V correlation:N/AWhat does this correlation
mean?N/AWhat does this decision mean for our equal pay
question: Since males and females have the same distribution by
pay grade it shows that males and females have equal
pay4Based on our sample data, can we conclude that males and
females are distributed across grades in a similar patternwithin
the population?What are the hypothesis statements:Ho: Both
males and females are distributed across grades in a similar
patternHa:Both males and females are not distributed across
grades in a similar patternDo manual calculations per cell here

17. (if desired)A BCDEFA BCDEFOBS COUNT -
m333210425M2.70.07142857140.10.12.66666666670.33333333
33OBS COUNT -
f124232225F2.70.07142857140.10.12.66666666670.333333333
31575512650Sum =
11.9428EXPECTEDTotal7.53.52.52.563257.53.52.52.56325157
5512650What is the value of the chi square statistic:
11.9429What is the p-value associated with this value:
0.0355786146Is the p-value <0.05?YesDo you reject or not
reject the null hypothesis: Reject the null hypothesisIf you
rejected the null, what is the Phi correlation:0.48874What does
this correlation mean?Between Gender and grade there is an
unstable areaWhat does this decision mean for our equal pay
question: Males and Females don't have equal pay5. How do
you interpret these results in light of our question about equal
pay for equal work?Results of t-confidence intervals in
questions 1 and 2 shows that there is a difference between the
average salaries for males and females They don't get equal pay.
Week 5Week 5 Correlation and Regression1. 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 in Output range box):c.Using r =
approximately .28 as the signicant r value (at p = 0.05) for a
correlation between 50 values, what variables aresignificantly
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?2Below 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:

18. 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.SalSUMMARY OUTPUTRegression StatisticsMultiple
R0.9915590747R Square0.9831893985Adjusted R
Square0.9808437332Standard
Error2.6575925726Observations50ANOVAdfSSMSFSignificanc
e
FRegression617762.29967387432960.383278979419.151611129
41.8121523852609E-
36Residual43303.70032612577.062798282Total4918066Coeffic
ientsStandard Errort StatP-valueLower 95%Upper 95%Lower
95.0%Upper 95.0%Intercept-1.74962121233.6183676583-
0.48353881570.6311664899-9.04675504275.547512618-
9.04675504275.547512618Midpoint1.21670105050.0319023509
38.13828811638.66416336978111E-
351.15236382831.28103827271.15236382831.2810382727Age-
0.00462801020.065197212-0.07098478760.9437389875-
0.13611071910.1268546987-
0.13611071910.1268546987Performace Rating-
0.05659644050.0344950678-1.64071109710.1081531819-
0.12616237470.0129694936-
0.12616237470.0129694936Service-
0.04250035730.0843369821-0.50393500330.6168793519-
0.21258209120.1275813765-
0.21258209120.1275813765Gender2.4203372120.86084431762.
81158528040.00739661880.6842791924.1563952320.68427919
24.156395232Degree0.27553341430.79980230480.34450190090
.732148119-1.33742165471.8884884833-
1.33742165471.8884884833Note: since Gender and Degree are
expressed as 0 and 1, they are considered dummy variables and
can be used in a multiple regression equation.Interpretation:For
the Regression as a whole:What is the value of the F statistic:
What is the p-value associated with this value: Is the p-value

19. <0.05?Do you reject or not reject the null hypothesis: What
does this decision mean for our equal pay question: For each of
the coefficients:InterceptMidpointAgePerf.
Rat.ServiceGenderDegreeWhat is the coefficient's p-value for
each of the variables: Is the p-value < 0.05?Do you reject or not
reject each null hypothesis: What are the coefficients for the
significant variables?Using only the significant variables, what
is the equation?Salary =Is gender a significant factor in
salary:If so, who gets paid more with all other things being
equal?How do we know? 3Perform a regression analysis using
compa as the dependent variable and the same
independentvariables 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.Regression hypothesesHo:Ha:Coefficient hypotheses
(one to stand for all the separate variables)Ho:Ha:Put C94 in
output range boxInterpretation:For the Regression as a
whole:What is the value of the F 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: What does this decision mean
for our equal pay question: For each of the coefficients:
InterceptMidpointAgePerf. Rat.ServiceGenderDegreeWhat is
the coefficient's p-value for each of the variables: Is the p-value
< 0.05?Do you reject or not reject each null hypothesis: What
are the coefficients for the significant variables?Using only the
significant variables, what is the equation?Compa = Is gender a
significant factor in compa:If so, who gets paid more with all
other things being equal?How do we know? 4Based on all of
your results to date, do we have an answer to the question of are
males and females paid equally for equal work?If so, which
gender gets paid more? How do we know?Which is the best
variable to use in analyzing pay practices - salary or compa?
Why?What is most interesting or surprising about the results we
got doing the analysis during the last 5 weeks?5Why did the
single factor tests and analysis (such as t and single factor
ANOVA tests on salary equality) not provide a complete answer

20. to our salary equality question?What outcomes in your life or
work might benefit from a multiple regression examination
rather than a simpler one variable test?