MARKETING MANAGEMENT PHILOSOPHIESCHAPTER 1 - ASSIGNMENTQuest.docx

MARKETING MANAGEMENT PHILOSOPHIES
CHAPTER 1 - ASSIGNMENT
Question 1.
Considering the differences of the philosophies, in some cases
slight differences, select a company (product or service) and
describe the current philosophy they pose for the customer.
Include in your comments the level of customer value delivered
by the company’s actions.
In other words, measure the company’s interaction with their
customers against the Market Concept Philosophy. Does the
company operate under the Market Concept Philosophy or do
they lean more toward one of the other Philosophies.
Be specific with your examples.
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
– 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
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.
HoFemalemaleMaleFemaleIn the first 3 questions use alpha =
0.05 in making your decisions on rejecting or not rejecting the
null hypothesis.4534241.0171.0964541240.8701.0251Below are
2 one-sample t-tests comparing male and female average
salaries to the overall sample mean. 4523251.1571.000(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)4522270.9790.956Based 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?4523281.1341.000MalesFemales4542281.1491.050Ho:
Mean salary = 45Ho: Mean salary = 454524471.0521.043Ha:
Mean salary =/= 45Ha: Mean salary =/=
454524401.1751.043,4569431.0431.210Note: While the results
both below are actually from Excel's t-Test: Two-Sample
Assuming Unequal Variances, 4536471.1341.161having 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.4534491.0431.096MaleHoFemaleHo4557581.0001.187Mean5
245Mean38454523661.0741.000Variance3160Variance334.6666
66666704550601.0201.041Observations2525Observations25254
524640.9031.043Hypothesized Mean Difference0Hypothesized
Mean Difference04575561.1221.119df24df244524600.9031.043t
Stat1.9689038266t Stat-1.91320635734524650.9821.043P(T<=t)
one-tail0.0303078503P(T<=t) one-
tail0.03386211844523621.0861.000t Critical one-
tail1.7108820799t Critical one-
tail1.71088207994522601.0750.956P(T<=t) two-
tail0.0606157006P(T<=t) two-
tail0.06772423694535661.0521.129t Critical two-
tail2.0638985616t Critical two-
tail2.06389856164524761.1401.043Conclusion: Do not reject
Ho; mean equals 45Conclusion: Do not reject Ho; mean equals
454577771.0871.149Is this a 1 or 2 tail test?1Is this a 1 or 2 tail
test?4555761.0521.145- why?because the hypothis specifies
only a difference,not a directionbecause the hypothis specifies
only a difference,not a direction4565721.1571.140P-value
is:0.060615701P-value is:0.0677Is P-value > 0.05?yesIs P-value
> 0.05?yesWhy do we not reject Ho?because the value is

higherWhy do we not reject Ho?because the P value is
higherInterpretation:Almost no significant difference in salaries
for males in this company compared to all employeesAlmost no
significant difference in salaries for females in this company
compared to all employees2Based 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.)Since the samples
are the same as they will be the same, both T-tests results are in
the same statistic, the same P- vHo: Two -sample test assuming
that there are equal variancesHa: Two -sample test assuming
that there are unequal variances.Test to use:Place B43 in
Outcome range box.t-Test: Two-Sample Assuming Equal
Variancest-Test: Two-Sample Assuming Unequal Variancesmale
femalemale femaleVariable 1Variable 2Variable 1Variable
2Mean5238Mean5238Variance316334.6666666667Variance316
334.6666666667Observations2525Observations2525Pooled
Variance325.3333333333Hypothesized Mean
Difference0Hypothesized Mean Difference0df48df48t
Stat2.7442189608t Stat2.7442189608P(T<=t) one-
tail0.0042530089P(T<=t) one-tail0.0042530089t Critical one-
tail1.6772241961t Critical one-tail1.6772241961P(T<=t) two-
tail0.0085060179P(T<=t) two-tail0.0085060179t Critical two-
tail2.0106347576t Critical two-tail2.0106347576P-value
is:0.008506Is P-value < 0.05?YesReject or do not reject
Ho:YesIf the null hypothesis was rejected, what is the effect
size value:0.0368Meaning of effect size
measure:64%Interpretation:b.Since the one and two tail t-test
results provided different outcomes, which is the proper/correct
apporach to comparing salary equality? Why? The P value-
0.008 is less than Alpha =0.05, it implies that there is enough
evidence to indicate that the overall population average salaries
of Males and females within the company do differ.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.)Ho:Ha:Statistical test to use:Place B75 in Outcome range
box.t-Test: Two-Sample Assuming Equal Variancest-Test: Two-
Sample Assuming Unequal
VariancesmalefemalemalefemaleVariable 1Variable 2Variable
1Variable
2Mean1.056241.06872Mean1.056241.06872Variance0.00702060
670.0049483767Variance0.00702060670.0049483767Observatio
ns2525Observations2525Pooled
Variance0.0059844917Hypothesized Mean
Difference0Hypothesized Mean Difference0df47df48t Stat-
0.5703690595t Stat-0.5703690595P(T<=t) one-
tail0.2855721176P(T<=t) one-tail0.2855439182t Critical one-
tail1.6779267216t Critical one-tail1.6772241961P(T<=t) two-
tail0.5711442353P(T<=t) two-tail0.5710878364t Critical two-
tail2.0117405137t Critical two-tail2.0106347576What is the p-
value:0.5710878364Is P-value < 0.05?noReject or do not reject
Ho:rejectIf the null hypothesis was rejected, what is the effect
size value:0.0368Meaning of effect size measure:64%
Interpretation: 4Since performance is often a factor in pay
levels, is the average Performance Rating the same for both
genders?Ho:Ha:Test to use:Place B106 in Outcome range
box.What is the p-value:0.29702594Is P-value < 0.05?noDo we
REJ or Not reject the null?rejectIf the null hypothesis was
rejected, what is the effect size value:0.1504Meaning of effect
size measure:54%Interpretation:5If 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?What are your conclusions about equal pay at this
point?The two T- Tests do offer a different conclusion given the
parameters. Why is this? The Compa variables degrade or null
the gradeUltimately , using the Compa lets us remove the
degree of impact of grade.
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:the average
performance rating is the same for each 6grade
levels8080100759095Alt. Hypothesis:the average performance
rating is not the same for 2 or of the 6.1007090958570Place
B17 in Outcome range
box.90908090100100GroupsCountSumAverageVariance8080809
09595A15126584.3333333333153.095238095265959095B75708
1.428571428672.619047619958095C5450901006090D54158315
7.59075E12104587.0833333333152.08333333337595F655091.6
666666667116.6666666667959510080857090Interpretation:Wh
at is the p-value:0.5702Is P-value < 0.05?NoDo we REJ or Not
reject the null?DO NOT reject null hypothesisIf the null
hypothesis was rejected, what is the effect size value (eta
squared):Meaning of effect size measure:There is not a huge
difference What does that decision mean in terms of our equal
pay question: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: Average salaries are the same for each 6
grade levels.Alt. Hypothesis:average salaries are not the same
for two of the 6
gradesABCDEF232741475876223442576677Place B55 in
Outcome range
box.233647506076GroupsCountSumAverageVariance243440496

975A1535323.53333333330.6952380952242843556472B722231
.714285714314.904761904824285677C521342.67.3233560D525
851.617.82465E1275162.583333333314.81060606062462F6453
75.53.524652460ANOVA2366Source of VariationSSdfMSFP-
valueF crit22Between
Groups17686.021428571453537.2042857143409.59411996921.
03856156090236E-352.427040119825Within
Groups379.9785714286448.635876623424What is the p-
value:1.0385Is 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):0.979Meaning of effect size measure:Since eta squared
> 0.5, there sample has a strong effect in drawing conclusions
about the null hypothesis.Interpretation:The results of ANOVA
above indicate the mean salaries of 2 grades in the population
differ.3The 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 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:I reject the nullIf 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:Do not
reject the nullIf 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:I do no reject the nullIf the null hypothesis
was rejected, what is the effect size value (eta
squared):Meaning of effect size measure:What do these
decisions mean in terms of our equal pay question:There is no
significant difference between the average compas betweem
male and female.4Many 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?232323Null
Hypothesis:Average salary of existing employees is the same to
the market23Alt. Hypothesis:everage salaries of existing
employees is above the market.2323Statistical test to use:Paired
T test for two or more dependent
samples.2323SalaryMidpoint23Mean4541.7623Variance368.693
877551263.451428571423Observations505023Pearson
Correlation0.988971782731Hypothesized Mean
Difference031df4931t Stat5.782704498131P(T<=t) one-
tail0.000000252540t Critical one-tail1.676550892640P(T<=t)

two-tail0.00000050548t Critical two-
tail2.009575237148485757676723What is the p-
value:0.00E+00( I do not know why this cell came up with this.
).,0.00E+0023Is P-value < 0.05?yes23Do we REJ or Not reject
the null?Reject the Null31If the null hypothesis was rejected,
what is the effect size value:We did not go over effect size , I
don't really have a formula for this.31Meaning of effect size
measure:3140Interpretation:Results of the above test indicates
that average salaries of existing employees is above the
market.4040485. Using the results up thru this week, what are
your conclusions about gender equal pay for equal work at this
point?4857With the results I came up with, you can come to the
conclusion that there is not a huge57difference between the
average salaries between males and females. So it can lead to a
conclusion that both males and females have equal pay for the
same work in this population.575757575757575767676767
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
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 HighMalesFemales<Reminder:
standard error is the sample standard deviation divided by the
square root of the sample size.>Interpretation: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 HighYes/NoCan 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?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 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 levelOBSERVEDA BCDEFTotalDo manual calculations
per cell here (if desired)M GradA BCDEFFem GradM GradMale
UndFem GradFemale UndMale UndFemale UndSum
=EXPECTEDM GradFor this exercise - ignore the requirement
for a correctionFem Gradfor expected values less than 5.Male
UndFemale UndInterpretation: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
decision mean for our equal pay question: 4Based 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: Ha:Do manual calculations per
cell here (if desired)A BCDEFA BCDEFOBS COUNT -
mMOBS COUNT - fFSum = EXPECTEDWhat 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 Phi
correlation:What does this correlation mean?What does this
decision mean for our equal pay question: 5. How do you
interpret these results in light of our question about equal pay
for equal work?
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:
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
<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
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?

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