BUS 308 Week 5 Assignment

Score:Week 5 Correlation and Regression

<1 point>1.    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):

c.Using r = approximately .28 as the signicant r value (at p = 0.05) for a correlation between 50 values, what variables are

significantly 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?

<1 point>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.

Sal

SUMMARY OUTPUT

Regression Statistics

Multiple R0.991559

R Square0.983189

Adjusted R Square0.980844

Standard Error2.657593

Observations50

ANOVA

dfSSMSFSignificance F

Regression617762.32960.38419.15161.81E-36

Residual43303.70037.0628

Total4918066

CoefficientsStandard Errort StatP-valueLower 95%Upper 95%Lower 95.0%Upper 95.0%

Intercept-1.749623.618368-0.48350.631166-9.046765.547513-9.046765.547513

Midpoint1.2167010.03190238.13838.66E-351.1523641.2810381.1523641.281038

Age-0.004630.065197-0.0710.943739-0.136110.126855-0.136110.126855

Performace Rating-0.05660.034495-1.64070.108153-0.126160.01297-0.126160.012969

Service-0.04250.084337-0.50390.616879-0.212580.127581-0.212580.127581

Gender2.4203370.8608442.811590.0073970.6842794.1563950.6842794.156395

Degree0.2755330.7998020.34450.732148-1.337421.888489-1.337421.888488

Note: 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.ServiceGenderDegree

What 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? 

<1 point>3Perform a regression analysis using compa as the dependent variable and the same independent

variables 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 hypotheses

Ho:

Ha:

Coefficient hyhpotheses (one to stand for all the separate variables)

Ho:

Ha:

Place D94 in output box.

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.ServiceGenderDegree

What 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? 

<1 point>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?

<2 points>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?

See comments at the right of the data set.

IDSalaryCompaMidpointAgePerformance RatingServiceGenderRaiseDegreeGender1Grade

8231233290915.80FAThe 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.

1123123411001914.80FA

14241.04323329012160FAThe column labels in the  table mean:

15241.043233280814.90FAID – Employee sample number Salary – Salary in thousands     

23231233665613.31FAAge – 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 raise

35241.043232390415.31FAGrade – job/pay gradeDegree (0= BS\BA 1 = MS)

36231232775314.31FAGender1 (Male or Female)Compa - salary divided by midpoint

37220.956232295216.21FA

42241.0432332100815.70FA

3341.096313075513.60FB

18361.1613131801115.61FB

20341.0963144701614.81FB

39351.129312790615.51FB

7411.0254032100815.70FC

13421.054030100214.71FC

22571.187484865613.80FD

24501.041483075913.81FD

45551.145483695815.20FD

17691.215727553130FE

48651.145734901115.31FE

28751.119674495914.41FF

43771.1496742952015.51FF

19241.043233285104.61MA

25241.0432341704040MA

40251.086232490206.30MA

2270.87315280703.90MB

32280.903312595405.60MB

34280.903312680204.91MB

16471.175404490405.70MC

27401403580703.91MC

41431.075402580504.30MC

5470.9794836901605.71MD

30491.024845901804.30MD

1581.017573485805.70ME

4661.15757421001605.51ME

12601.0525752952204.50ME

33641.122573590905.51ME

38560.9825745951104.50ME

44601.0525745901605.21ME

46651.145739752003.91ME

47621.087573795505.51ME

49601.0525741952106.60ME

50661.1575738801204.60ME

6761.1346736701204.51MF

9771.149674910010041MF

21761.1346743951306.31MF

29721.074675295505.40MF