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- September 13, 2020
- By menge

Problem Set Week Five

Complete the problems included in the resources below and submit your work in an Excel document. Be sure to show all of your work and clearly label all calculations.

All statistical calculations will use the Employee Salary Data Set and the Week 5 assignment sheet.

Carefully review the Grading Rubric for the criteria that will be used to evaluate your assignment.

(THIS IS A NEW DATA SET WITH DIFFERENT NUMBERS FROM PREVIOUS CLASSES, TO CATCH PLAIGARISM, Please do your own work!)

Score:

<1 point> Week 5 Correlation and Regression

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. d. 2 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. <1 point> 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? Does this help us answer our equal pay for equal work question? Below 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

Ha: The regression coefficient for each variable is significant Note: technically we have one for each input variable.

Listing it this way to save space. Sal

SUMMARY OUTPUT

Regression Statistics

Multiple R 0.99155907

R Square 0.9831894

Adjusted R Square 0.98084373

Standard Error 2.65759257

Observations

50

ANOVA

df

Regression

Residual

Total SS

MS

F

Significance F

6

17762.3 2960.383 419.15161 1.812E-036

43 303.70033 7.062798

49

18066 Standard

Coefficients

Error

t Stat

P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%

Intercept -1.74962121 3.6183677 -0.483539 0.6311665 -9.046755 5.54751262 -9.0467550427 5.547512618

Midpoint 1.21670105 0.0319024 38.13829 8.66E-035 1.15236383 1.28103827 1.1523638283 1.2810382727

Age

-0.00462801 0.0651972 -0.070985 0.943739 -0.1361107 0.1268547 -0.1361107191 0.1268546987

Performace Rating -0.05659644 0.0344951 -1.640711 0.1081532 -0.1261624 0.01296949 -0.1261623747 0.0129694936

Service -0.04250036 0.084337 -0.503935 0.6168794 -0.2125821 0.12758138 -0.2125820912 0.1275813765

Gender 2.420337212 0.8608443 2.811585 0.0073966 0.68427919 4.15639523 0.684279192 4.156395232

Degree

0.27553341 0.7998023 0.344502 0.7321481 -1.3374217 1.88848848 -1.3374216547 1.8884884833

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:

Intercept

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? Midpoint Age Perf. Rat. Service Gender Degree Using only the significant variables, what is the equation?

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> 3 Salary = Perform 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:

Intercept

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> 4 <2 points> 5 Midpoint Age Perf. Rat. Service Gender Degree Based 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? Why 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 sala

What outcomes in your life or work might benefit from a multiple regression examination rather than a simpler one variable test? ary equality question?