Assignment Help >> Applied Statistics
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 viceversa?
e. Does this help us answer our equal pay for equal work question?
2 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

Regression Statistics 

Multiple R 
0.9915591 

R Square 
0.9831894 

Adjusted R Square 
0.9808437 

Standard Error 
2.6575926 

Observations 
50 
ANOVA 






df 
SS 
MS 
F 
Significance F 
Regression 
6 
17762.3 
2960.38 
419.1516 
1.812E36 
Residual 
43 
303.7003 
7.0628 


Total 
49 
18066 





Coefficients 
Standard Error 
t Stat 
Pvalue 
Lower 95% 
Upper 95% 
Lower 95.0% 
Upper 95.0% 

Intercept 
1.749621 
3.618368 
0.4835 
0.631166 
9.046755 
5.5475126 
9.04675504 
5.54751262 

Midpoint 
1.2167011 
0.031902 
38.1383 
8.66E35 
1.1523638 
1.2810383 
1.152363828 
1.28103827 

Age 
0.004628 
0.065197 
0.071 
0.943739 
0.136111 
0.1268547 
0.13611072 
0.1268547 

Performace Rating 
0.056596 
0.034495 
1.6407 
0.108153 
0.126162 
0.0129695 
0.12616237 
0.01296949 

Service 
0.0425 
0.084337 
0.5039 
0.616879 
0.212582 
0.1275814 
0.21258209 
0.12758138 

Gender 
2.4203372 
0.860844 
2.81159 
0.007397 
0.6842792 
4.1563952 
0.684279192 
4.15639523 

Degree 
0.2755334 
0.799802 
0.3445 
0.732148 
1.337422 
1.8884885 
1.33742165 
1.88848848 
What is the value of the F statistic:
What is the pvalue associated with this value:
Is the pvalue <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:
What is the coefficient's pvalue for each of the variables:
Is the pvalue < 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?
Is gender a significant factor in salary:
If so, who gets paid more with all other things being equal?
How do we know?
3 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:
Interpretation:
For the Regression as a whole:
What is the value of the F statistic:
What is the pvalue associated with this value:
Is the pvalue < 0.05?
Do you reject or not reject the null hypothesis:
What does this decision mean for our equal pay question:
What is the coefficient's pvalue for each of the variables:
Is the pvalue < 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?
Is gender a significant factor in compa:
If so, who gets paid more with all other things being equal?
How do we know?
4 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?
5 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 salary equality question?
What outcomes in your life or work might benefit from a multiple regression examination rather than a simpler one variable test?