| Week 5 Correlation and Regression |
| For each question involving a statistical test below, 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. |
| 1 |
Create a correlation table for the variables in our data set. (Use analysis ToolPak function Correlation.) |
| a. Interpret the results. What variables seem to be important in seeing if we pay males and females equally for equal work? |
| 2 |
Below is a regression analysis for salary being predicted/explained by the other variables in our sample (Mid, |
| age, ees, sr, raise, and deg 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.) |
| 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 |
| Sal |
The analysis used Sal as the y (dependent variable) and |
| SUMMARY OUTPUT |
mid, age, ees, sr, g, raise, and deg as the dependent |
| variables (entered as a range). |
| Regression Statistics |
| Multiple R |
0.99215498 |
| R Square |
0.9843715 |
| Adjusted R Square |
0.98176675 |
| Standard Error |
2.59277631 |
| Observations |
50 |
| ANOVA |
| df |
SS |
MS |
F |
Significance F |
| Regression |
7 |
17783.7 |
2540.52 |
377.914 |
8.44043E-36 |
| Residual |
42 |
282.345 |
6.72249 |
| Total |
49 |
18066 |
| Coefficients |
Standard Error |
t Stat |
P-value |
Lower 95% |
Upper 95% |
Lower 95.0% |
Upper 95.0% |
| Intercept |
-4.009 |
3.775 |
-1.062 |
0.294 |
-11.627 |
3.609 |
-11.627 |
3.609 |
| Mid |
1.220 |
0.030 |
40.674 |
0.000 |
1.159 |
1.280 |
1.159 |
1.280 |
| Age |
0.029 |
0.067 |
0.439 |
0.663 |
-0.105 |
0.164 |
-0.105 |
0.164 |
| EES |
-0.096 |
0.047 |
-2.020 |
0.050 |
-0.191 |
0.000 |
-0.191 |
0.000 |
| SR |
-0.074 |
0.084 |
-0.876 |
0.386 |
-0.244 |
0.096 |
-0.244 |
0.096 |
| G |
2.552 |
0.847 |
3.012 |
0.004 |
0.842 |
4.261 |
0.842 |
4.261 |
| Raise |
0.834 |
0.643 |
1.299 |
0.201 |
-0.462 |
2.131 |
-0.462 |
2.131 |
| Deg |
1.002 |
0.744 |
1.347 |
0.185 |
-0.500 |
2.504 |
-0.500 |
2.504 |
| Interpretation: |
Do you reject or not reject the regression null hypothesis? |
| Do you reject or not reject the null hypothesis for each variable? |
| What is the regression equation, using only significant variables if any exist? |
| What does result tell us about equal pay for equal work for males and females? |
| 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. |
| 4 |
Based on all of your results to date, is gender a factor in the pay practices of this company? Why or why not? |
| Which is the best variable to use in analyzing pay practices – salary or compa? Why? |
| 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? |
