BUS 308 Week 5 Assignment
| 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? |
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