1Create a correlation table for the variables in our data set. (Use analysis ToolPak function Correlation.) a. Interpret the results. What…
| 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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