| 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 | 0.9921549762 |
| | R Square | 0.9843714969 |
| | Adjusted R Square | 0.9817667464 |
| | Standard Error | 2.5927763074 |
| | Observations | 50 |
| | ANOVA |
| | | df | SS | MS | F | Significance F |
| | Regression | 7 | 17783.6554628284 | 2540.5222089755 | 377.9139268848 | 8.44042689148567E-36 |
| | Residual | 42 | 282.3445371716 | 6.7224889803 |
| | 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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bus_308_week_5_assignment_1.xlsx
