# 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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