Score: |
Week 5 |
Correlation and Regression |
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<1 point> |
1. |
Create a correlation table for the variables in our data set. (Use analysis ToolPak or StatPlus:mac LE function Correlation.) |
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a. |
Reviewing the data levels from week 1, what variables can be used in a Pearson’s Correlation table (which is what Excel produces)? |
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b. Place table here (C8): |
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c. |
Using r = approximately .28 as the signicant r value (at p = 0.05) for a correlation between 50 values, what variables are |
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significantly related to Salary? |
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To compa? |
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d. |
Looking at the above correlations – both significant or not – are there any surprises -by that I |
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mean any relationships you expected to be meaningful and are not and vice-versa? |
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e. |
Does this help us answer our equal pay for equal work question? |
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<1 point> |
2 |
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Below is a regression analysis for salary being predicted/explained by the other variables in our sample (Midpoint, |
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age, performance rating, service, gender, and degree variables. (Note: since salary and compa are different ways of |
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expressing an employee’s salary, we do not want to have both used in the same regression.) |
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Plase interpret the findings. |
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Ho: The regression equation is not significant. |
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Ha: The regression equation is significant. |
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Ho: The regression coefficient for each variable is not significant |
Note: technically we have one for each input variable. |
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Ha: The regression coefficient for each variable is significant |
Listing it this way to save space. |
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Sal |
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SUMMARY OUTPUT |
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Regression Statistics |
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Multiple R |
0.9915591 |
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R Square |
0.9831894 |
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Adjusted R Square |
0.9808437 |
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Standard Error |
2.6575926 |
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Observations |
50 |
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ANOVA |
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