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Job Sat. INTR. EXTR. Benefits
5.2 5.5 6.8 1.4
5.1 5.5 5.5 5.4
5.8 5.2 4.6 6.2
5.5 5.3 5.7 2.3
3.2 4.7 5.6 4.5
5.2 5.5 5.5 5.4
5.1 5.2 4.6 6.2
5.8 5.3 5.7 2.3
5.3 4.7 5.6 4.5
5.9 5.4 5.6 5.4
3.7 6.2 5.5 6.2
5.5 5.2 4.6 6.2
5.8 5.3 5.7 2.3
5.3 4.7 5.6 4.5
5.9 5.4 5.6 5.4
3.7 6.2 5.5 6.2
5.5 5.2 4.6 6.2
5.2 5.5 5.5 5.4
5.1 5.2 4.6 6.2
5.8 5.3 5.7 2.3
5.3 4.7 5.6 4.5
5.9 5.4 5.6 5.4
3.7 6.2 5.5 6.2
5.5 5.2 4.6 6.2
5.6 5.6 4.8 5.1





1. First run a regression analysis using the BENEFITS column of all data points in the data set as the independent variable and the INTRINSIC job satisfaction column of all data points in the data set as the dependent variable.

2. Run a regression analysis using the BENEFITS column of all data points in the data set as the independent variable and the EXTRINSIC job satisfaction column of all data points in the data set as the dependent variable.

3. Run a regression analysis using the BENEFITS column of all data points in the data set as the independent variable and the OVERALL job satisfaction column of all data points in the data set as the dependent variable.

4. Then answer the following questions:
* What are the least squares regression line equations for each of the 3 different regressions?
* What are the slopes and the y-intercepts?
* What are the R-squared values for the 3 different regressions?

5. Finally, make very specific comments and give reasons regarding any similarities or differences in the output results.

6. Which regression produces the strongest correlation coefficient result? Why?

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