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Regression Analysis with Excel using Benefits

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Using Excel as your processing tool, work through three simple regression analyses.

First run a regression analysis using the BENEFITS column of all data points in the AIU data set as the independent variable and the INTRINSIC job satisfaction column of all data points in the AIU data set as the dependent variable. Create a graph with the trendline displayed. What is the least squares regression line equation? What are the slope and the y-intercept? What is the R-squared value?
Next, run a regression analysis using the BENEFITS column of all data points in the AIU data set as the independent variable and the EXTRINSIC job satisfaction column of all data points in the AIU data set as the dependent variable. Create a graph with the trendline displayed. What is the least squares regression line equation? What are the slope and the y-intercept? What is the R-squared value?
Next, run a regression analysis using the BENEFITS column of all data points in the AIU data set as the independent variable and the OVERALL job satisfaction column of all data points in the AIU data set as the dependent variable. Create a graph with the trendline displayed. What is the least squares regression line equation? What are the slope and the y-intercept? What is the R-squared value?
Finally, make very specific comments and give reasons regarding any similarities or differences in the output results. Which regression produces the strongest correlation coefficient result? Why?

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Using Excel as your processing tool, work through three simple regression analyses.

First run a regression analysis using the BENEFITS column of all data points in the AIU data set as the independent variable and the INTRINSIC job satisfaction column of all data points in the AIU data set as the dependent variable.
Use Excel, we get the following table (see sheet 1)

SUMMARY OUTPUT

Regression Statistics
Multiple R 0.036211
R Square 0.001311
Adjusted R Square -0.03568
Standard Error 0.808959
Observations 29

ANOVA
df SS MS F Significance F
Regression 1 0.023199 0.023199 0.03545 0.852064
Residual 27 17.66921 0.654415
Total 28 17.69241

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 5.494236 1.296758 4.236902 0.000236 2.833509 8.154963 2.833509 8.154963
X Variable 1 -0.04922 0.261391 -0.18828 0.852064 -0.58555 0.487115 -0.58555 0.487115

Create a graph with the trendline displayed.

See sheet 2 the ...

Solution Summary

Regression analysis with excel using the benefits column is discussed.

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