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Example Regression Analysis

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Prepare a report using Excel as your processing tool to process 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.
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.
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 for each of the three different regressions. What are the least squares regression line equations for each of the three different regressions? What are the slopes and the y-intercepts? What are the R-squared values for the three different regressions? 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?

The report should be well written and should flow well with no grammatical errors. It should include proper citation in APA formatting in both the in-text and reference pages and include a title page, be double-spaced and in Times New Roman 12-point font. APA formatting is necessary to ensure academic honesty.

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Hi,
Please see the attached Word and Excel file for complete explanations.

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.

The trendline equation is y = 0.05x + 4.817 with
y = Intrinsic job satisfaction
x = Benefits.
Slope = 0.05
Intercept = 4.817
R2 = 0.001

Run a regression analysis ...

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This solution provides assistance with the regression analysis problem.

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