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Using Excel for Simple Regression Analyses

Using Excel as your processing tool, work through three simple regression analyses and use the data from the attached files.

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?

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?

Attachments

Solution Preview

See the first worksheet in the attached Excel file. It contains a scatterplot and trendline, along with values for the regression equation, slope, y-intercept, and r-squared, for each of the three regression analyses. Below are comments on each of the analyses:

(1) 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 slope is -0.2335, which indicates than an increase by 1 in the BENEFITS score is associated with a decrease by 0.2335 in the INTRINSIC score. The slope is negative, which indicates a negative correlation between the two variables (being more satisfied with benefits is associated with lower intrinsic job satisfaction and vice versa).

The y-intercept is 6.6398, which indicates that a BENEFITS score of 0 is associated with an INTRINSIC score of 6.6398. This means that having no satisfaction with benefits is still associated with a relatively high intrinsic job satisfaction.

R-squared is ...

Solution Summary

The first worksheet in the attached Excel file contains a scatterplot and trendline, along with values for the regression equation, slope, y-intercept, and r-squared, for each of the three regression analyses. Included are comments on each of the analyses. A regression analysis can be run 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. A regression analysis can be run 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.

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