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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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    https://brainmass.com/statistics/regression-analysis/regression-analysis-with-excel-218774

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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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