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

1.First run a regression analysisusing the BENEFITS column of all datapoints 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?

2.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?

3. 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?

4.Finally, make very specific comments and give reasons regarding any similarities or differences in the output results. Which regression produces the strongest correlationcoefficient result? Why?

Be sure to provide references in APA format for any resources you may use.

Regression analysis

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

Solution:

Scatter plot representing Intrinsic and Benefits

Regression Analysis

r² 0.004 n 25

r -0.061 k 1

Std. Error 1.119 Dep. Var. INTRINSIC

ANOVA table

Source SS df MS F p-value

Regression 0.1088 1 0.1088 0.09 .7707

Residual 28.7816 23 1.2514

Total 28.8904 24

Regression output confidence interval

variables coefficients std. error t (df=23) p-value 95% lower 95% …

A regression analysis on job benefits and intrinsic job satisfaction is analyzed. A graph is created to determine the trend. The R square of the data’s trendline is given.