A. Determine the regression equation.
b. What is the value of R-squared? Comment on the value.
c. Conduct a global hypothesis test to determine whether any of the independent variables are different from zero.
d. Conduct individual hypothesis tests to determine whether any of the independent variables can be dropped.
e. If variables are dropped, recomputed the regression equation and R-squared.
First of all, please see attached file (either in SPSS or Excel). This is the regression output. I will refer to it, in my response.
a) The regression equation is the following...
incomehat = 27.045 + .951*gender + .021*home + .68*yrs_educ + .187*mort_pay - .015*age
I should mention that I have included the variable mort_pay in 000's of $'s (ie, have divided each number in the data list by 1000, to get it into the same scale as income, which is also in 000's of $'s.)
b) R-square = .763
This means that 76.3% of the ...
A step-by-step solution is provided. The solution illustrates the process of estimating a regression model and then analyzing it. The regression coefficients (and their statistical significance) and the R-squared statistic are the focus of our attention. Detailed explanations are provided for each task. SPSS and Excel output included as attachments.