predict the selling price in ‘000s of dollars with all of the other variables potentially being
Download the Real Estate data file and open it up in SPSS. Use a stepwise regression to
predict the selling price in ‘000s of dollars with all of the other variables potentially being
independent variables. In a Word document, describe the process that you used to get your results. Which variables ended up being significant predictors of the selling price? What is
the regression equation? Using the Cronk book as a model, provide interpretation and
analysis of the regression coefficients and explain what they mean in terms of the effect of
each IV on the DV. What is the adjusted R squared value? Provide interpretation and analysis of what that means.
SPSS Data Attached
In addition, you should create an addendum to your “cheat sheet” from Module 3 and include the following tests, what they can be used to show, and the requirements for their use:
Multiple discriminant analysis
Logistic regression
Canonical correlation
Module 3 Cheat Sheet is attached THEREFORE the additional info can be added:
Multiple discriminant analysis
Logistic regression
Canonical correlation
Expert Answer:
To perform a stepwise regression in SPSS, follow these steps:
- Open the Real Estate data file in SPSS and select “Analyze” from the top menu.
- Select “Regression” from the list of options and click “OK”.
- In the “Dependent” field, select “Selling_Price”.
- In the “Independent(s)” field, select all of the other variables as potential independent variables.
- Click the “Method” button and select “Stepwise” as the method.
- Click the “Statistics” button and select any additional statistics that you would like to see in the output, such as p-values and 95% confidence intervals.
- Click “OK” to run the analysis.
The output will show the results of the stepwise regression, including the regression equation, the p-values for each independent variable, and the adjusted R squared value.
The significant predictors of selling price are the variables that have a p-value less than 0.05. These variables will be included in the final regression equation. The regression coefficients for these variables represent the estimated change in the dependent variable (selling price) for a one unit change in the independent variable, holding all other variables constant.
The adjusted R squared value represents the proportion of the variance in the dependent variable that can be explained by the independent variables. A higher adjusted R squared value
