linear regression analysis in spss (procedure and output)
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Linear regression analysis in SPSS is conducted in the following steps: 1) Select the dependent and independent variables by dragging them into the appropriate boxes. 2) Select desired statistics such as regression coefficients, Durbin-Watson, and model fit from the Statistics button. 3) The output displays descriptive statistics, the correlation between variables, regression model summaries including R square and ANOVA tables, and coefficients for the regression model with significance values.
linear regression analysis in spss (procedure and output)
- 1. Linear Regression (Procedure and Output in SPSS )
- 2. Data View Data view represents the cases/data points for different variables. Here we have demand as Dependent Variable and Price as a Independent Variable.
- 3. Variable View Variable view represents the properties of all variables. One can toggle between data and variable view with the shortcut ctrl + t.
- 4. Step 1 Running Linear Regression: Click on the Analyze tab of Menu bar then click Regression option and select Linear.
- 5. Step 2 Select Continuous dependent variable and drag it to Dependent box and select continuous independent variable and drag it to Independent(s) box.
- 6. Step 3 Click on Statistics button to select desired statistics option
- 7. Step 4 Select Estimates under Regression coefficients. Select Durbin Watson under Residual box and select Model fit and Descriptives.
- 8. Step 5 Click on Ok button to get the Output window with Results.
- 9. Descriptive Statistics This table displays the Mean, Std. deviation and sample size for Dependent and Independent variables.
- 10. Correlation This table displays the Correlation between Dependent variable and Independent variable with it’s associated significance value..
- 11. Variable Information This table displays the information about variables in the model and variable that have been removed from model in the Stepwise Method
- 12. Regression Model Summary This table displays the R Square Value (Goodness of Fit for the model) with it’s associated significance value and Std. error of the estimates.
- 13. ANOVA Table This table displays the Statistic for ANOVA with its associated significance value that test the hypothesis that the model is not having any variability in Dependent Variable.
- 14. Coefficients for Regression Model This table displays the Coefficients for the Regression model with it’s T value and significance value.
- 15. Developed and designed by: Address: Mau - Chitrakoot Contact: unexplordsolutions@gmail.com