Question

What are the steps to run a Multiple Linear Regression Model in
SPSS?

Answer 1

To run a Multiple Linear Regression Model in SPSS, ascertain the independent and dependent variables, create a scatter plot, use the regression function to find the best-fit line, assess the correlation coefficient and R-squared value, and interpret the regression equation and model diagnostics.

Step-by-step explanation:

Running a Multiple Linear Regression Model in SPSS involves several key steps. First, it is crucial to understand the nature of the data and decide which variable will be the independent variable and which will be the dependent variable. Once variables are determined, the following steps can be followed:

1. Enter the data into SPSS and define the variables appropriately in the Variable View.
2. Generate a scatter plot to visualize the relationship between the independent and dependent variables. You can create a scatter plot by going to Graphs > Legacy Dialogs > Scatter/Dot.
3. Use the Analyze menu and navigate to Regression > Linear to open the linear regression dialog box. In this dialog box, you will configure your independent and dependent variables.
4. Specify the dependent variable and one or more independent variables in the provided fields.
5. If needed, check the statistics you would like to include in the output, such as estimates, model fit, and diagnostics for potential outliers or influential cases.
6. Click OK to run the regression analysis. SPSS will output the regression equation, coefficients, the correlation coefficient, and other model diagnostics such as the significance of the model and the coefficients.
7. Interpret the results, including the regression equation in the form ý = a + bx, where 'a' is the intercept and 'b' are the slope coefficients for the independent variables.
8. Finally, assess the model's goodness-of-fit through the R-squared value, and review residual plots to ensure the model assumptions are met.

Remember to interpret the results in the context of your research question, and consider if the least-squares line is valid for any extrapolated predictions.