The presentation contains the stepwise procedure to conduct Multiple Linear Regression using IBM SPSS.

1. Multiple linear Regression
(Procedure and Output in SPSS)

2. Data View
Data view represents the cases/data points for
different variables. Here we have one continuous
dependent variable, one continuous independent
variable and one categorical dependent variable.

3. Variable View
Variable view represents the properties of all
variables. Here group of categorical variable
are defined as:
1 = Male and 2 = female

4. Step 1 (Creating Dummy for Categorical
Variable)
Info: We are going to create dummy
for categorical variable. Here we have
2 subgroups so we need to create 1(2-
1) dummy variable.
Click on the Transform tab of
Menu bar then select
Recode into different
variables.

5. Step 2
Select the categorical
variable and drag it to
Numeric variable - >Output
variable box.

6. Step 3
Type new name and label for
dummy variable and click
change. Then click on the Old
and New variable button to
give the new values for
dummy.

7. Step 4
Info: We are creating dummy for Male
group (coded as 1) while Female group
(coded as 2) will be base.
Give old value for Male group
as 1 in the Old value box and
new value as 1 in New Value
box. Then click on add button
to add the preferred changes in
Old->New box.

8. Step 5
Select all other values (in this case
female) 2 in the Old value box and
new value as 0 in New Value box.
Then click on add button to add the
preferred changes in
Old->New box.

9. Step 6
Click on Ok button
to run the process
to create Dummy
variable.

10. Step 7
Here in the data view you
can see the new dummy
variable has been added
to the dataset.

11. Step 8 (Running Multiple Regression)
Running Linear Regression:
Click on the Analyze tab of
Menu bar then click
Regression option and select
Linear.

12. Step 9
Select Continuous dependent variable and
drag it to Dependent box and select
continuous and Categorical independent
variable and drag it to Independent(s) box.

13. Step 10
Click on Statistics button to
select desired statistics
option

14. Step 11
Select Estimates and
Confidence Interval under
Regression coefficients.
Select Durbin Watson under
Residual box and select
Model fit and Descriptive.

15. Step 12
Click on Ok button to get the
Output window with Results.

16. Descriptive Statistics
This table displays the Mean, Std.
deviation and sample size for
Dependent and Independent
variables.

17. Correlation
This table displays the Correlation
between Dependent variable and
Independent variable with it’s associated
significance value..

18. Variable Information
This table displays the information about variables
in the model and variable that have been
removed from model in the Stepwise Method

19. 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.

20. ANOVA Table
This table displays the Statistic for ANOVA with its associated
significance value that test the hypothesis that all of the
coefficients of Independent Variables are Zero.

21. Coefficients for Regression Model

22. Residual Statistics
This table displays the
Descriptive Statistic for
Residuals.

23. Developed and designed by:
Address: Mau - Chitrakoot
Contact: unexplordsolutions@gmail.com