Dealing with Categorical variables in Multiple Regression I have a data having 2 continuous and 4 categorical variables. Each categorical variable has 3 levels. I want to know how to include the variables  in the model. I am using SPSS
Variables:


*

*Sales - Dependent Variable

*Retail Price

*Location : TX, CA, PY

*Display Location in Store : DL1, DL2, DL3

*Price Point : 2.5, 4.5, 1.0


I have created 9 dummy variables. 3 for Location, 3 for Display Location in Store and 3 for Price Point.
I am confused on how to use these independent variables.
How to add the independent variables into the models. Should I add all the nine dummy variables or two dummy variables of each categorical variables along with Retail Price.
I searched on net but , did not get any solution. I would appreciate any kind of help.
Thanks in advance.
 A: It has been a while since I last used SPSS, but I am pretty sure that it can handle the creation and inclusion of dummy variables automatically (or mostly automatically).  You should not need to create the dummy variables yourself, just try including the categorical variables as predictors when specifying the model and see what happens.  You do need to make sure that SPSS recognizes that they are categorical variables, this is not a problem if the data is stored as "TX", "CA", "PY", but with the Price Point variable you may need to somehow specify it as a categorical variable/factor.
If you want to do it by hand, then you can only include 2 dummy variables for each variable (the one that you do not include will become the baseline level).
A: If you start with the original categorical variables, the result depends on which procedure you use.  REGRESSION treats all variables as continuous, so it would not give reasonable results here.  You would need to use the dummy variables.
If you use GLM, you classify the regressors as scale or factor, and it will do the right thing.  Similarly with GENLIN and several other procedures related to linear models.
