- Handle qualitative independent variables with a quantitative proxy or use a dummy variable.
- When using a dummy independent variables (such as assigning a number to the degree of consumer confidence), define a collectively exhaustive set of “j” categories, then j-1 (“j minus one”) will give you the number of dummy variables for inclusion in your model.
- Models with dummy independents can easily be misspecified.

**Model types with qualitative dependent variables**

**Probit models**– based on a normal distribution and attempt to estimate the probability that the dependent variable will equal 1.**Logit models**– based on the logistic distribution and like Probit models, they attempt to estimate the probability that the dependent variable will equal 1.**Discriminant Analysis**– creates a score and if the score crosses a threshold then the dependent variable is assigned a 1.

Looking at the big picture, you want your multiple regression model to:

- Have a good theoretical basis and;
- Pass the most stringent statistical tests (refer back to the sub-section “Assumption Violations”).

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