Categorical Independent Variable And Categorical Dependent Variable. This model is the most popular for binary dependent variables. Probability Analyse-classify-discrimnant analysis- save- discrimination scores and group membership Note.
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The predictors can be anything nominal or ordinal categorical or continuous or a mix. In the logistic regression model the dependent variable is binary. If the dependent variable is normally distributed and you have a categorical independent variable that has just 2 levels dichotomous then you use INDEPENDENT T.
A categorical variable values are just names that indicate no ordering.
Probability Analyse-classify-discrimnant analysis- save- discrimination scores and group membership Note. This model is the most popular for binary dependent variables. We need to convert the categorical variable gender into a form that makes sense to regression analysis. It is highly recommended to start from this model setting before more sophisticated categorical modeling is carried out.
