![]() ![]() Theory is only briefly reviewed, as these methods should be supported by a full course (or more). In today's session we introduce Python libraries that enable estimating models of each of these types. If the dependent variable is categorical (or represents a choice outcome) with a small number of values (like travel modes), then the most common model form is Multinomial Logit (MNL) If the dependent variable is categorical (or represents a choice outcome) with two values (rent or own for example) than a binary logit model or logistic regression is usually the preferred model. If the dependent variable (the variable we are trying to explain or predict) is continuous (has a large range, like price of housing), then we use multiple regression. This material uses Python to demonstrate some aspects of statistical models with continuous or categorical values to be predicted. Statistical Modeling With Python Multiple Regression and Discrete Choice Models ![]()
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