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Regression with a Binary Dependent Variable - Chapter 9

Regression with a Binary Dependent Variable Chapter 9. Michael Ash CPPA. Lecture 22. Course Notes I Endgame I Take-home final I Distributed Friday 19 May I Due Tuesday 23 May (Paper or emailed PDF ok; no Word, Excel, etc.). I Problem Set 7. I Optional, worth up to 2 percentage points of extra credit I Due Friday 19 May I Regression with a Binary Dependent Variable Binary Dependent Variables I Outcome can be coded 1 or 0 (yes or no, approved or denied, success or failure) Examples? I Interpret the Regression as modeling the probability that the Dependent Variable equals one (Y = 1). I Recall that for a Binary Variable , E (Y ) = Pr(Y = 1).

Poisson or negative binomial regression. I Ordered Responses, e.g., completed educational credentials. Ordered logit or probit. I Discrete Choice Data, e.g., mode of travel. Characteristics of choice, chooser, and interaction. Multinomial logit or probit, I Can sometimes convert to several binary problems. I Censored and Truncated Regression ...

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