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LogisticRegressionNotes.tex
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\section{Logistic Regression}
%http://data.princeton.edu/wws509/notes/c3.pdf
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%http://case.truman.edu/Documents/SPSS%20Logistic%20Regression.pdf
%http://www.ats.ucla.edu/stat/spss/output/logistic.htm
Logistic regression, also called a logit model, is used to model \textbf{dichotomous} response variables.
In the logit model the \textbf{log odds} of the outcome is modeled as a linear combination of the predictor variables.
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\subsection{Odds Ratio}
\subsection{Logits}
dened as the ratio of the probability to its complement, or the ratio of
favorable to unfavorable cases. If the probability of an event is a half, the
odds are one-to-one or even.
\[ odds_i = \frac{\pi_i}{1-\pi_i} \]
calculating the logit or log-odds
\[ Log(odds_i) = log left( \frac{\pi_i}{1-\pi_i} \right) \]
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