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Lecture 20 - Logistic Regression

Lecture 20 - Logistic RegressionStatistics 102 Colin RundelApril 15, 2013 Background1 Background2 GLMs3 Logistic Regression4 Additional ExampleStatistics 102 Lec 20 Colin RundelBackgroundRegression so far ..At this point we have covered:Simple linear regressionRelationship between numerical response and a numerical or categoricalpredictorMultiple regressionRelationship between numerical response and multiple numericaland/or categorical predictorsWhat we haven t seen is what to do when the predictors are weird(nonlinear, complicated dependence structure, etc.) or when the responseis weird (categorical, count data, etc.)Statistics 102 (Colin Rundel)Lec 20 April 15, 20132 / 30 BackgroundRegression so far ..At this point we have covered:Simple linear regressionRelationship between numerical response and a numerical or categoricalpredictorMultiple regressionRelationship between numerical response and multiple numericaland/or categorical predictorsWhat we haven t seen is what to do when the predictors are weird(nonlinear, complicated dependence structure, etc.)

Logistic Regression Logistic Regression Logistic regression is a GLM used to model a binary categorical variable using numerical and categorical predictors. We assume a binomial distribution produced the outcome variable and we therefore want to model p the probability of success for a given set of predictors.

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