Lab9_Logistic Regression

Lab9_Logistic Regression - EXST 7015 - Statistical...

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EXST 7015 - Statistical Inference II, Fall 2011 Lab 9: Logistic Regression OBJECTIVES Logistic Regression is a type of predictive model that can be used when the dependent variable is a categorical variable with two categories – for example male/female, fail/pass, live/die, has disease/doesn’t have disease, wins race/doesn’t win, etc. Thus the dependent variable can take the value 1 with a probability of success (p), or the value 0 with a probability of failure (1-p). The independent or predictor variable can take any form (continuous, dichotomous and/or dummy variable with more than two categories). That is, logistic regression makes no assumption about the distribution of the independent variables. They do not have to be normally distributed, linearly related or of equal variance within each group. The relationship between the independent and dependent variables is not a linear function as shown below: p = e ( α + β 1X1+ β 2X2 +…+ β iXi) /{1+ e ( α + β 1X1+ β 2X2 +…+ β iXi) } where = the constant of the equation and, = the coefficient of the independent variables. The computed value, p, is a probability in the range of 0 to 1. Much of the interpretation of logistic regression model centers on the ratio. Odds = p/(1-p) = e ( α + β 1X1+ β 2X2 +…+ β iXi) where Odds can take on values between zero and infinity. The logarithm of odds,
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Lab9_Logistic Regression - EXST 7015 - Statistical...

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