Multivariate Logistic Regression - McGill University

1 Multivariate Logistic RegressionAs in univariate Logistic Regression , let (x) represent the probability of an event thatdepends onpcovariates or independent variables. Then, using an formulationfor modeling the probability, we have: (x) =e 0+ 1X1+ 2X2+..+ pXp1 +e 0+ 1X1+ 2X2+..+ pXpSo, the form is identical to univariate Logistic Regression , but now with more than onecovariate. [Note: by univariate Logistic Regression , I mean Logistic Regression withone independent variable ; really there are two variables involved, the independentvariable and the dichotomous outcome, so it could also be termed bivariate.]To obtain the corresponding logit function from this, we calculate (lettingXrepresentthe whole set of covariatesX1,X2,..,Xp):logit[ (X)] = ln[ (X)1 (X)]= ln e 0+ 1X1+ 2X2+.

Logistic regression with dummy or indicator variables Chapter 1 (section 1.6.1) of the Hosmer and Lemeshow book described a data set called ICU. Deleting the ID variable, there are 20 variables in this data set, which we describe in the table below:

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  Logistics, Variable, Dummy, Regression, Multivariate, Multivariate logistic regression

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