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INTRODUCTION TO BINARY LOGISTIC REGRESSION

1 INTRODUCTION TO BINARY LOGISTIC REGRESSION BINARY LOGISTIC REGRESSION is a type of REGRESSION analysis that is used to estimate the relationship between a dichotomous dependent variable and dichotomous-, interval-, and ratio-level independent variables. Many different variables of interest are dichotomous , whether or not someone voted in the last election, whether or not someone is a smoker, whether or not one has a child, whether or not one is unemployed, etc. These types of variables are often referred to as discrete or qualitative. Many discrete or qualitative variables can be thought of as events. Dichotomous or dummy variables are usually coded 1, indicating success or yes, and 0, indicating failure or no. The mean of a dichotomous variable coded 1 and 0 is equal to the proportion of cases coded as 1, which can also be interpreted as a probability.

The Logit Transformation So what can we do? As I mentioned earlier, many topics of interest are dichotomous. Logistic regression uses the logit transformation to linearize the non-linear relationship between X and the probability of Y. It does this through the use of odds and logarithms. So, the logit is a

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