The logistic model (also called logit model) is a natural candidate when one is interested in a binary outcome. For instance, a researcher might be interested in knowing what makes a politician successful or not. For the purpose of this blog post, “success” means the probability of winning an election. In that case, it would be sub-optimal to use a linear regression model to see what factors are associated with successful politicians, as the outcome variable is binary (a politician either wins or loses an election). The linear model is built around the idea that the outcome variable is continuous.