In statistics, a logistic regression is a type of regression analysis used for predicting the outcome of a categorical dependent variable (a dependent variable that can take on a limited number of categories) based on one or more predictor variables. The probabilities describing the possible outcome of a single trial are modeled, as a function of explanatory variables, using a logistic function.

Logistic regression measures the relationship between a categorical dependent variable and a continuous independent variable (or several), by converting the dependent variable to probability scores. As such, logistic regression treats the same set of problems probit regression treats using similar techniques.

Why is Logistic Regression Useful?

Logistic regression is very useful for marketing. For example, if you are working on customer retention for a company, you could find the variables that are most important to retention. From there, a logistic regression that would give the probability, based on past behavior that people would leave. This would show you the customers that are most likely to leave. You could then use this information, put it in a pivot table with the revenue from the customer, and know where to best focus your attention.

The following logisitic regression video tutorials should help you get a feel for the functionalitly. We think they are really useful, and hope you can find come good uses for them.

Video Tutorial by Prof Raj Venkatesan on Logistic Regression