The purpose of this web site is to help you understand, run, and interpret logistic regression analyses as quickly and easily as possible.
Many visitors find this web site because they realize that their data does not fit the assumptions of regular linear regression (least-squares regression). Instead they realize they need to use a method specifically designed for data where the Y-variable is binary (all explained below).
Other visitors are users of logistic regression and are seeking answers to a specific question.
But in both cases, this web site is here to help you. I assume that you have some experience with regular linear regression (linear least-squares regression), the kind of experience you probably have had from a introductory statistics course in college or high school.
Logistic regression can be explained quickly and easily by making reference to linear least squares regression and that is the primary approach that we use.
We have all used the word “odds” to describe the probability of something. “Odds are it will rain tomorrow” … (read more)
Least-Squares Regression Background: The Coefficient Table (5 Parts)
In much of this web site, I am going to explain logistic regression by making references and analogies to least-squares regression. This five-part series reviews the required background relating to use of the regression coefficient table. Skip this if you are very familiar with least-squares regression.
Least-squares regression output always has three parts. The most important of these is the coefficient table. The basic structure of the least-squares regression output is reviewed in this article together with an example…(read more)
In logistic regression, as in least-squares regression, you often want to try to assess the effects of the independent X-variables on the dependent Y-variable. The odds-ratio and its interpretation are important here … (read more)