## About This Site

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.

# Table of Contents

Scan this column for topics:

You should think about using logistic regression when your *Y*-variable takes on only two values. Such a variable ….. (read more)

It is an error to use regular least-squares regression when the dependent *Y*-variable is binary … (read more)

Logistic regression analysis creates a model (a mathematical equation) that predicts the proba- bility that the *Y*-variable takes on a value of 1 … (read more)

Just to be sure that you have a clear idea of what a data set that is appropriate for logistic regression analysis looks like … (read more)

For some reason, which remains mysterious to me, all throughout … (read more)

We have all used the word “odds” to describe the probability of something. “Odds are it will rain tomorrow” … (read more)

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)

Perhaps, most important use of the regression coefficient table is to determine which X-variables matter … (read more)

In this Article, I discuss the next use of the regression coefficients, namely to try to assess the impact of each of the X-variables … (read more)

Another key use of the regression coefficients is to make predictions … (read more)

Finally, in this Article, I discuss assessing the uncertainty of the regression coefficient estimates … (read more)

This series of Articles discusses the logistic regression coefficient table and its main uses. The discussion here parallels the review of least-squares regression above.

As with least-squares linear regression, the most important part of the output of a logistic regression is the regression coefficient table … (read more)

This article discusses one of the most important uses of the coefficient table, determining which variables matter… (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)

How to use the logistic regression coefficients and the logistic regression equations to predict probabilities. (read more)

How to assess the uncertainty in the estimated logistic regression coefficients and the corresponding odds-ratios. (read more)

In this article I address how much data you need in order to run standard logistic regression analyses.(read more)