Probability and Statistics > Regression Analysis > Logistic Regression / Logit Model

In order to understand logistic regression (also called the logit model), you may find it helpful to review these topics:

The Nominal Scale.

What is Linear Regression?

## Simple Logistic Regression

Simple logistic regression is almost identical to linear regression. However, linear regression uses two measurements and logistic regression uses **one measurement and one nominal value**. The measurement variable is always the independent variable. It’s used when you want to find the probability of getting a certain nominal variable when you have a particular measurement variable.

## Logistic Regression, ANOVA and Student’s T-Tests

ANOVA and Student’s T-Tests can also be used to analyze data that has one nominal variable and one measurement variable. Logistic regression is used when you want to *predict the probability for the nominal variable.* Here’s an example to clarify that statement:

You measure the BMI for a group of 50-year-old women, then ten years later you survey the women to see who had a myocardial infarction (a heart attack). You could evaluate your data in different ways, depending on your goal:

**Student’s T-Test**: You can test the null hypothesis that BMI is not linked to myocardial infarction.**Logistic Regression:**You can predict the probability that a 50-year-old woman with a certain BMI would have a heart attack in the next decade.

## Logistic Regression vs. Linear Regression

In linear regression, you must have two measurements (x and y). In logistic regression, your dependent variable (your y variable) is nominal. In the above example, your y variable could be “had a myocardial infarction” vs. “did not have a myocardial infarction.” However, you can’t plot those nominal variables on a graph, so what you do is plot the **probability** of each variable (from 0 to 1). For example, your study might show that a woman with a BMI of 30 has a 4% chance of having a heart attack within the next ten years; you could plot that as 30 for the X variable and 0.04 for the Y variable.

## Comparison with Discriminant Analysis

Discriminant analysis is a classification method that gets its name from discriminating, the act of recognizing a difference between certain characteristics. The two goals are:

- Construction of a classification method to separate members of a population.
- Using the classification method to allocate new members to groups within the population.

Discriminant Analysis is used when you have a set of naturally formed groups and you want to find out which continuous variables discriminate between them. The simplest example of DA is to use a single variable to predict where a member will fall in a population. For example, using high school GPA to predict whether a student will drop out of college, graduate from college, or graduate with honors.

A more complex example: you might want to find out which variables discriminate between credit applicants who are a high, medium, or low risk for default. You could collect data on credit card holder characteristics and use that information to determine what variables are the best predictors for whether a particular person will be a high, medium, or low risk. New observations (in this case, new applicants) could then be allocated to a particular group.

As well as the credit and banking industries, other uses for Discriminant Analysis include:

- Developing facial recognition technology.
- Classifying biological species.
- Classifying tumors.
- Determining the best candidates for college admissions.

Logistic Regression is often preferred over Discriminant Analysis as it can handle categorical variables *and* continuous variables. Logistic Regression also does not have as many assumptions associated with it. For example, Discriminant Analysis requires the assumptions of equal variance-covariance within each group, multivariate normality, and the data must be linearly related. Logistic Regression does not have these requirements.

**Next**: Goodness of fit for logistic regression with the Hosmer-Lemeshow test.

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