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Logistic Regression

Home » Glossaries » Logistic Regression

Logistic Regression

Logistic Regression:

Logistic regression is used with binary data when you want to model the probability that a specified outcome will occur. Specifically, it is aimed at estimating parameters a and b in the following model:

Li = log  pi


1-pi

= a + b xi,

where pi is the probability of a success for given value xi of the explanatory variable X.

Use of the log of the odds p/(1-p) (the logit) guarantees that the predicted value of p will always be between 0 and 1.

See also: Regression analysis.

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Courses Using This Term

Categorical Data Analysis
This course will teach you the analysis of contingency table data. Topics include tests for independence, comparing proportions as well as chi-square, exact methods, and treatment of ordered data. Both 2-way and 3-way tables are covered.
Generalized Linear Models
This course will explain the theory of generalized linear models (GLM), outline the algorithms used for GLM estimation, and explain how to determine which algorithm to use for a given data analysis.
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