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			<gallery>
			File:Exam_pass_logistic_curve.svg\|Logistic curve showing exam pass probability
			File:Exam_pass_logistic_curve.svg\|Logistic curve showing exam pass probability
			File:Logistic-curve.svg\|Logistic curve
			File:Odds_Ratio-1.jpg\|Odds ratio illustration
			File:Logistic_Regression_in_SPSS.png\|Logistic regression analysis in SPSS
			File:Logistic-sigmoid-vs-scaled-probit.svg\|Comparison of logistic sigmoid and scaled probit functions
			</gallery>

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2024-02-22T21:44:24Z

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'''Logistic Regression''' is a statistical model that in its basic form uses a logistic function to model a binary dependent variable, although many more complex extensions exist. In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic model (a form of binary regression).

== Mathematical Model ==

Mathematically, a binary logistic model has a dependent variable with two possible values, such as pass/fail which is represented by an indicator variable, where the two values are labeled "0" and "1". In the logistic model, the log-odds (the logarithm of the odds) for the value labeled "1" is a linear combination of one or more independent variables ("predictors"); the independent variables can each be a binary variable (two classes, coded by an indicator variable) or a continuous variable (any real value). The corresponding probability of the value labeled "1" can vary between 0 (certainly the value "0") and 1 (certainly the value "1"), hence the labeling; the function that converts log-odds to probability is the logistic function, hence the name. The unit of measurement for the log-odds scale is called a logit, from logistic unit, hence the alternative names. Analogous models with a different sigmoid function instead of the logistic function can also be used, such as the probit model; the defining characteristic of the logistic model is that increasing one of the independent variables multiplicatively scales the odds of the given outcome at a constant rate, with each independent variable having its own parameter; for a binary independent variable this generalizes the odds ratio.

== Applications ==

Logistic regression can be used in various fields, including machine learning, most medical fields, and social sciences. For example, the Trauma and Injury Severity Score (TRISS), which is widely used to predict mortality in injured patients, was originally developed by Boyd et al. using logistic regression. Many other medical scales used to assess severity of a patient have been developed using logistic regression. Logistic regression may be used to predict the risk of developing a given disease (e.g. diabetes; coronary heart disease), based on observed characteristics of the patient (age, sex, body mass index, results of various blood tests, etc.).

== See Also ==
* [[Generalized linear model]]
* [[Probit model]]
* [[Machine learning]]
* [[Regression analysis]]

[[Category:Statistical models]]
[[Category:Regression analysis]]
[[Category:Binary classification]]
[[Category:Generalized linear models]]
[[Category:Medical statistics]]

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Logistic regression - Revision history

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