Logistic regression

In statistics, logistic regression, or logit model[1] is a regression model where the dependent variable (DV) is categorical. This article covers the case of binary dependent variables—that is, where it can take only two values, such as pass/fail, win/lose, alive/dead or healthy/sick. Cases with more than two categories are referred to as multinomial logistic regression, or, if the multiple categories are ordered, as ordinal logistic regression.[2]

在统计学中,逻辑回归,或者逻辑模型,是一个因变量(DV,dependent variable)是分类的回归模型。本文介绍了二元因变量的情况——也就是说,因变量只能取两个值,如通过/失败,获胜/失败,活着/死亡 或者 健康/生病。有两个以上分类的情况被称为多项逻辑回归,或者,如果多个分类是有序的,称为序数逻辑回归。

Logistic regression was developed by statistician David Cox in 1958.[2][3] The binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). As such it is not a classification method. It could be called a qualitative response/discrete choice model in the terminology of economics.

逻辑回归由统计学家 David Cox 在 1958 年提出。二元逻辑模型用于估计二元响应的概率,基于一个或多个预测(独立)变量(特性)。因此它不是一个分类方法。在经济学术语中,它可以被称为定性反应/离散选择模型。

Logistic regression measures the relationship between the categorical dependent variable and one or more independent variables by estimating probabilities using a logistic function , which is the cumulative logistic distribution. Thus, it treats the same set of problems as probit regression using similar techniques, with the latter using a cumulative normal distribution curve instead. Equivalently, in the latent variable interpretations of these two methods, the first assumes a standard logistic distribution of errors and the second a standard normal distribution of errors.[citation needed]

逻辑回归 通过使用逻辑函数估计概率 来测量分类因变量和一个或多个独立自变量间的关系,其中逻辑函数是累计逻辑分布。因此它使用类似的技术,将一组同样的问题作为概率回归来处理,其中后者使用了累积正态分布曲线。同样,在这两种方法的潜在变量解释中,第一个假设了误差的标准对数分布,第二个假设了误差的标准正态分布。