![]() ![]() ![]() Logistic and linear regression belong to the same family of models called GLM ( Generalized Linear Model): in both cases, an event is linked to a linear combination of explanatory variables.įor linear regression, the dependent variable follows a normal distribution N(μ,σ) where μ is a linear function of the explanatory variables. Models for logistic regression Binomial logistic regression For example, in the medical field, we seek to assess from what dose of a drug, a patient will be cured. The principle of the logistic regression model is to explain the occurrence or not of an event (the dependent variable noted Y) by the level of explanatory variables (noted X). It is widely used in the medical field, in sociology, in epidemiology, in quantitative marketing (purchase or not of products or services following an action) and in finance for risk modeling (scoring). Logistic regression is a frequently used method because it allows to model binomial (typically binary) variables, multinomial variables (qualitative variables with more than two categories) or ordinal (qualitative variables whose categories can be ordered). Definition of the logistic regression in XLSTAT Principle of the logistic regression ![]()
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