Linear probability model: Difference between revisions

Revision as of 15:11, 17 December 2023

Revision as of 06:40, 9 January 2025

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:<math>\beta_0 = \frac {b_0+a}{2a},\;\; \beta=\frac{\mathbf b}{2a}.</math>

This method is a general device to obtain a conditional probability model of a binary variable: if we assume that the distribution of the error term is Logisticlogistic, we obtain the [[logit model]], while if we assume that it is the Normalnormal, we obtain the [[probit model]] and, if we assume that it is the logarithm of a Weibull distribution, the [[Generalized linear model|complementary log-log model]].

== See also ==