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One situation where the linear probability model is commonly used, is when the data set is so large that [[maximum likelihood]] estimation of a logit or probit model is computationally difficult. For the linear probability model, <math> E[Y|X] = \Pr(Y=1|X) =x'\beta</math>, so the parameter <math> \beta </math> can be estimated using [[least squares]].
Other Drawbacks:
Unrealistic marginal effects at low and high parts of the distribution.
[[Category:Regression analysis]]
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