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: <math> Pr(Y=1 | X=x) = x'\beta. </math>
A drawback of this model is that, unless restrictions are placed on <math> \beta </math>, the estimated coefficients can imply probabilities outside the [[unit interval]] <math> [0,1] </math> . For this reason, the [[logit model]] or the [[probit model]] are more commonly used.
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]].
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