Linear probability model

In statistics, a linear probability model is a special case of a binomial regression model. Here the dependent variable for each observation takes values which are either 0 or 1. The probability of observing a 0 or 1 in any one case is treated as depending on one or more explanatory variables. For the 'linear probability model', this relationship is a particularly simple one, and allows the model to be fitted by simple linear regression. In statistics, a linear probability model is a special case of a binomial regression model. Here the dependent variable for each observation takes values which are either 0 or 1. The probability of observing a 0 or 1 in any one case is treated as depending on one or more explanatory variables. For the 'linear probability model', this relationship is a particularly simple one, and allows the model to be fitted by simple linear regression. The model assumes that, for a binary outcome (Bernoulli trial), Y {displaystyle Y} , and its associated vector of explanatory variables, X {displaystyle X} ,