Logit Regression and Transformation (More...)

This transformation is referred to as the logit or logistic transformation.Note that p' can theoretically assume any value between minus and plus infinity.Since the logit transform solves the issue of the 0/1 boundaries for the original dependent variable (probability), we could use those (logit transformed) values in an ordinary linear regression equation.In fact,if we perform the logit transform on both sides of the logit regression equation stated earlier,we obtain the standard linear multiple regression model:
p' = (b0 +b1*x1 + ... + bn*xn)
For additional details,see also Nonlinear Estimation or the Generalized Linear Models chapter;see also Probit Transformation and Regression and Multinomial logit and probit regression for similar transformations.