Web logs

Logistic regression   does not make many of the key assumptions of  linear regression  and  general linear models  that are based on ordinary least squares algorithms – particularly regarding linearity, normality, homoscedasticity, and measurement level. Besides that, one of the assumptions of regression is that the variance of Y is constant across values of X (homoscedasticity), which cannot be the case with a binary variable, because its variance is p(1-p). Suppose 50 percent of the people are 1s, then the variance of .25 would be its maximum value. As  we move to more extreme values, the variance decreases, for example when p =.10, the variance is .1*.9 = .09, so as p approaches 1 or 0, the variance approaches 0.   Firstly, it does not need a linear relationship between the dependent and independent variables.  Logistic regression can handle all sorts of rela...