title: What is the assumption for General Linear Regression?
body: <p>Take Logistic Regression and Beta Regression as an example. Both are using logit(u/1-u) as the transformed response when fitting a linear regression. Logistic has 0-1 as original response, while beta has p which is between (0,1) as original response.
  As we all know, linear regression requires residual to follow normality, homoscedasticity and No autocorrelation. How about GLM? I know that the original response doesn't have to follow these assumptions, but what about logit(u/1-u)? Does it need to fit the above assumption? Can we simply think <code>transformed_response = X*B</code> as a linear regression with response <code>transformed_response</code> that follow some normal distribution (mean,variance)?</p>