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one of the things that generalized linear models actually generalize is the relations between the predictor variables or variable to the mean of a distribution so in regular normal linear regression we have that mu I is equal to some beta 0 plus beta 1 X I and this isnt the case that we have only one predictor variable and in generalized linear models we have that some function of mu is equal to this linear predictor and the question is why do we even need to have this function why cant we just use the identity function which we have here which basically means that G mu is mu of I why do we need something other than the identity function and I think the main reason is to preserve the linearity structure so this thing over here is a linear structure its a line its an I per plane etc and if our data really comes lets say like this then maybe we dont need any transformation maybe a straight line works you know if this is new and or Y also and this is X then this line is new which i
