# Normality requirements for GLM and GEE models

From what I was reading for several hours now about these models, requirements and normality, I understand the following, albeit there are some contradictory statements, so I am confused.

May I ask you to either confirm or correct the following statements, posisbly with some citable reference(s)?

(1) For generalized (not general) linear models,

• normality is not required for the "input" variables (independent/predictor variables and dependent/response variable) of the model, but
• normality is required for the residuals. This is independent from the specified distribution e.g. glm(y ~ x1 + x2 + x3, family = gaussian ...), which refers to the response variable, not the residuals.

(2) For generalized estimating equations (GEE), normality is not required,

• neither for independent/predictor and dependent/response variable,

• nor for residuals of the model.

(I am using R and glm() in {stats}, geeglm() in {geepack})

• Thanks! With residuals I refer to the values I get from stats::glm()$residuals and geepack::geeglm()$residuals. How else would one call them correctly? boot::glm.diag.plots() offers e.g. QQ plots to assess normality of ordered deviance residuals, implying that they should be normal? Mar 9 at 23:05