is the degrees of freedom associated with each parameter estimate. There is one degree of freedom unless the model is not full rank. In this case, any parameter that is confounded with previous ...

You construct a generalized linear model by deciding on response and explanatory variables for your data and choosing an appropriate link function and response probability distribution. Some examples ...

Many response variables are handled poorly by regression models when the errors are assumed to be normally distributed. For example, modeling the state damaged/not damaged of cells after treated with ...