Ever wonder what sorts of errors freshman math students make? There are a couple of pretty basic ones.

The linear response function, which could also be specified as RESPONSE MARGINALS, yields one probability, Pr (brand preference=M), as the response function to be analyzed.

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 ...

It only makes sense. I did linear regression in google docs and I did it for python. But what if you neither of those? Can you do it by hand? Why yes. Suppose I take the same data from the pylab ...

We suggest reducing the design matrix to row echelon form as a means of finding the structure of all estimable functions. We illustrate the procedure with two examples.