CHAPTER 3 Normal Linear Models: Statistical Inference
Chapter 2 introduced least squares fitting of ordinary linear models. For n independent observations 
, with 
for μi = E( yi) and a model matrix 
and parameter vector 
, this model states that
We now add to this model the assumption that {yi} have normal distributions. The model is then the normal linear model. This chapter presents the foundations of statistical inference about the parameters of the normal linear model.
We begin this chapter by reviewing relevant distribution theory for normal linear models. Quadratic forms incorporating normally distributed response variables and projection matrices generate chi-squared distributions. One such result, Cochran's theorem, is the basis of significance tests about 
in the normal linear model. Section 3.2 shows how the tests use the chi-squared quadratic forms to construct test statistics ...
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