In the previous two chapters we dealt with models having linear structures in which the factors affecting the response variable take on a fixed number of levels. This is indeed the case in many designed experiments. In other designed experiments, and in many other situations, we encounter response variables that depend linearly on one or more continuous variables. Both of these are particular cases of what is known as linear models. The purpose of this chapter is to develop procedures for making inferences about general linear models when the response variable is normally distributed.
KeywordsMultiple Linear Regression Model Unbiased Estimator Scatter Diagram Simple Regression Model Full Regression
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