General Aspects of Fitting Regression Models
The ordinary multiple linear regression model is frequently used and has parameters that are easily interpreted. In this chapter we study a general class of regression models, those stated in terms of a weighted sum of a set of independent or predictor variables. It is shown that after linearizing the model with respect to the predictor variables, the parameters in such regression models are also readily interpreted. Also, all the designs used in ordinary linear regression can be used in this general setting. These designs include analysis of variance (ANOVA) setups, interaction effects, and nonlinear effects. Besides describing and interpreting general regression models, this chapter also describes, in general terms, how the three types of assumptions of regression models can be examined.
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