| Popis: |
This chapter deals with methods to estimate parameters of linear parametric models using ordinary least squares (OLS). The univariate case is first reviewed along with equations for the uncertainty in the model estimates as well as in the model predictions. Several goodness-of-fit indices to evaluate the model fit are also discussed, and the assumptions inherent in OLS are highlighted. Next, multiple linear models are treated, and several notions specific to correlated regressors are presented. The insights which residual analysis provides are discussed, and different types of remedial actions to improper model residuals are addressed. Other types of linear models such as splines and models with indicator variables are discussed. Finally, a real-world case study analysis which was meant to verify whether actual field tests supported the claim that a refrigerant additive improved chiller thermal performance is discussed. |
