Our objective here is to learn how to use multiple regression for creating and analyzing mod- els. Thus, we learn how multiple regression works and some guidelines for interpretation. A good understanding provides the capability for solving a wide range of applied problems. This study of multiple regression methods parallels the study of simple regression. The first step in model development is model specification, which includes the selection of model variables and the model form. Next, we study the least squares process, followed by an anal- ysis of variability to identify the effects of each predictor variable. Then we study estimation, confidence intervals, and hypothesis testing. Computer applications are used extensively to indicate how the theory is applied to realistic problems. Your study of this material will be aided if you relate the ideas in this chapter to those presented in Chapter 11. Model Specification X20 We begin with an application that illustrates the important task of regression model speci- fication. Model specification includes selection of the exogenous variables and the func- tional form of the model. Example 12.1 Process Manufacturing (Regression Model Specification) The production manager for Flexible Circuits, Inc., has asked for your assistance in studying a manufacturing process. Flexible circuits are produced from a continuous roll of flexible resin material with a thin film of copper-conducting material bonded to its surface. Copper is bonded to the resin by passing the resin through a copper-based solution. The thickness of the copper is critical for high-quality circuits. Copper thick- ness depends, in part, on the temperature of the copper solution, speed of the produc- tion line, density of the solution, and thickness of the flexible resin material. To control the thickness of the bonded copper, the production manager needs to know the effect of each of these variables. You have been asked for assistance in developing a multiple regression model