By transforming the variables, we can estimate a linear multiple regression model and use the results as a nonlinear model. Inference procedures for transformed quadratic mod- els are the same as those that we have previously developed for linear models. In this way we avoid confusion that would result if different statistical procedures were used for linear versus quadratic models. The coefficients must be combined for interpretation. Thus, if we have a quadratic model, then the effect of a variable, X, is indicated by the coefficients of both the linear and the quadratic terms. We can also perform a simple hypothesis best to de termine if a quadratic model is an improvement over a linear model The Zor X variable is merely an additional variable whose coefficient can be tested-HB-0-using the conditional Student's for statistic. If a quadratic model fits the data better than a linear model, then the coefficient of the quadratic variable-2-x-will be significantly differ- ent from 0. The same approach applies if we have variables such as Z - XiaZ - x x 12.7 Trademar Na Models u18 Example 12.11 Production Costs (Quadratic Model Estimation) Arnold Sorenson, production manager of New Frontiers Instruments, Inc. was inter- ested in estimating the mathematical relationship between the number of electronic assemblies produced during an 8-hour shift and the average cost per assembly. This function would then be used to estimate cost for various production order bids and to determine the production level that would minimize average cost. Data are found in the data file Production Cost