x11 = Aging temperature (degrees, Celsius) a. The engineers used stepwise regression in order to search for a parsimonious set of predictor variables. Do you agree with this decision? Explain. b. The stepwise regression selected the following independent variables: x1 = Carbon x2 = Manganese C=hromium, x5: Molybdenum, x6: Copper, x3: Vanadium x9: Plate thickness, x10: Solution treating, and X11 = Aging temperature. Based on this information, determine the total number of rst-order models that were t in the stepwise routine. 0. Refer to part b. All these variables were statistically signicant in the stepwise model, with R2 = .94. Consequently, the engineers used the estimated stepwise model to predict yield strength. Do you agree with this decision? Explain. 6.6. Yield strength of steel alloy. Industrial engineers at the University of Florida used regression modeling as a tool to reduce the time and cost associated with developing new metallic alloys (Modelling and Simulation in Materials Science and Engineering, Vol. 13. 2005). To illustrate, the engineers built a regression model for the tensile yield strength (y) of a new steel alloy. The potential important predictors of yield strength are listed below. X1 = Carbon amount (% weight) x2 = Manganese amount (% weight) x3 = Chromium amount (% weight) x4 = Nickel amount (% weight) x5 = Molybdenum amount (% weight) x5 = Copper amount (% weight) x7 = Nitrogen amount (% weight) X8 = Vanadium amount (% weight) x9 = Plate thickness (millimeters) x10 = Solution treating (milliliters)