(5 points) Using the multiple linear regression model you developed in part (a-iii), what is the predicted price for an automobile age = 29, kilometers = 43905, hp = 110, and weight = 1170. c. (5 points) Using the multiple linear regression model you developed in part (a-iii), what is the predicted price for an automobile age = 29, kilometers = 43905. d. (5 points) Compare and comment on the price predictions in b and c. Which price would be acceptable to a customer? Why? e. (10 points) Use JMP to partition the Data (set seed = 1234): 80% for training and 20% for validation. Find a regression model for price using JMP with all predictors. What are the values for R-Sq and MSE for training and validation data? What accuracy might one expect if this model is deployed in practice? Which variables seem to matter most?

A E F Id HP 1 90 2 90 1 2. 3 4 5 6 3 90 Weight 1165 1165 1165 1165 1170 1170 4 90 5 90 6 90 7 8 7 90 8 9 10 11 12 9 10 11 12 13 14 15 16 17 18 19 1245 1245 1185 1105 1185 1185 1185 1185 1185 1185 1185 1105 90 192 69 192 192 192 192 192 192 192 110 13 14 15 16 17 18 20 19 110 1065 1105 20 110 110 21 1105 21 22 23 24 25 B D Price Age Kilometers 13500 23 46986 13750 23 72937 13950 24 41711 14950 26 48000 13750 30 38500 12950 32 61000 16900 27 94612 18600 30 75889 21500 27 19700 12950 23 71138 20950 25 31461 19950 22 43610 19600 25 32189 21500 31 23000 22500 32 34131 22000 28 18739 22750 30 34000 17950 24 21716 16750 24 25563 16950 30 64359 15950 30 67660 16950 29 43905 15950 28 56349 16950 28 32220 16250 29 25813 15950 25 28450 17495 27 34545 15750 29 41415 16950 28 44142 17950 30 11090 12950 29 9750 15750 22 35199 15950 27 29510 14950 26 32692 15500 22 41000 15750 26 43000 15950 25 25000 14950 23 10000 15750 32 25329 14750 27 27500 13950 22 49059 16750 27 44068 13950 22 46961 Data Dictionary Data 22 110 1170 23 110 1120 1120 24 110 26 25 110 1120 26 1120 110 110 27 27 28 29 30 31 1120 1120 28 110 29 110 110 1120 1120 30 32 31 97 1100 32 97 1100 33 34 33 97 1100 35 34 97 1100 1100 97 35 36 97 1100 1100 37 97 38 97 36 37 38 39 40 41 42 43 44 1100 1100 39 97 40 97 1100 1100 41 97 42 97 1100 43 97 1100