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24 Dec
2019

the Gasoline Consumption data in… | Good Grade Guarantee!

Please solve this using SAS (or R)- Data set (Gasoline Consumption and Automotive Variables)Y X_1 X_2 X_3 X_4 X_5 X_6 X_7 X_8 X_9 X_{10} X_{11}18.9 350.0 165 260 8.00 2.56 4 3 200.3 69.9 3910 117.0 350.0 170 275 8.50 2.56 4 3 199.6 72.9 3860 120.0 250.0 105 185 8.25 2.73 1 3 196.7 72.2 3510 118.3 351.0 143 255 8.00 3.00 2 3 199.9 74.0 3890 120.1 225.0 95 170 8.40 2.76 1 3 194.1 71.8 3365 011.2 440.0 215 330 8.20 2.88 4 3 184.5 69.0 4215 122.1 231.0 110 175 8.00 2.56 2 3 179.3 65.4 3020 121.5 262.0 110 200 8.50 2.56 2 3 179.3 65.4 3180 134.7 89.7 70 81 8.20 3.90 2 4 155.7 64.0 1905 030.4 96.9 75 83 9.00 4.30 2 5 165.2 65.0 2320 016.5 350.0 155 250 8.50 3.08 4 3 195.4 74.4 3885 136.5 85.3 80 83 8.50 3.89 2 4 160.6 62.2 2009 021.5 171.0 109 146 8.20 3.22 2 4 170.4 66.9 2655 019.7 258.0 110 195 8.00 3.08 1 3 171.5 77.0 3375 120.3 140.0 83 109 8.40 3.40 2 4 168.8 69.4 2700 017.8 302.0 129 220 8.00 3.00 2 3 199.9 74.0 3890 114.4 500.0 190 360 8.50 2.73 4 3 224.1 79.8 5290 114.9 440.0 215 330 8.20 2.71 4 3 231.0 79.7 5185 117.8 350.0 155 250 8.50 3.08 4 3 196.7 72.2 3910 116.4 318.0 145 255 8.50 2.45 2 3 197.6 71.0 3660 123.5 231.0 110 175 8.00 2.56 2 3 179.3 65.4 3050 121.5 360.0 180 290 8.40 2.45 2 3 214.2 76.3 4250 131.9 96.9 75 83 9.00 4.30 2 5 165.2 61.8 2275 013.3 460.0 223 366 8.00 3.00 4 3 228.0 79.8 5430 123.9 133.6 96 120 8.40 3.91 2 5 171.5 63.4 2535 019.7 318.0 140 255 8.50 2.71 2 3 215.3 76.3 4370 113.9 351.0 148 243 8.00 3.25 2 3 215.5 78.5 4540 113.3 351.0 148 243 8.00 3.26 2 3 216.1 78.5 4715 113.8 360.0 195 295 8.25 3.15 4 3 209.3 77.4 4215 116.5 350.0 165 255 8.50 2.73 4 3 185.2 69.0 3660 111.6 Refer to the Gasoline Consumption data in Tables 9.18 and 9.19 (a) Would you include all the variables to predict the gasoline consumption of the cars? Explain, giving reasons (b) Six alternative models have been suggested: (a) Regress Y on Xi. (b) Regress Y on X10 (c) Regress Y on Xi and X10 (d) Regress Y on X2 and Xio (e) Regress Y on Xs and X10 (f) Regress Y on Xg and X5, and Xio Among these regression models, which would you choose to predict the gasoline consumption of automobiles? Can you suggest a better model? (c) Plot Y against X1, X2, Xg, and X1o (one at a time). Do the plots suggest that the relationship between Y and the 11 predictor variables may not be linear? (d) The gasoline consumption was determined by driving each car with the same load over the same track (a road length of about 123 miles). Instead of using Y (miles per gallon), it was suggested that we consider a new variable, W 100/Y (gallons per hundred miles). Plot W against X1, X2, Xs, and X10 and examine if the relationship between W and the 11 predictor variables is more linear than that between Y and the 11 predictor variables (e) Repeat Part (b) using W in place of Y. What are your conclusions? (f) Regress Y on X13, where X13 X8/X10 (g) Write a brief report describing your findings. Make a recommendation on the model to be used for predicting gasoline consumption of cars ghn

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