1. Find the best model for predicting Y (weight) based on X1 (age), X2 (height), and X3 (indicator for male). Consider as predictors all possible linear and quadratic terms. Consider possible transformations of Y. Include all appropriate diagnostics. When you have found your "best" model, predict a new Y when X1 = 26, X2 = 70, and X3 = 1, giving a 95% prediction interval. The data set, shown below, appears in "RegressionFall 17.xlsx". Y X1 X2 X3 240.00 20 71.0 | 1 100.43 20 67.2 0 233.41 20 68.1 1 107.61 20 67.7 0 238.91 20 68.6 1 97.03 21 65.2 0 233.66 21 67.6 0 Y X1 X2 X3 105.92 23 66.2 0 115.98 23 67.3 0 122.05 23 67.6 0 280.65 23 69.0 1 102.74 23 66.0 0 97.46 24 65.4 0 127.92 24 67.8 0 107.79 21 67.4 0 288.27 24 68.4 1 109.71 21 67.5 0 230.00 24 70.8 1 168.00 21 69.4 1 91.06 24 64.7 0 86.90 22 65.1 0 101.19 22 66.2 0 323.27 22 71.6 1 111.44 22 67.4 0 220.00 22 69.2 1 95.76 25 293.46 25 67.9 1 103.51 25 65.2 0 280.00 25 72.0 1 321.51 25 69.0 1 63.8 0