Step 1 :The question is asking us to determine which regression equation is best for predicting city fuel consumption. To determine this, we need to consider the P-value, R-squared value, and adjusted R-squared value. The P-value tells us the significance of the predictor variables, the R-squared value tells us the proportion of the variance in the dependent variable that is predictable from the independent variable(s), and the adjusted R-squared value takes into account the number of predictors in the model. The best model would have a low P-value and high R-squared and adjusted R-squared values.
Step 2 :Looking at the table, we can see that all models have a P-value of 0.000, which means all predictor variables are significant. The model with the highest R-squared value is the one with WT/DISP/HWY as predictor variables, and the model with the highest adjusted R-squared value is the one with WT/HWY as predictor variables.
Step 3 :Therefore, we need to choose between these two models. Since the adjusted R-squared value takes into account the number of predictors in the model, it is a better measure for model comparison. Therefore, the model with WT/HWY as predictor variables is the best for predicting city fuel consumption.
Step 4 :Final Answer: \(\boxed{\text{D. The equation CITY } =6.71-0.00159 \text{ WT } +0.674 \text{ HWY is best because it has a low P-value and the highest adjusted value of } R^{2}}\).