Use the time/tip data from the table below, which includes data from New York City taxi rides. (The distances are in miles, the times are in minutes, the fares are in dollars, and the tips are in dollars.) Find the regression equation, letting time be the predictor (x) variable. Find the best predicted tip for a ride that takes 30 minutes. How does the result compare to the actual tip amount of $4.70? Distance 12.71 8.51 Time Fare Tip 0.49 0.68 1.65 1.40 1.80 1.02 D 6.00 11.00 18.00 25.00 8.00 6.30 9.80 12.30 16.30 7.80 2.46 1.50 2.34 OO 27.00 31.00 2.00 36.80 31.75 4.80 0.00 2.98 0.00 1.89 1.96 The regression equation is = 1.33+ (0.0192)x. (Round the y-intercept to two decimal places as needed. Round the slope to four decimal places as needed.) The best predicted tip for a ride that takes 30 minutes is $1.64. (Round to the nearest cent as needed.) How does the result compare to the actual tip amount of $4.70? OA. The best predicted value is close to the actual tip of $4.70. B. The best predicted value is very different from the actual tip of $4.70. C. The best predicted value is exactly the same as the actual tip of $4.70. OD. The result does not make sense given the context of the data.