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Question 17 (1 point) Suppose that a researcher studying the weight of female college athletes wants to predict the weights based on height, measured in inches, the percentage of body fat of an athlete, and age. The researcher calculates the regression equation as (weight) = 3.805*(height) + 1.483*(percent body fat) - 0.814*(age) - 80.775. If a female athlete is 68 inches tall, has a 24 percentage of body fat, is 21 years old, and has a weight of 205.791, the residual is 9.328. Choose the correct interpretation of the residual. O 1i The height of the athlete is 9.328 inches less than what we would expect. 0 2i The weight of the athlete is 9.328 pounds larger than what we would expect. 0 3i The height of the athlete is 9.328 inches larger than what we would expect. 0 4i The weight of the athlete is 9.328 pounds less than what we would expect. 0 5) The weight of the athlete is 205.791 pounds larger than what we would expect. Question 16 (1 point) A trucking company considered a multiple regression model for relating the dependent variable of total daily travel time for one of its drivers (hours) to the predictors distance traveled (miles) and the number of deliveries of made. After taking a random sample, a multiple regression was performed and the equation is (time) = 0.086*(distance) + 1.1*(deliveries) - 1.423. Suppose for a given driver's day, he is scheduled to drive 113 miles and make 6 stops. Suppose it took him 15.464 hours to complete the trip. What is the residual based on the regression model? 0 1) -0.854 O 2) 0.569 O 3) We do not know the observations in the data set, so we cannot answer that question. 0 4) -0.569 O 5) -93.105 Question 15 (1 point) Suppose the sales (10005 of $) of a fast food restaurant are a linear function of the number of competing outlets within a 5 mile radius and the population (10005 of people) within a 1 mile radius. The regression equation quantifying this relation is (sales) = 0.537*(competitors) + 6.589*(population) + 6.728. What would you expect the sales (in 10005 of $) to be of a store that has 7 competitors and a population of 15 thousand people within a 1 mile radius? 0 1) 109.322 0 2) We do not know the observations in the data set, so we cannot answer that question. 0 3) 60.906 0 4) 95.866 0 5) 102.594 Predictor Coef Stdev t-ratio p Constant 1 . 354 7. 916 0. 17 0. 867 distance 1. 326 0 . 014 92 . 8 1. 639e-18 deliveries 0. 175 0 . 301 0 . 58 0. 5718 s = 5.924 R-sq = 99. 868 R-sq (adj ) = 99. 848 Analysis of Variance SOURCE DF SS MS F P Regression 2 302409.2 151204.6 4308.93