stat2160 questions

Question 5 (1 point) Suppose that for a typical FedEx package delivery, the cost of the shipment is a function of the weight of the package. You find out that the regression equation for this relationship is (cost of delivery) = 0.063*(weight) + 6.01. If a package you want to ship weighs 22.342 ounces and the true cost of the shipment is $14,078, the residual is 6.66. Interpret this residual in terms of the problem. 0 ll The cost of delivery is 14.078 dollars larger than what we would expect. 0 2) The weight is 6.66 points less than what we would expect. 0 3) The weight is 6.66 points larger than what we would expect. 0 4) The cost of delivery is 6.66 dollars larger than what we would expect. 0 5) The cost of delivery is 6.66 dollars less than what we would expect. Question 6 (1 point) You work for a company in the marketing department. Your manager has tasked you with forecasting sales by month for the next year. You notice that over the past 12 months sales have consistently gone up in a linear fashion, so you decide to run a regression the company's sales history. If 10 months are sampled and the regression output is given below, what can we conclude about the slope of time? Predictor Coet Stdev P Constant 536.492 16.342 (0.0001 time 11.53! 2.7634 0.003 s = 26.622 R-sq = 68.?11: Rsqtadj) = 641.8% Analysis of Variance SOURCE SS HS Regression 12452.9 12452.9 Error 5669.9 708.7 Total 18122.9 1) Since we are not given the dataset, we do not have enough information to determine if the slope differs from 0. 2) Not enough evidence was found to conclude the slope differs significantly from 0. O 3i The slope is equal to 0. O 4) The slope is 11.584 and therefore differs from 0. O 5) The slope significantly differs from 0. Question 7 [1 point) Suppose that a researcher wants to predict the weight of female college athletes based on their heightI percent body fat, and age. A sample is taken and the following regression table is produced. Based on the F-test alone, what is the correct conclusion about the regression slopes? Predi otor Coef Stdev t ratio D Constant 35.95 147.794 0.24 0.8123 116191113 3.635 1.344 2.71 0.02046 percent body fat 1.508 2.747 0.55 0.594 age 1.538 3.602 8.44 0.6678 s = 27.192 Rsq = 41.14% Rsqtadj} = 25.08% Analysis of Varianoe SOURCE. D}? 55 HE E D Regression 3 5683.91 1894.64 2.56 0.108 Error 11 8133.3 739.39 Total 14 13817 .21 O 1) We did not find significant evidence to conclude that at least one slope differs from zero. 0 2) At least one of the regression slopes does not equal zero. 0 3) All the regression slopes are equal to zero. 0 4) We do not have the dataset, therefore, we are unable to make a conclusion about the slopes. O 5) All the regression slopes do not equal zero. Question 8 [1 point) Suppose that a researcher wants to predict the weight of female college athletes based on their height and percent body fat. If a sample is taken and the following regression table is produced, interpret the slope of the height variable. Predi otor Coef Stdev t ratio D Constant 5.98 11.325 -0.53 0.6071 height 3.534 0.158 22.39 3.728e11 percent body fat 1.072 0.207 5.18 0.0002293 3 = 2212 Rsq = 97.58% Rsqtadj} = 97.64% Analysis of Varianoe SOURCE D}? 55 H5 1? D Regression 2 2848.62 1424.3]. 291.2 (0.0001 Error 12 58.69 4.89 Total 14 2907. 31 O 1) When height increases by 1 inch, weight decreases by 3.534 pounds, holding all other variables constant. 0 2) When height increases by 1 inch, weight increases by 3.534 poundsI holding all other variables constant. 0 3) When height decreases by 1 inch, weight increases by 3.534 pounds, holding all other variables constant. 0 4) We do not have enough information to say. 0 5) When height increases by 3.534 inches, weight increases by 1 pound, holding all other variables constant