140 utes Before 6:00 p.m.) A statistical program is recommended. Retail chain Kroger has more than 2,700 locations and is the largest supermarket in the United States based on revenue. Kroger has invested heavily in data, technology, and analytics. Feeding predictive models with data from an infrared sensor system called QueVision to anticipate when shoppers will reach the checkout counters, Kroger is able to alert workers to open more checkout lines as needed. This has allowed Kroger to lower its average checkout time from four minutes to less than 30 seconds. Consider the data in the file Checkout. The file contains 32 observations. Each observation gives the arrival time (measured in minutes before 6 p.m.) and the shopping time (measured in minutes). (a) Develop a scatter diagram for arrival time as the independent variable. 60 50 40 30 20 10 Shopping Time (Minutes) 0 0 20 40 60 80 100 120 140 . . Arrival Time (Minutes Before 6:00 p.m.) 120 : 100 80 RO ng Time (Minutes) 140 120 100 Arrival Time (Minutes Before 6:00 p.m.) 20 30 888 50 40 60 . . 80 60 40 20 10 20 30 40 50 60 Shopping Time (Minutes)) ... Does there appear to be an outlier or influential observation? (Enter your answer as an ordered pair in the form x, y. If there is no answer, enter NONE.) (x, y)=(111,24 Explain. (Select all that apply.) The point is an outlier or influential observation because if it were dropped from the data set, the slope of the estimated regression line would change sign. The point is an outlier or influential observation because it does not fit the trend shown by the remaining data. The point is an outlier or influential observation because if it were dropped from the data set, the intercept of the estimated regression line would change sign. The point is an outlier or influential observation because it has high leverage. There are no outliers or influential observations. (c) Using the entire data set, develop the estimated regression equation that can be used to predict the shopping time given the arrival time. (Let x arrival time (in minutes before 6:00 p.m.), and let y shopping time (in minutes). Round your numerical values to four decimal places.) 9.2543(x)+14.2765 (d) Use residual analysis to determine whether any outliers or influential observations are present. Which of the following points have a standardized residual greater than 2 or less than -2? (Select all that apply.) (39, 37) (114, 56) (53, 40) (111, 24) (c) Using the entire data set, develop the estimated regression equation that can be used to predict the shopping time given the arrival time. (Let x-arrival time (in minutes before 6:00 p.m.), and let y shopping time (in minutes). Round your numerical values to four decimal places.) 9.2543(x)+14.2765 (d) Use residual analysis to determine whether any outliers or influential observations are present. Which of the following points have a standardized residual greater than 2 or less than -2? (Select all that apply.) (39, 37) (114, 56) (53,40) (111, 24) (e) Drop this outlier from the data set and fit an estimated regression equation to the remaining data. 9= Compare the estimated slope for the new estimated regression equation to the estimated slope obtained in part (c). Does this approach confirm the conclusion you reached in part (d)? Explain. O Yes, because the value of the slope of the fitted line changed after removing the influential observation. No, because the value of the slope of the fitted line did not change after removing the influential observation There are no outliers or influential observations