A statistical program is recommended. A large supermarket chain has invested heavily in data, technology, and analytics. Feeding predictive models with data from an infrared sensor system to anticipate when shoppers will reach the checkout counters, they are able to alert workers to open more checkout lines as needed. This has allowed them to lower their average checkout time from four minutes to less than 30 seconds. Consider the data in the following table containing 32 observations. Suppose each observation gives the arrival time (measured in minutes before 6 p.m.) and the shopping time (measured in minutes). (minutes before Shopping Time Arrival Time 6:00 p.m.) 58 (minutes) 23 Arrival Time (minutes before Shopping Time (minutes) 6:00 p.m.) 38 36 23 18 15 25 78 35 83 28 32 16 52 40 23 18 96 45 18 23 31 24 30 13 25 38 D 12 59 27 133 53 113 55 55 21 30 23 0 13 109 42 92 37 49 27 26 15 102 42 12 72 28 17 114 21 (a) Develop a scatter diagram for arrival time as the independent variable. 120 0 shopping Time (Minutes) 30 140 B 120- 60 20 60 80 100 120 140 Arrival Time (Minutes Before 6:00p.m.) 0 20 60 Shopping Time (Minutes) (b) What does the scatter diagram developed in part (a) indicate about the relationship between the two variables? The scatter diagram Indicates a nonlinear relationship between arrival time and shopping time. The scatter diagram indicates a negative linear relationship between arrival time and shopping time. The scatter diagram indicates a positive linear relationship between arrival time and shopping time. The scatter diagram indicates no apparent relationship between arrival time and shopping time. 30 60 100 120 140 Arrival Time (Minutes Before no pan.)