Retail chain Kroger has more than 2700 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 (Retail Touchpoints). 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).Arrival Time (Minutes Before 6:00 p.m.) Shopping Time (Minutes) 59 24 22 19 58 25 84 29 53 40 97 45 38 32 12 25 0 11 132 54 55 21 0 14 91 36 11 15 3 11 27 16 39 37 15 13 77 35 31 17 24 17 19 24 23 30 39 24 58 26 114 56 30 22 108 41 48 26 103 41 71 27 111 24a. Select the correct scatter diagram for arrival time as the independent variable.Graph A b. What does the scatter diagram developed in part (a) indicate about the relationship between the two variables? There appears to be a |positive relationship between the two variables. Does there appear to be any outliers and/or influential observations? Observation |32 (111, 24) appears to be an observation with high leverage and may be very influential in terms of fitting a linear model to the data. c. Using the entire data set, develop the estimated regression equation that can be used to predict the shopping time given the arrival time (to 4 decimals). y = d. Use residual analysis to determine whether any outliers or influential observations are present. Observation | - Select your answer - has a standardized residual [- Select your answer - v ) , indicating it is an outlier. e. After looking at the scatter diagram in part (a), suppose you were able to visually identify what appears to be an influential observation. Drop this observation from the data set and fit an estimated regression equation to the remaining data (to 4 decimals). y = 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. The slope of the estimated regression equation is now as compared to a value of when this observation is included. Thus, we see that this observation |- Select your answer - | a impact on the value of the slope of the fitted line and hence we - Select your answer - v| that it is an influential observation