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).

Arrival Time (minutes before 6:00 p.m.)	Shopping Time (minutes)
58	23
21	18
57	25
83	28
54	40
98	45
37	33
11	25
0	12
131	53
55	21
0	13
92	37
12	15
2	12
28	17

Arrival Time (minutes before 6:00 p.m.)	Shopping Time (minutes)
38	36
15	12
76	35
32	16
23	16
18	23
24	30
38	23
57	27
113	55
30	21
107	42
47	27
104	42
72	28
113	22

(b)What does the scatter diagram developed in part (a) indicate about the relationship between the two variables?

The scatter diagram indicates no apparent 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 a nonlinear relationship between arrival time and shopping time.

Does there appear to be an outlier or influential observation? (Enter your answer as an ordered pair in the formx,y. If there is no answer, enter NONE.)

(x,y) =

Explain. (Select all that apply.)

The point is an outlier or influential observation because it has high leverage.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 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 slope of the estimated regression line would change sign.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. (Letx= arrival time (in minutes before 6:00 p.m.),and lety= shopping time (in minutes).Round your numerical values to four decimal places.)=

(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 than2? (Select all that apply.)

(38, 36)(54, 40)(113, 22)(113, 55)

(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. (If there are no influential observations, enter your estimated regression equation from part (c).)=