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
23	18
57	25
83	28
54	40
98	45
37	33
13	25
0	12
131	53
55	21
0	13
92	37
12	15
4	12
28	17

Arrival Time (minutes before 6:00 p.m.)	Shopping Time (minutes)
38	36
15	14
76	35
32	18
23	18
18	23
24	30
38	23
57	27
113	55
30	21
109	42
49	27
102	42
72	26
113	22

(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.)

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(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).)

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