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10090ELine 1 (20 85)-Line 2-(35:80)80Line 3 (50 75)70Line-4-(60-60):60Line 5-(70-50)5040Line 6 (90 32).3020101090 10060% of waiting customers804030-1002070-10The percent of waiting customers is the -coordinate, while the percent of return customers is the coordinate. So, the50TO% of return-austomers- The percent of waiting customers is the -coordinate, while the percent of return customers is the ycoordinate. So, thetable of values corresponds to the points(20,85), (35,80), (50,75), (60, 60), (70, 50), (90,32)FEEDBACKContent attributionUnderstand the relationship between scatter plots and tables and determine patternsQuestionreturn customers if 80% of customers wait more thanUsing the linear relationship graphed above, estimate the percent of10 minutes in lineProvide your answer below

Question

Using the linear relationship graphed below; estimate the percent of return customers if 80% of customers wait more than 10 minutes in line.

Provide your answer below: ___%

Question for answering above: A grocery store manager explored the relationship between the percent of customers that wait more than 10 minutes in line and the percent of return customers at the store. The manager collects information from 6 checkout lines, shown in the table below.

Use the graph below to plot the points and develop a linear relationship between the percent of waiting customers and the percent of return customers.

Line

% of Waiting Customers

% of Return Customers

1

20

85

2

35

80

3

50

75

4

60

60

5

70

50

6

90

32

 

 
100
90
ELine 1 (20 85)
-Line 2-(35:80)
80
Line 3 (50 75)
70
Line-4-(60-60):
60
Line 5-(70-50)
50
40
Line 6 (90 32).
30
20
10
10
90 100
60
% of waiting customers
80
40
30
-10
0
20
70
-10
The percent of waiting customers is the -coordinate, while the percent of return customers is the coordinate. So, the
50
TO
% of return-austomers-
help_outline

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100 90 ELine 1 (20 85) -Line 2-(35:80) 80 Line 3 (50 75) 70 Line-4-(60-60): 60 Line 5-(70-50) 50 40 Line 6 (90 32). 30 20 10 10 90 100 60 % of waiting customers 80 40 30 -10 0 20 70 -10 The percent of waiting customers is the -coordinate, while the percent of return customers is the coordinate. So, the 50 TO % of return-austomers-

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The percent of waiting customers is the -coordinate, while the percent of return customers is the ycoordinate. So, the
table of values corresponds to the points
(20,85), (35,80), (50,75), (60, 60), (70, 50), (90,32)
FEEDBACK
Content attribution
Understand the relationship between scatter plots and tables and determine patterns
Question
return customers if 80% of customers wait more than
Using the linear relationship graphed above, estimate the percent of
10 minutes in line
Provide your answer below
help_outline

Image Transcriptionclose

The percent of waiting customers is the -coordinate, while the percent of return customers is the ycoordinate. So, the table of values corresponds to the points (20,85), (35,80), (50,75), (60, 60), (70, 50), (90,32) FEEDBACK Content attribution Understand the relationship between scatter plots and tables and determine patterns Question return customers if 80% of customers wait more than Using the linear relationship graphed above, estimate the percent of 10 minutes in line Provide your answer below

fullscreen
check_circleAnswer
Step 1

The percent of return customers if 80% of customers wait more than 10 minutes in line is calculated below:

 From the given information, the percent of waiting customers is the x-coordinate; the percent of return customers is the y-coordinate.

The regression analysis is conducted here by using MINITAB. The software procedure is given below:

  • Choose Stat > Regression > Regression.
  • In Response, enter the column containing the response as % of return customers.
  • In Predictors, enter the columns containing the predictor as % of waiting customers.
  • Click OK.

The output using MINITAB is as follows:

Regression Analysis: % of return cust versus % of waiting cus
The regression equation is
of return customers (y) 106 0.789 of waiting customers (x)
Predictor
Coef SE Coef
T
106.393
Constant
5.557 19.15 0.000
of waiting customers (x) -0.78879 0.09455 -8.34
0.001
R-Sq 94.6
R-sq (adj)93.2
s5.28222
Analysis of Variance
Source
DF
ss
MS
1 1941.7 1941.7
Regression
69.59 0.001
Residual Error
4
111.6
27.9
Total
5 2053.3
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Regression Analysis: % of return cust versus % of waiting cus The regression equation is of return customers (y) 106 0.789 of waiting customers (x) Predictor Coef SE Coef T 106.393 Constant 5.557 19.15 0.000 of waiting customers (x) -0.78879 0.09455 -8.34 0.001 R-Sq 94.6 R-sq (adj)93.2 s5.28222 Analysis of Variance Source DF ss MS 1 1941.7 1941.7 Regression 69.59 0.001 Residual Error 4 111.6 27.9 Total 5 2053.3

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Step 2

From the output, the least squares regression equation is y = 106.393 – 0.78879 (% of waiting customers).

The percent of return customers if 80...

y106.393-0.78879(% if waiting customers)
106.393-0.78879(80)
=106.393 -63.1032
43.29%
22
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y106.393-0.78879(% if waiting customers) 106.393-0.78879(80) =106.393 -63.1032 43.29% 22

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