A manager at the Camden Walmart is interested in learning more about the relationship between the number of customers in a checkout line and the total time it takes to check out. She selects a random sample of 11 customers and measured the number of customers who were in front of the selected customer in line and the time until that customer had finished checking out. An analysis of the data is provided below.  Identify and interpret the y-intercept of the least squares regression line in context.    Identify the coefficient of determination, r2. Interpret it in context.   One of the data points appears to be an outlier. Describe this point and explain why it is considered an outlier.

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A manager at the Camden Walmart is interested in learning more about the relationship between the number of customers in a checkout line and the total time it takes to check out. She selects a random sample of 11 customers and measured the number of customers who were in front of the selected customer in line and the time until that customer had finished checking out. An analysis of the data is provided below. 

  1. Identify and interpret the y-intercept of the least squares regression line in context. 

 

  1. Identify the coefficient of determination, r2. Interpret it in context.

 

  1. One of the data points appears to be an outlier. Describe this point and explain why it is considered an outlier. 
1,250
1,000+
750-
500+
250-
1
2
3 4
5
7
Customers in Line
Predictor
Coef
SE Coef
T
Constant
72.95
110.36
0.66
0.525
Customers in line
174.40
35.06
4.97
0.001
S = 200.01
R-Sq = 73.33%
R-Sq (adj) = 70.37%
Time (seconds)
Transcribed Image Text:1,250 1,000+ 750- 500+ 250- 1 2 3 4 5 7 Customers in Line Predictor Coef SE Coef T Constant 72.95 110.36 0.66 0.525 Customers in line 174.40 35.06 4.97 0.001 S = 200.01 R-Sq = 73.33% R-Sq (adj) = 70.37% Time (seconds)
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