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(d) Would it be advisable to use this regression model to predict coffee shop sales for a university town with 35,000 students? Explain your answer.

(d) Would it be advisable to use this regression model to predict coffee shop sales for a university town with 35,000 students? Explain your answer.

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter10: Statistics
Section10.6: Summarizing Categorical Data
Problem 10CYU
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Part  C D E 

6. In a university town, the sales (in thousands of pounds) of coffee shops were thought to be related to the student population (in thousands) in their neighbourhoods. The figures for a random sample of 10 coffee shops are recorded, and are shown in the following table: 6 8 | 8 58 105 88 118 117 137 157 169 149 202 20 22 Student population (in 000s) 2 Sales (in £000s) 12 16 20 26 Computer output from the regression of sales on student population is: Predictor Constant Student population Coefficient Std error t-ratio 9.2260 0.5803 60.00 5.00 p-value 0.0002 6.5033 8.6167 2.55 x 10-5 S = 13.8293 R-sq = 90.27% R = 0.9501 (a) Interpret R= 0.9501 from the above computer output. (b) Write down the equation of the estimated regression line. (c) What conclusion can you draw about the connection between the two variables? (d) Would it be advisable to use this regression model to predict coffee shop sales for a university town with 35,000 students? Explain your answer. (e) Suggest an alternative independent variable to explain coffee shop sales, briefly justifying your choice.
Transcribed Image Text:6. In a university town, the sales (in thousands of pounds) of coffee shops were thought to be related to the student population (in thousands) in their neighbourhoods. The figures for a random sample of 10 coffee shops are recorded, and are shown in the following table: 6 8 | 8 58 105 88 118 117 137 157 169 149 202 20 22 Student population (in 000s) 2 Sales (in £000s) 12 16 20 26 Computer output from the regression of sales on student population is: Predictor Constant Student population Coefficient Std error t-ratio 9.2260 0.5803 60.00 5.00 p-value 0.0002 6.5033 8.6167 2.55 x 10-5 S = 13.8293 R-sq = 90.27% R = 0.9501 (a) Interpret R= 0.9501 from the above computer output. (b) Write down the equation of the estimated regression line. (c) What conclusion can you draw about the connection between the two variables? (d) Would it be advisable to use this regression model to predict coffee shop sales for a university town with 35,000 students? Explain your answer. (e) Suggest an alternative independent variable to explain coffee shop sales, briefly justifying your choice.
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