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Problem Situation #1: STARBUCKS GROWTH: Year # of stores 1971 1 1987 17 1990 84 1994 425 1997 1,412 2000 3,501 2005 10,241 2010 16,858 2014 21,366 HINT: Before using technology to find the regression equations, rewrite the "year" column to be "years since 1971". 1) Create a linear model. a. b. What is the equation? What is the R or R2 value? What is the rate of change (include C. units)? [hint: this is the slope!] 2) Create a quadratic model. What is the equation? What is the R or R2 value? a. b. 4) 3) Using the best fit model, predict the number of Starbucks stores in 2020. What is the current number of Starbucks stores (as reported online)? Do you think the prediction for 2020 is approximately correct? Why or why not?

Problem Situation #1: STARBUCKS GROWTH: Year # of stores 1971 1 1987 17 1990 84 1994 425 1997 1,412 2000 3,501 2005 10,241 2010 16,858 2014 21,366 HINT: Before using technology to find the regression equations, rewrite the "year" column to be "years since 1971". 1) Create a linear model. a. b. What is the equation? What is the R or R2 value? What is the rate of change (include C. units)? [hint: this is the slope!] 2) Create a quadratic model. What is the equation? What is the R or R2 value? a. b. 4) 3) Using the best fit model, predict the number of Starbucks stores in 2020. What is the current number of Starbucks stores (as reported online)? Do you think the prediction for 2020 is approximately correct? Why or why not?

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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**More Linear and Quadratic Regression Practice** **Problem Situation #1:** **STARBUCKS GROWTH:** | Year | # of Stores | |------|-------------| | 1971 | 1 | | 1987 | 17 | | 1990 | 84 | | 1994 | 425 | | 1997 | 1,412 | | 2000 | 3,501 | | 2005 | 10,241 | | 2010 | 16,858 | | 2014 | 21,366 | *HINT:* Before using technology to find the regression equations, rewrite the "year" column to be "years since 1971." 1) Create a linear model. - a. What is the equation? - b. What is the R or R2 value? - c. What is the rate of change (include units)? [hint: this is the slope!] 2) Create a quadratic model. - a. What is the equation? - b. What is the R or R2 value? 3) Using the best fit model, predict the number of Starbucks stores in 2020. 4) What is the current number of Starbucks stores (as reported online)? Do you think the prediction for 2020 is approximately correct? Why or why not? --- **Problem Situation #2:** **CORVETTE PRICES:** | Year | Cost of New Car in $ | |------|----------------------| | 1953 | $3,498 | | 1960 | $3,872 | | 1965 | $4,321 | | 1969 | $4,781 | | 1982 | $18,290 | | 1988 | $28,289 | | 1995 | $36,785 | | 2000 | $39,475 | | 2005 | $43,710 | | 2010 | $48,465 | *HINT:* Before using technology to find the regression equations, rewrite the "year" column to be "years since 1953." 1) Create a linear model. - a. What is the equation? - b. What is the R or R2 value? - c. What is the rate of
Transcribed Image Text:**More Linear and Quadratic Regression Practice** **Problem Situation #1:** **STARBUCKS GROWTH:** | Year | # of Stores | |------|-------------| | 1971 | 1 | | 1987 | 17 | | 1990 | 84 | | 1994 | 425 | | 1997 | 1,412 | | 2000 | 3,501 | | 2005 | 10,241 | | 2010 | 16,858 | | 2014 | 21,366 | *HINT:* Before using technology to find the regression equations, rewrite the "year" column to be "years since 1971." 1) Create a linear model. - a. What is the equation? - b. What is the R or R2 value? - c. What is the rate of change (include units)? [hint: this is the slope!] 2) Create a quadratic model. - a. What is the equation? - b. What is the R or R2 value? 3) Using the best fit model, predict the number of Starbucks stores in 2020. 4) What is the current number of Starbucks stores (as reported online)? Do you think the prediction for 2020 is approximately correct? Why or why not? --- **Problem Situation #2:** **CORVETTE PRICES:** | Year | Cost of New Car in $ | |------|----------------------| | 1953 | $3,498 | | 1960 | $3,872 | | 1965 | $4,321 | | 1969 | $4,781 | | 1982 | $18,290 | | 1988 | $28,289 | | 1995 | $36,785 | | 2000 | $39,475 | | 2005 | $43,710 | | 2010 | $48,465 | *HINT:* Before using technology to find the regression equations, rewrite the "year" column to be "years since 1953." 1) Create a linear model. - a. What is the equation? - b. What is the R or R2 value? - c. What is the rate of
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