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A local coffee shop models the number of customers in its shop according to the time of day (t= 0 corresponds to the opening time of 5:00 a.m.) by counting customers at certain times and taking the average number The coffee shop uses the polynomial regression M) = -0.07 – 0.04P + 5.2t + 30 with the coefficient of determination 2 = 0.94. customers over a one-month perioc N(t) 12 s this model appropriate for tvalues between O and 10? O No, this model is not appropriate because this data should be modeled by a logistic function. O No, this model is not appropriate because this data should be modeled by a linear function. O Yes, this model is appropriate because the number of customers is concave down between t= 0 and t= 10. O Yes, this model is appropriate because this data should be modeled by a polynomial, and the function fits the data strongly.

A local coffee shop models the number of customers in its shop according to the time of day (t= 0 corresponds to the opening time of 5:00 a.m.) by counting customers at certain times and taking the average number The coffee shop uses the polynomial regression M) = -0.07 – 0.04P + 5.2t + 30 with the coefficient of determination 2 = 0.94. customers over a one-month perioc N(t) 12 s this model appropriate for tvalues between O and 10? O No, this model is not appropriate because this data should be modeled by a logistic function. O No, this model is not appropriate because this data should be modeled by a linear function. O Yes, this model is appropriate because the number of customers is concave down between t= 0 and t= 10. O Yes, this model is appropriate because this data should be modeled by a polynomial, and the function fits the data strongly.

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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A local coffee shop models the number of customers in its shop according to the time of day (t= 0 corresponds to the opening time of 5:00 a.m.) by counting customers at certain times and taking the average number of customers over a one-month period. The coffee shop uses the polynomial regression N(f) = –0.07 – 0.04P + 5.2t + 30 with the coefficient of determination 2 = 0.94. N(t) 50 40 30 20 10 t 1 2 3 6. 7 8. 9. 10 11 12 13 -10 Is this model appropriate for tvalues between 0 and 10? No, this model is not appropriate because this data should be modeled by a logistic function. No, this model is not appropriate because this data should be modeled by a linear function. Yes, this model is appropriate because the number of customers is concave down between t = 0 and t= 10. %3D Yes, this model is appropriate because this data should be modeled by a polynomial, and the function fits the data strongly.
Transcribed Image Text:A local coffee shop models the number of customers in its shop according to the time of day (t= 0 corresponds to the opening time of 5:00 a.m.) by counting customers at certain times and taking the average number of customers over a one-month period. The coffee shop uses the polynomial regression N(f) = –0.07 – 0.04P + 5.2t + 30 with the coefficient of determination 2 = 0.94. N(t) 50 40 30 20 10 t 1 2 3 6. 7 8. 9. 10 11 12 13 -10 Is this model appropriate for tvalues between 0 and 10? No, this model is not appropriate because this data should be modeled by a logistic function. No, this model is not appropriate because this data should be modeled by a linear function. Yes, this model is appropriate because the number of customers is concave down between t = 0 and t= 10. %3D Yes, this model is appropriate because this data should be modeled by a polynomial, and the function fits the data strongly.
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