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Glenco Rocks had been having too many accidents on its work site over the past few years. In January 2015, it began a program to decrease the number of accidents per month on its work site. Below is the data and the logistic regression function fit t in months, where t=0 corresponds to January 2015. TNumber of Accidents per Month 25 20 15 10 p2 = 0.964 Time (in months), 5 10 15 20 25 30 35 40 In January of 2016 (when t= 12), Glenco Rocks concluded that there would be about 5 accidents per month by about January 2018. Is this a valid conclusion and why? Yes, this is a valid conclusion because the ? value is so close to 1, and there are no outliers. O No, this is not a valid conclusion because January 2018 (when t= 36) is too far to extrapolate based on the data. Yes, this is a valid conclusion because the regression function predicts about 5 accidents per month in January 2018 (when t= 36). No, this is not a valid conclusion because a polynomial function should have been used to fit the data.

Glenco Rocks had been having too many accidents on its work site over the past few years. In January 2015, it began a program to decrease the number of accidents per month on its work site. Below is the data and the logistic regression function fit t in months, where t=0 corresponds to January 2015. TNumber of Accidents per Month 25 20 15 10 p2 = 0.964 Time (in months), 5 10 15 20 25 30 35 40 In January of 2016 (when t= 12), Glenco Rocks concluded that there would be about 5 accidents per month by about January 2018. Is this a valid conclusion and why? Yes, this is a valid conclusion because the ? value is so close to 1, and there are no outliers. O No, this is not a valid conclusion because January 2018 (when t= 36) is too far to extrapolate based on the data. Yes, this is a valid conclusion because the regression function predicts about 5 accidents per month in January 2018 (when t= 36). No, this is not a valid conclusion because a polynomial function should have been used to fit the data.

Algebra and Trigonometry (6th Edition)
Algebra and Trigonometry (6th Edition)
6th Edition
ISBN:9780134463216
Author:Robert F. Blitzer
Publisher:Robert F. Blitzer
ChapterP: Prerequisites: Fundamental Concepts Of Algebra
Section: Chapter Questions
Problem 1MCCP: In Exercises 1-25, simplify the given expression or perform the indicated operation (and simplify,...
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Glenco Rocks had been having too many accidents on its work site over the past few years. In January 2015, it began a program to decrease the number of accidents per month on its work site. Below is the data and the logistic regression function fit to the number of work accidents per month over time, measured in months, where t= 0 corresponds to January 2015. Number of Accidents per Month 25 20 15- 10 5- ? = 0.964 Time (in months), 10 15 20 25 30 35 40 -5 In January of 2016 (when t = 12), Glenco Rocks concluded that there would be about 5 accidents per month by about January 2018. Is this a valid conclusion and why? Yes, this is a valid conclusion because the value is so close to 1, and there are no outliers. No, this is not a valid conclusion because January 2018 (when t = 36) is too far to extrapolate based on the data. Yes, this is a valid conclusion because the regression function predicts about 5 accidents per month in January 2018 (when t= 36). No, this is not a valid conclusion because a polynomial function should have been used to fit the data.
Transcribed Image Text:Glenco Rocks had been having too many accidents on its work site over the past few years. In January 2015, it began a program to decrease the number of accidents per month on its work site. Below is the data and the logistic regression function fit to the number of work accidents per month over time, measured in months, where t= 0 corresponds to January 2015. Number of Accidents per Month 25 20 15- 10 5- ? = 0.964 Time (in months), 10 15 20 25 30 35 40 -5 In January of 2016 (when t = 12), Glenco Rocks concluded that there would be about 5 accidents per month by about January 2018. Is this a valid conclusion and why? Yes, this is a valid conclusion because the value is so close to 1, and there are no outliers. No, this is not a valid conclusion because January 2018 (when t = 36) is too far to extrapolate based on the data. Yes, this is a valid conclusion because the regression function predicts about 5 accidents per month in January 2018 (when t= 36). No, this is not a valid conclusion because a polynomial function should have been used to fit the data.
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