The average speed reached by the winners of the Tour de France increased by 53.9% since the first race in 1903 through 2017. We performed a regression of the average winning speeds on years, for the modern era of the Tour, from 1967 to 2017. We obtained the following scatterplot, showing the fitted line: ̂ Average Speed = −225.48 + 0.1323 Y ear. i) Are the conditions for regression met? ii) The years 1999–2005 have been disqualified because of doping. How does this help explain the fact that 9 out of 10 residuals in the final 10 years are negative? iii) Would you extrapolate from this model to predict next year’s
The average speed reached by the winners of the Tour de France increased by 53.9% since the first race in 1903 through 2017. We performed a regression of the average winning speeds on years, for the modern era of the Tour, from 1967 to 2017. We obtained the following scatterplot, showing the fitted line: ̂ Average Speed = −225.48 + 0.1323 Y ear. i) Are the conditions for regression met? ii) The years 1999–2005 have been disqualified because of doping. How does this help explain the fact that 9 out of 10 residuals in the final 10 years are negative? iii) Would you extrapolate from this model to predict next year’s
Chapter4: Linear Functions
Section: Chapter Questions
Problem 40RE: For the following exercises, consider the data in Table 5, which shows the percent of unemployed ina...
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The average speed reached by the winners of the Tour de France increased by 53.9% since the first race in 1903 through 2017. We performed a regression of the average winning speeds on years, for the modern era of the Tour, from 1967 to 2017. We obtained the following
i) Are the conditions for regression met?
ii) The years 1999–2005 have been disqualified because of doping. How does this help explain the fact that 9 out of 10 residuals in the final 10 years are negative?
iii) Would you extrapolate from this model to predict next year’s winning speed?
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