Research was conducted on the amount of training for 5K race and the time a contestant took to run the race. The researcher recorded the number of miles a contestant ran during the last month of training and the time it took the contestant to complete the 5K. The results are below. Miles Trained 66 71 83 100 110 110 Time (Minutes) 23 27 35 35 45 47 (a) Give the correlation coefficient. Round to one decimal place. (b) Use technology to write the regression equation that predicts the time it takes a contestant to complete the race by using the miles trained as the explanatory variable. Complete the missing parts of the equation below, rounding values to one decimal place. y = (c) Interpret the y-intercept in the context of this scenario. Each additional mile of training reduces the time needed to complete the 5K by 0.5 minutes. A contestant who has not trained at all in the last month can expect to complete the 5K in -6.7 minutes. Each additional mile of training reduces the time needed to complete the 5K by -6.7 minutes. A contestant who has not trained at all in the last month can expect to complete the 5K in 0.5 minutes. x
Research was conducted on the amount of training for 5K race and the time a contestant took to run the race. The researcher recorded the number of miles a contestant ran during the last month of training and the time it took the contestant to complete the 5K. The results are below. Miles Trained 66 71 83 100 110 110 Time (Minutes) 23 27 35 35 45 47 (a) Give the correlation coefficient. Round to one decimal place. (b) Use technology to write the regression equation that predicts the time it takes a contestant to complete the race by using the miles trained as the explanatory variable. Complete the missing parts of the equation below, rounding values to one decimal place. y = (c) Interpret the y-intercept in the context of this scenario. Each additional mile of training reduces the time needed to complete the 5K by 0.5 minutes. A contestant who has not trained at all in the last month can expect to complete the 5K in -6.7 minutes. Each additional mile of training reduces the time needed to complete the 5K by -6.7 minutes. A contestant who has not trained at all in the last month can expect to complete the 5K in 0.5 minutes. x
MATLAB: An Introduction with Applications
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Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
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
Transcribed Image Text:**Research on Training for a 5K Race**
Research was conducted on the amount of training for a 5K race and the time a contestant took to run the race. The researcher recorded the number of miles a contestant ran during the last month of training and the time it took the contestant to complete the 5K. The results are below:
| Miles Trained | 66 | 71 | 83 | 100 | 110 | 110 | 110 |
|---------------|----|----|----|-----|-----|-----|-----|
| Time (Minutes)| 23 | 27 | 35 | 35 | 45 | 47 | 47 |
**(a)** Give the correlation coefficient. *Round to one decimal place.*
⎕
**(b)** Use technology to write the regression equation that predicts the time it takes a contestant to complete the race by using the miles trained as the explanatory variable. *Complete the missing parts of the equation below, rounding values to one decimal place.*
\( \hat{y} = \) ⎕ + ⎕\(x\)
**(c)** Interpret the \( y \)-intercept in the context of this scenario.
- ( ) Each additional mile of training reduces the time needed to complete the 5K by 0.5 minutes.
- ( ) A contestant who has not trained at all in the last month can expect to complete the 5K in -6.7 minutes.
- ( ) Each additional mile of training reduces the time needed to complete the 5K by -6.7 minutes.
- ( ) A contestant who has not trained at all in the last month can expect to complete the 5K in 0.5 minutes.
**(d)** Use your answer from part (b) to predict the time needed to complete the 5K if a runner trained 100 miles last month. *Round to one decimal place.*
⎕ minutes
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