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Research was conducted on the amount of training for 5K and the time a contestant took to run the race. The researcher recorded the number of miles during training ( a 1 month period) and the time to complete the 5K. The results are below. Miles Trained 20 18 67 46 66 16 Time (Minutes) 27.5 33 22 18.4 17 32.1 a) Give the correlation coefficient. r = b) Give the equation of the regression line. Round the values to 4 deimal places. This is in the form y = ax + b, so the slope goes in the first box and the y-inercept in the 2nd box. y = x + c) Interpret the y-intercept (Explain it's meaning, do not just give its value.) d) Predict the time in the 5K if someone trained 67 miles. Use the rounded values entered for the slope and intercept of the regression line to compute the predicted y-value. y = e) Give the residual for 67 miles trained. This is the actual y value for the runner who trained for miles minus the predicted value. Residual=

Research was conducted on the amount of training for 5K and the time a contestant took to run the race. The researcher recorded the number of miles during training ( a 1 month period) and the time to complete the 5K. The results are below. Miles Trained 20 18 67 46 66 16 Time (Minutes) 27.5 33 22 18.4 17 32.1 a) Give the correlation coefficient. r = b) Give the equation of the regression line. Round the values to 4 deimal places. This is in the form y = ax + b, so the slope goes in the first box and the y-inercept in the 2nd box. y = x + c) Interpret the y-intercept (Explain it's meaning, do not just give its value.) d) Predict the time in the 5K if someone trained 67 miles. Use the rounded values entered for the slope and intercept of the regression line to compute the predicted y-value. y = e) Give the residual for 67 miles trained. This is the actual y value for the runner who trained for miles minus the predicted value. Residual=

Functions and Change: A Modeling Approach to College Algebra (MindTap Course List)
Functions and Change: A Modeling Approach to College Algebra (MindTap Course List)
6th Edition
ISBN:9781337111348
Author:Bruce Crauder, Benny Evans, Alan Noell
Publisher:Bruce Crauder, Benny Evans, Alan Noell
Chapter3: Straight Lines And Linear Functions
Section3.CR: Chapter Review Exercises
Problem 15CR: Life Expectancy The following table shows the average life expectancy, in years, of a child born in...
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Research was conducted on the amount of training for 5K and the time a contestant took to run the race. The researcher recorded the number of miles during training ( a 1 month period) and the time to complete the 5K. The results are below. Miles Trained 20 18 67 46 66 16 Time (Minutes) 27.5 33 22 18.4 17 32.1 a) Give the correlation coefficient. r = b) Give the equation of the regression line. Round the values to 4 deimal places. This is in the form y = ax + b, so the slope goes in the first box and the y-inercept in the 2nd box. y = X + c) Interpret the y-intercept (Explain it's meaning, do not just give its value.) d) Predict the time in the 5K if someone trained 67 miles. Use the rounded values entered for the slope and intercept of the regression line to compute the predicted y-value. y = e) Give the residual for 67 miles trained. This is the actual y value for the runner who trained for miles minus the predicted value. Residual=
Transcribed Image Text:Research was conducted on the amount of training for 5K and the time a contestant took to run the race. The researcher recorded the number of miles during training ( a 1 month period) and the time to complete the 5K. The results are below. Miles Trained 20 18 67 46 66 16 Time (Minutes) 27.5 33 22 18.4 17 32.1 a) Give the correlation coefficient. r = b) Give the equation of the regression line. Round the values to 4 deimal places. This is in the form y = ax + b, so the slope goes in the first box and the y-inercept in the 2nd box. y = X + c) Interpret the y-intercept (Explain it's meaning, do not just give its value.) d) Predict the time in the 5K if someone trained 67 miles. Use the rounded values entered for the slope and intercept of the regression line to compute the predicted y-value. y = e) Give the residual for 67 miles trained. This is the actual y value for the runner who trained for miles minus the predicted value. Residual=
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