BResearch was conducted on the amount of training for 5K race and the time a contestant took to run therace. The researcher recorded the number of miles a contestant ran during the last month of training andthe time it took the contestant to complete the 5K. The results are below.Miles Trained 66 71 83 100 110 110Time (Minutes) 23 27 3535 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 tocomplete the race by using the miles trained as the explanatory variable. Complete the missing parts ofthe 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 -6.7minutes.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 0.5minutes.+(d) Use your answer from part (b) to predict the time needed to complete the 5K if a runner trained 100miles last month. Round to one decimal place.minutes

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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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The correlation coefficient between midterm and final scores in a large statistics class is r=0.6. A scatterplot of the two variables is football shaped. A particular student has a midterm score that is 0.5 SDs below the average midterm score of all students. Using regression, we would predict that the student's final score is _________ SDs _________ the average final score of all students and therefore at the _________ percentile of final scores of all students.
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 65 78 90 91 104 112 Time (Minutes) 23 26 34 39 42 50 (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. ý (c) Interpret the y-intercept in the context of this scenario. O Each additional mile of training reduces the time needed to complete the 5K by 0.6 minutes. A contestant who has not trained at all in the last month can expect to complete the 5K in 0.6 minutes. X O A contestant who has not trained at all in the last…
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 45 77 37 25 60 64 Time (Minutes) 32.9 28 36.2 27.5 19.5 18.3 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 37 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 37 miles trained.This is the actual y value for the runner who trained for miles minus the predicted value.Residual=
The data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 45 inches. Is the result close to the actual weight of 333 pounds? Use a significance level of 0.05. Chest size (inches) Weight (pounds) Click the icon to view the critical values of the Pearson correlation coefficient r. 47 61 51 47 54 56 282 495 371 349 421 389 (...) What is the regression equation? y=+x (Round to one decimal place as needed.) an example Get more help. Clear all Check answer brrec Us |
Correlation and linear regression
Using the data, run a regression where you control for “Promotion,” and test the effect of “Wins” on “Attendance”. Does “Promotion” have a significant effect on “Attendance”? Wins Promotion Attendance 4 29500 36300 6 55700 40100 6 71300 41200 8 87000 53000 6 75000 44000 7 72000 45600 5 55300 39000 7 81600 47500
help
Listed below are paired data consisting of movie budget amounts and the amounts that the movies grossed. Find the regression equation, letting the budget be the predictor (x) variable. Find the best predicted amount that a movie will gross if its budget is $105 million. Use a significance level of a = 0.05. Budget ($)in Millions Gross ($) in Millions 41 23 114 75 78 47 120 64 10 59 127 22 12 150 2 0 127 18 111 65 113 112 102 94 64 98 211 41 22 288 57 Click the icon to view the critical values of the Pearson correlation coefficient r. ..... x. (Round to one decimal place as needed.) The regression equation is = + The best predicted gross for a movie with a $105 million budget is $ million. (Round to one decimal place as needed.)
For the paired data below, do a complete regression analysis: a) Compute the value of the correlation coefficient. b) Use the Critical Value (CV) from the table to determine if there is a significant linear correlation. c) Determine the linear regression equation.

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