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Every morning, Tania leaves for work a few minutes after 7:00 A.M. For eight days, she keeps track of the time she leaves (the number of minutes after 7:00) and the number of minutes it takes her to get to work. Following are the results. Time Leaving 13 14 16 30 20 12 9. 17 16 10 16 Length of Commute 27 20 23 45 20 21 20 28 27 23 30 a. Construct a scatterplot of the length of commute (y) versus the time leaving (x). b. Compute the least-squares regression line for predicting the length of commute from the time leaving. c. Compute the coefficient of determination. d. Which point is an outlier?

Every morning, Tania leaves for work a few minutes after 7:00 A.M. For eight days, she keeps track of the time she leaves (the number of minutes after 7:00) and the number of minutes it takes her to get to work. Following are the results. Time Leaving 13 14 16 30 20 12 9. 17 16 10 16 Length of Commute 27 20 23 45 20 21 20 28 27 23 30 a. Construct a scatterplot of the length of commute (y) versus the time leaving (x). b. Compute the least-squares regression line for predicting the length of commute from the time leaving. c. Compute the coefficient of determination. d. Which point is an outlier?

Elementary Algebra
Elementary Algebra
17th Edition
ISBN:9780998625713
Author:Lynn Marecek, MaryAnne Anthony-Smith
Publisher:Lynn Marecek, MaryAnne Anthony-Smith
Chapter3: Math Models
Section3.5: Solve Uniform Motion Applications
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Every morning, Tania leaves for work a few minutes after 7:00 A.M. For eight days, she keeps track of the time she leaves (the number of minutes after 7:00) and the number of minutes it takes her to get to work. Following are the results. Time Leaving 13 14 16 30 20 12 17 16 10 16 Length of Commute 27 20 23 45 20 21 20 28 27 23 30 a. Construct a scatterplot of the length of commute (y) versus the time leaving (x). b. Compute the least-squares regression line for predicting the length of commute from the time leaving. c. Compute the coefficient of determination. d. Which point is an outlier? e. Remove the outlier and compute the least-squares regression line for predicting the length of commute from the time leaving. f. Is the outlier influential? Explain. g. Compute the coefficient of determination for the data set with the outlier removed. Is the relationship stronger, weaker, or about equally strong without the outlier? h. Compute the point estimates be and bị. i. Construct a 95% confidence interval for B1. j. Test the hypotheses Ho: BI = 0 versus H1 B1 +0. Use the a = 0.01 level of significance.
Transcribed Image Text:Every morning, Tania leaves for work a few minutes after 7:00 A.M. For eight days, she keeps track of the time she leaves (the number of minutes after 7:00) and the number of minutes it takes her to get to work. Following are the results. Time Leaving 13 14 16 30 20 12 17 16 10 16 Length of Commute 27 20 23 45 20 21 20 28 27 23 30 a. Construct a scatterplot of the length of commute (y) versus the time leaving (x). b. Compute the least-squares regression line for predicting the length of commute from the time leaving. c. Compute the coefficient of determination. d. Which point is an outlier? e. Remove the outlier and compute the least-squares regression line for predicting the length of commute from the time leaving. f. Is the outlier influential? Explain. g. Compute the coefficient of determination for the data set with the outlier removed. Is the relationship stronger, weaker, or about equally strong without the outlier? h. Compute the point estimates be and bị. i. Construct a 95% confidence interval for B1. j. Test the hypotheses Ho: BI = 0 versus H1 B1 +0. Use the a = 0.01 level of significance.
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