# 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  16Lenght of Commute:      27  20  23  45  20   45 20  28  27  23  30a. Construct a scatterplot of the lenght 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 determintion.

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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

Lenght of Commute:      27  20  23  45  20   45 20  28  27  23  30

a. Construct a scatterplot of the lenght 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 determintion.

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Step 1

a.Scatter diagram of the data:

The line that best fits the data is least-squares regression line.

The procedure to draw scatterplot using EXCEL is as follows.

• Select the data Time Leaving (x) and Length of Commute (y).
• Go to Insert.
• Select Scatter under Charts.
• Right click on the obtained chart, check linear, Display Equation on chart, and Display R-squared value on chart.

The scatter diagram for the data that is obtained using EXCEL is as follows: help_outlineImage TranscriptioncloseScatter plot of Time Leaving versus Length of Commute 50 40 Length of Comm ute y 0.745x+16.283 R2 0.2192 20 Linear (Length of Commute) 10 10 20 40 30 30 fullscreen
Step 2

b.Least squares regression line:

From the scatter diagram obtained in part (a), the least squares regression line for p...

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