Question

PLS HELP ASAP (100 POINTS) The line of best fit for the following data is represented by y = 0.81x + 6.9.

x y
3 9
6 9
5 13
7 13
8 16
8 11


What is the sum of the residuals? What does this tell us about the line of best fit?

A. 0.37; This indicates that the line of best fit is not very accurate and is a good model for prediction.
B. −0.37; This indicates that the line of best fit is accurate and is an overall a good model for prediction.
C. 0; This indicates that the line of best fit is very accurate and a good model for prediction.
D. 0; This indicates that the line of best fit is not very accurate and is not a good model for prediction.

Answer 1

Answer:Answer:

B. −0.37;

This indicates that the line of best fit is accurate and is an overall good model for prediction.

Explanation:

y = 0.81x + 6.9

residual value = Measured value - Predicted value

Measured value = actual y-coordinate of the point, y

Predicted value = value of y from the equation, y1

residual value = (actual y-coordinate of the point, y) - (value of y from the equation, y1)

residual value = y - y1

x y y1 residual (y-y1)

3 9 9.33 -0.33

6 9 11.76 -2.76

5 13 10.95 2.05

7 13 12.57 0.43

8 16 13.38 2.62

8 11 13.38 - 2.38

Sum of residuals:

sum = (-0.33) +(-2.76)+(2.05)+(0.43)+(2.62)+(-2.38)

sum of residuals = -0.37

ANSWER:

B. −0.37; This indicates that the line of best fit is accurate and is an overall good model for prediction.

(Though not very accurate as it should have been if the sum of residuals was equal to 0).

A total discrepancy of -0.37 is not too bad.

Answer 2

B. −0.37;

This indicates that the line of best fit is accurate and is an overall good model for prediction.

Explanation:

y = 0.81x + 6.9

residual value = Measured value - Predicted value

Measured value = actual y-coordinate of the point, y

Predicted value = value of y from the equation, y1

residual value = (actual y-coordinate of the point, y) - (value of y from the equation, y1)

residual value = y - y1

x y y1 residual (y-y1)

3 9 9.33 -0.33

6 9 11.76 -2.76

5 13 10.95 2.05

7 13 12.57 0.43

8 16 13.38 2.62

8 11 13.38 - 2.38

Sum of residuals:

sum = (-0.33) +(-2.76)+(2.05)+(0.43)+(2.62)+(-2.38)

sum of residuals = -0.37

ANSWER:

B. −0.37; This indicates that the line of best fit is accurate and is an overall good model for prediction.

(Though not very accurate as it should have been if the sum of residuals was equal to 0).

A total discrepancy of -0.37 is not too bad.