O Regression TablePredictor (x) Variables P-ValueWT/DISP/HWYR2Adjusted R2Regression Equation0.0000.9430.933CITY = 6.83 - 0.00132WT - 0.253DISP + 0.654HWYWT/DISP0.0000.7490.721CITY = 38.4 - 0.00161WT - 1.28DISPWT/HWY0.0000.9420.936CITY = 6.65 - 0.00161WT + 0.675HWYDISP/HWY0.0000.9350.928CITY = 1.84 - 0.626DISP +0.707HWYWT0.0000.7120.697CITY = 41.8 - 0.00602WTDISP0.0000.6590.641CITY = 28.8 - 3.03DISPHWY0.0000.9250.921CITY = - 3.14 + 0.823HWYPrintDone The accompanying table shows results from regressions performed on data from a random sample of 21 cars. The response (y) variable is CITY (fuel consumption in mi/gal). The predictor (x) variables are WT (weight in pounds), DISP (enginedisplacement in liters), and HWY (highway fuel consumption in mi/gal). Which regression equation is best for predicting city fuel consumption? Why?E Click the icon to view the table of regression equations.Choose the correct answer below.O A. The equation CITY = 6.65 - 0.00161WT + 0.675HWY is best because it has a low P-value and the highest adjusted value of R2.O B. The equation CITY = 6.83 - 0.00132WT - 0.253DISP + 0.654HWY is best because it has a low P-value and the highest value of R?.OC. The equation CITY = 6.83 - 0.00132WT - 0.253DISP + 0.654HWY is best because it uses all of the available predictor variables.O D. The equation CITY = - 3.14 + 0.823HWY is best because it has a low P-value and its R2 and adjusted R? values are comparable to the R2 and adjusted R? values of equations with more predictor variables.

Name: O Regression TablePredictor (x) Variables P-ValueWT/DISP/HWYR2Adjuste
Uploaded: 2024-03-20T06:38:44.000Z
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Description: O Regression TablePredictor (x) Variables P-ValueWT/DISP/HWYR2Adjusted R2Regression Equation0.0000.9430.933CITY = 6.83 - 0.00132WT - 0.253DISP + 0.654HWYWT/DISP0.0000.7490.721CITY = 38.4 - 0.00161WT - 1.28DISPWT/HWY0.0000.9420.936CITY = 6.65 - 0.001

The equation City = -3.14 + 0.823HWY is best because its p-value is significant, also both R2 and…

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R2 Adjusted R² Regression Equation CITY = 6.83 – 0.00132WT – 0.253DISP + 0.654HWY CITY = 38.4 – 0.00161WT – 1.28DISP Predictor (x) Variables P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 WT/DISP/HWY 0.943 0.933 WT/DISP 0.749 0.721 WT/HWY DISP/HWY 0.942 0.936 CITY = 6.65 - 0.00161WT + 0.675HWY CITY = 1.84 – 0.626DISP + 0.707HWY CITY = 41.8 - 0.00602WT 0.935 0.928 WT 0.712 0.697 0.641 0.921 DISP 0.659 CITY = 28.8 - 3.03DISP HWY 0.925 CITY = - 3.14 + 0.823HWY Print Done

R2 Adjusted R² Regression Equation CITY = 6.83 – 0.00132WT – 0.253DISP + 0.654HWY CITY = 38.4 – 0.00161WT – 1.28DISP Predictor (x) Variables P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 WT/DISP/HWY 0.943 0.933 WT/DISP 0.749 0.721 WT/HWY DISP/HWY 0.942 0.936 CITY = 6.65 - 0.00161WT + 0.675HWY CITY = 1.84 – 0.626DISP + 0.707HWY CITY = 41.8 - 0.00602WT 0.935 0.928 WT 0.712 0.697 0.641 0.921 DISP 0.659 CITY = 28.8 - 3.03DISP HWY 0.925 CITY = - 3.14 + 0.823HWY Print Done

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O Regression Table
Predictor (x) Variables P-Value
WT/DISP/HWY
R2
Adjusted R2
Regression Equation
0.000
0.943
0.933
CITY = 6.83 - 0.00132WT - 0.253DISP + 0.654HWY
WT/DISP
0.000
0.749
0.721
CITY = 38.4 - 0.00161WT - 1.28DISP
WT/HWY
0.000
0.942
0.936
CITY = 6.65 - 0.00161WT + 0.675HWY
DISP/HWY
0.000
0.935
0.928
CITY = 1.84 - 0.626DISP +0.707HWY
WT
0.000
0.712
0.697
CITY = 41.8 - 0.00602WT
DISP
0.000
0.659
0.641
CITY = 28.8 - 3.03DISP
HWY
0.000
0.925
0.921
CITY = - 3.14 + 0.823HWY
Print
Done

The accompanying table shows results from regressions performed on data from a random sample of 21 cars. The response (y) variable is CITY (fuel consumption in mi/gal). The predictor (x) variables are WT (weight in pounds), DISP (engine
displacement in liters), and HWY (highway fuel consumption in mi/gal). Which regression equation is best for predicting city fuel consumption? Why?
E Click the icon to view the table of regression equations.
Choose the correct answer below.
O A. The equation CITY = 6.65 - 0.00161WT + 0.675HWY is best because it has a low P-value and the highest adjusted value of R2.
O B. The equation CITY = 6.83 - 0.00132WT - 0.253DISP + 0.654HWY is best because it has a low P-value and the highest value of R?.
OC. The equation CITY = 6.83 - 0.00132WT - 0.253DISP + 0.654HWY is best because it uses all of the available predictor variables.
O D. The equation CITY = - 3.14 + 0.823HWY is best because it has a low P-value and its R2 and adjusted R? values are comparable to the R2 and adjusted R? values of equations with more predictor variables.

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