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? Click the icon to view the table of regression equations. Choose the correct answer below. OA. The equation CITY=6.86-0.00131WT-0.258DISP+0.659HWY is best because it has a low P-value and the highest value of R². OB. The equation CITY=6.73 -0.00157WT +0.668HWY is best because it has a low P-value and the highest adjusted value of R². OC. The equation CITY = -3.15+0.823HWY is best because it has a low P-value and its R² and adjusted R² values are comparable to the R² and adjusted R² values of equations with more predictor variables. O D. The equation CITY=6.86 -0.00131WT-0.258DISP+0.659HWY is best because it uses all of the available predictor variables. - X Regression Table Predictor (x) Variables P-Value R² Adjusted R² Regression Equation WT/DISP/HWY 0.000 0.943 0.933 WT/DISP 0.000 0.748 0.720 WT/HWY 0.000 0.942 0.936 DISP/HWY 0.000 0.934 0.927 CITY=6.86-0.00131WT-0.258DISP+0.659HWY CITY = 38.4-0.00157WT-1.31DISP CITY=6.73-0.00157WT+0.668HWY CITY = 1.85-0.626DISP+0.702HWY CITY=41.8-0.00604WT CITY=29.4-2.96DISP CITY = -3.15+0.823HWY WT 0.000 0.713 0.698 DISP 0.000 0.659 0.641 HWY 0.000 0.924 0.920
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? Click the icon to view the table of regression equations. Choose the correct answer below. OA. The equation CITY=6.86-0.00131WT-0.258DISP+0.659HWY is best because it has a low P-value and the highest value of R². OB. The equation CITY=6.73 -0.00157WT +0.668HWY is best because it has a low P-value and the highest adjusted value of R². OC. The equation CITY = -3.15+0.823HWY is best because it has a low P-value and its R² and adjusted R² values are comparable to the R² and adjusted R² values of equations with more predictor variables. O D. The equation CITY=6.86 -0.00131WT-0.258DISP+0.659HWY is best because it uses all of the available predictor variables. - X Regression Table Predictor (x) Variables P-Value R² Adjusted R² Regression Equation WT/DISP/HWY 0.000 0.943 0.933 WT/DISP 0.000 0.748 0.720 WT/HWY 0.000 0.942 0.936 DISP/HWY 0.000 0.934 0.927 CITY=6.86-0.00131WT-0.258DISP+0.659HWY CITY = 38.4-0.00157WT-1.31DISP CITY=6.73-0.00157WT+0.668HWY CITY = 1.85-0.626DISP+0.702HWY CITY=41.8-0.00604WT CITY=29.4-2.96DISP CITY = -3.15+0.823HWY WT 0.000 0.713 0.698 DISP 0.000 0.659 0.641 HWY 0.000 0.924 0.920
Algebra & Trigonometry with Analytic Geometry
13th Edition
ISBN:9781133382119
Author:Swokowski
Publisher:Swokowski
Chapter7: Analytic Trigonometry
Section7.6: The Inverse Trigonometric Functions
Problem 94E
Related questions
Question
Transcribed Image Text: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?
Click the icon to view the table of regression equations.
Choose the correct answer below.
A. The equation CITY=6.86 -0.00131WT -0.258DISP+0.659HWY is best because it has a low P-value and the highest value of R².
B. The equation CITY=6.73 -0.00157WT +0.668HWY is best because it has a low P-value and the highest adjusted value of R².
C.
The equation CITY= -3.15+0.823HWY is best because it has a low P-value and its R² and adjusted R² values are comparable to the R² and adjusted R²
values of equations with more predictor variables.
O D. The equation CITY=6.86 -0.00131WT-0.258DISP + 0.659HWY is best because it uses all of the available predictor variables.
Regression Table
Predictor (x) Variables P-Value R²
WT/DISP/HWY
Adjusted R²
0.933
Regression Equation
0.000
0.943
WT/DISP
0.000 0.748
0.720
WT/HWY
0.000 0.942
0.936
CITY = 6.86 -0.00131WT-0.258DISP+0.659HWY
CITY = 38.4 -0.00157WT - 1.31 DISP
CITY=6.73 -0.00157WT +0.668HWY
CITY = 1.85-0.626DISP+0.702HWY
CITY = 41.8 -0.00604WT
CITY = 29.4-2.96DISP
DISP/HWY
0.000 0.934
0.927
WT
0.000 0.713
0.698
DISP
0.000 0.659
0.641
HWY
0.000 0.924
0.920
CITY 3.15 +0.823HWY
X
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