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 milgal). The predictor (x) variables are WT (weight in pounds), DISP (engine displacement in liters), and HWY (highway fuel consumption in mi/gal). If only one predictor (x) variable is used to predict the city fuel consumption, which single variable is best? Why? Click the icon to view the table of regression equations. The best variable is (Type integers or decimals. Do not round.) adjusted R?. because it has the best combination of P-value, and

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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 mil/gal). If only one predictor (x) variable is used to predict the city fuel consumption, which single variable is best? Why?
Click the icon to view the table of regression equations.
The best variable is
adjusted R?,
because it has the best combination of
P-value,
and
(Type integers or decimals. Do not round.)
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 mil/gal). If only one predictor (x) variable is used to predict the city fuel consumption, which single variable is best? Why? Click the icon to view the table of regression equations. The best variable is adjusted R?, because it has the best combination of P-value, and (Type integers or decimals. Do not round.)
Regression Table
R2 Adjusted R?
Predictor (x) Variables P-Value
WT/DISP/HWY
WT/DISP
WT/HWY
DISP/HWY
Regression Equation
CITY = 6.82 - 0.00133WT - 0.259DISP + 0.651HWY
CITY = 38.3 - 0.00156WT - 1.28DISP
CITY = 6.68 – 0.00156WT +0.673HWY
CITY = 1.81 - 0.621DISP + 0.704HWY
CITY = 42.1- 0.00607WT
0.000
0.000
0.000
0.944
0.934
0.747
0.719
0.941
0.934
0.000
0.936
0.929
WT
0.000
0.712
0.697
DISP
0.000
0.659
0.641
CITY = 29.1 - 2.98DISP
HWY
0.000
0.923
0.919
CITY = - 3.11 + 0.815HWY
Print
Done
Transcribed Image Text:Regression Table R2 Adjusted R? Predictor (x) Variables P-Value WT/DISP/HWY WT/DISP WT/HWY DISP/HWY Regression Equation CITY = 6.82 - 0.00133WT - 0.259DISP + 0.651HWY CITY = 38.3 - 0.00156WT - 1.28DISP CITY = 6.68 – 0.00156WT +0.673HWY CITY = 1.81 - 0.621DISP + 0.704HWY CITY = 42.1- 0.00607WT 0.000 0.000 0.000 0.944 0.934 0.747 0.719 0.941 0.934 0.000 0.936 0.929 WT 0.000 0.712 0.697 DISP 0.000 0.659 0.641 CITY = 29.1 - 2.98DISP HWY 0.000 0.923 0.919 CITY = - 3.11 + 0.815HWY Print Done
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