Write the multiple regression equation for miles per gallon as the response variable. Use weight and horsepower as predictor variables. How might the car rental company use this model?
Write the multiple regression equation for miles per gallon as the response variable. Use weight and horsepower as predictor variables. How might the car rental company use this model?
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question
Write the multiple regression equation for miles per gallon as the response variable. Use weight and horsepower as predictor variables.
How might the car rental company use this model?
![OLS Regression Results
===
===== ===== ===
===
R-squared:
Adj. R-squared:
Dep. Variable:
mpg
0.831
Model:
OLS
0.818
Method:
Least Squares
F-statistic:
66.21
Date:
Sat, 04 Dec 2021
Prob (F-statistic):
3.88e-11
Time:
08:48:52
Log-Likelihood:
-67.378
No. Observations:
30
AIC:
140.8
Df Residuals:
27
BIC:
145.0
Df Model:
Covariance Type:
nonrobust
=====
сoef
std err
t
P>|t|
[0.025
0.975]
Intercept
36.0368
1.558
23.131
0.000
32.840
39.234
wt
-3.6478
0.597
-6.109
0.000
-4.873
-2.423
hp
-0.0302
0.008
-3.557
0.001
-0.048
-0.013
===
====
====
============
=========
Omnibus:
5.966
Durbin-Watson:
1.353
Prob(Omnibus):
0.051
4.340
Jarque-Bera (JB):
Prob(JB):
Skew:
0.876
0.114
Kurtosis:
3.632
Cond. No.
608.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fab04051d-220f-4e22-ad42-dacf93a25243%2F607f09dc-22b7-4a0d-9d5e-c1b4695251ba%2F86nwsys_processed.png&w=3840&q=75)
Transcribed Image Text:OLS Regression Results
===
===== ===== ===
===
R-squared:
Adj. R-squared:
Dep. Variable:
mpg
0.831
Model:
OLS
0.818
Method:
Least Squares
F-statistic:
66.21
Date:
Sat, 04 Dec 2021
Prob (F-statistic):
3.88e-11
Time:
08:48:52
Log-Likelihood:
-67.378
No. Observations:
30
AIC:
140.8
Df Residuals:
27
BIC:
145.0
Df Model:
Covariance Type:
nonrobust
=====
сoef
std err
t
P>|t|
[0.025
0.975]
Intercept
36.0368
1.558
23.131
0.000
32.840
39.234
wt
-3.6478
0.597
-6.109
0.000
-4.873
-2.423
hp
-0.0302
0.008
-3.557
0.001
-0.048
-0.013
===
====
====
============
=========
Omnibus:
5.966
Durbin-Watson:
1.353
Prob(Omnibus):
0.051
4.340
Jarque-Bera (JB):
Prob(JB):
Skew:
0.876
0.114
Kurtosis:
3.632
Cond. No.
608.
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