The block of code below produces a simple linear regression model using "miles per gallon" as the response variable and "weight" (of the car) as a predictor variable. The ols method in statsmodels.formula.api submodule returns all statistics for this simple linear regression model. Click the block of code below and hit the Run button above. from statsmodels.formula.api import ols # create the simple linear regression model with mpg as the response variable and weight as the predictor variable model = ols( 'mpg - wt', data=cars_df).fit() #print the model summary print(model.summary()) OLS Regression Results ---- -- ----- =-------------- 0.745 R-squared: Adj. R-squared: F-statistic: Dep. Variable: mpg OLS Model: 0.736 Method: Least Squares Fri, 26 Nov 2021 81.88 Prob (F-statistic): Log-Likelihood: Date: 8.33e-10 Time: No. Observations: 06:16:17 -75.934 155.9 158.7 30 AIC: Df Residuals: 28 BIC: Df Model: 1 Covariance Type: nonrobust ----- ----- ---- ------ coef std err P>|t| (0.025 0.975) t Intercept 37.3323 2.014 18.534 0.000 33.206 41.458 wt -5.3511 0.591 -9.049 0.000 -6.562 -4.140 ====== ======= ====== === ====== 2.439 Durbin-Watson: Omnibus: Prob(Omnibus): Skew: Kurtosis: 2.338 0.295 2.073 Jarque-Bera (JB): Prob(JB): Cond. No. 0.625 0.355 2.689 12.9 ------ =----===--------- =----E==- ----===---==---- =----- =----====-------- =--
The block of code below produces a simple linear regression model using "miles per gallon" as the response variable and "weight" (of the car) as a predictor variable. The ols method in statsmodels.formula.api submodule returns all statistics for this simple linear regression model. Click the block of code below and hit the Run button above. from statsmodels.formula.api import ols # create the simple linear regression model with mpg as the response variable and weight as the predictor variable model = ols( 'mpg - wt', data=cars_df).fit() #print the model summary print(model.summary()) OLS Regression Results ---- -- ----- =-------------- 0.745 R-squared: Adj. R-squared: F-statistic: Dep. Variable: mpg OLS Model: 0.736 Method: Least Squares Fri, 26 Nov 2021 81.88 Prob (F-statistic): Log-Likelihood: Date: 8.33e-10 Time: No. Observations: 06:16:17 -75.934 155.9 158.7 30 AIC: Df Residuals: 28 BIC: Df Model: 1 Covariance Type: nonrobust ----- ----- ---- ------ coef std err P>|t| (0.025 0.975) t Intercept 37.3323 2.014 18.534 0.000 33.206 41.458 wt -5.3511 0.591 -9.049 0.000 -6.562 -4.140 ====== ======= ====== === ====== 2.439 Durbin-Watson: Omnibus: Prob(Omnibus): Skew: Kurtosis: 2.338 0.295 2.073 Jarque-Bera (JB): Prob(JB): Cond. No. 0.625 0.355 2.689 12.9 ------ =----===--------- =----E==- ----===---==---- =----- =----====-------- =--
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i need a simple linear regression equation for miles per gallon as the response variable and weight as the predictor variable.
![The block of code below produces a simple linear regression model using "miles per gallon" as the response variable and "weight" (of the car) as a predictor
variable. The ols method in statsmodels.formula.api submodule returns all statistics for this simple linear regression model.
Click the block of code below and hit the Run button above.
In [6]:
from statsmodels.formula.api import ols
# create the simple linear regression model with mpg as the response variable and weight as the predictor variable
model =
ols('mpg
wt',
data=cars_df).fit()
#print the model summary
print(model.summary())
OLS Regression Results
==== ===
=============== ===
==== ==
Dep. Variable:
Model:
R-squared:
Adj. R-squared:
mpg
0.745
OLS
0.736
Least Squares
Fri, 26 Nov 2021
Method:
F-statistic:
81.88
Prob (F-statistic):
Log-Likelihood:
Date:
8.33e-10
Time:
06:16:17
-75.934
No. Observations:
30
AIC:
155.9
Df Residuals:
28
BIC:
158.7
Df Model:
1
Covariance Type:
nonrobust
сoef
std err
t
P>|t|
[0.025
0.975]
Intercept
37.3323
2.014
18.534
0.000
33.206
41.458
wt
-5.3511
0.591
-9.049
0.000
-6.562
-4.140
==
===
===
Omnibus:
2.439
Durbin-Watson:
2.338
Prob(Omnibus):
0.295
2.073
Jarque-Bera (JB):
Prob(JB):
Skew:
0.625
0.355
Kurtosis:
2.689
Cond. No.
12.9
======](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fab04051d-220f-4e22-ad42-dacf93a25243%2F3879699e-04c7-4656-b5d4-2ab8764d7d87%2F8zw1erj_processed.png&w=3840&q=75)
Transcribed Image Text:The block of code below produces a simple linear regression model using "miles per gallon" as the response variable and "weight" (of the car) as a predictor
variable. The ols method in statsmodels.formula.api submodule returns all statistics for this simple linear regression model.
Click the block of code below and hit the Run button above.
In [6]:
from statsmodels.formula.api import ols
# create the simple linear regression model with mpg as the response variable and weight as the predictor variable
model =
ols('mpg
wt',
data=cars_df).fit()
#print the model summary
print(model.summary())
OLS Regression Results
==== ===
=============== ===
==== ==
Dep. Variable:
Model:
R-squared:
Adj. R-squared:
mpg
0.745
OLS
0.736
Least Squares
Fri, 26 Nov 2021
Method:
F-statistic:
81.88
Prob (F-statistic):
Log-Likelihood:
Date:
8.33e-10
Time:
06:16:17
-75.934
No. Observations:
30
AIC:
155.9
Df Residuals:
28
BIC:
158.7
Df Model:
1
Covariance Type:
nonrobust
сoef
std err
t
P>|t|
[0.025
0.975]
Intercept
37.3323
2.014
18.534
0.000
33.206
41.458
wt
-5.3511
0.591
-9.049
0.000
-6.562
-4.140
==
===
===
Omnibus:
2.439
Durbin-Watson:
2.338
Prob(Omnibus):
0.295
2.073
Jarque-Bera (JB):
Prob(JB):
Skew:
0.625
0.355
Kurtosis:
2.689
Cond. No.
12.9
======
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