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. [ 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: F-statistic: 0.745 mpg OLS Least Squares Fri, 26 Nov 2021 0.736 81.88 8.33e-10 Method: Date: Time: Prob (F-statistic): Log-Likelihood: 06:16:17 -75.934 No. Observations: 30 AIC: 155.9 Df Residuals: 28 BIC: 158.7 Df Model: 1 Covariance Type: nonrobust =====: ======= ========= coef p>|t|| [0.025 0.975] std err 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 === ===: ===-== Omnibus: Prob(Omnibus): Skew: Kurtosis: 2.439 Durbin-Watson: 2.338 0.295 2.073 Jarque-Bera (JB): Prob(JB): 0.625 0.355 2.689 Cond. No. 12.9 ====== ================= ============ = ============ ================= ======
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. [ 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: F-statistic: 0.745 mpg OLS Least Squares Fri, 26 Nov 2021 0.736 81.88 8.33e-10 Method: Date: Time: Prob (F-statistic): Log-Likelihood: 06:16:17 -75.934 No. Observations: 30 AIC: 155.9 Df Residuals: 28 BIC: 158.7 Df Model: 1 Covariance Type: nonrobust =====: ======= ========= coef p>|t|| [0.025 0.975] std err 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 === ===: ===-== Omnibus: Prob(Omnibus): Skew: Kurtosis: 2.439 Durbin-Watson: 2.338 0.295 2.073 Jarque-Bera (JB): Prob(JB): 0.625 0.355 2.689 Cond. No. 12.9 ====== ================= ============ = ============ ================= ======
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
![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%2F8a3c1503-b542-455f-b893-c7211642407a%2Fxt7cpdi_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
======
Transcribed Image Text:What is the slope coefficient? Is this coefficient significant at a 5% level of
significance (alpha=D0.05)? (Hint: Check the P-value, P>It|, for weight in the
Python output.) See Step 4 in the Python script.
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