Get Data¶

import statsmodels.api as sm
import statsmodels.formula.api as smf
from linearmodelplot import plot_lm_summary

import warnings
warnings.simplefilter('ignore',FutureWarning)

iris = sm.datasets.get_rdataset('iris').data

iris.columns = ['Sepal_Length','Sepal_Width','Petal_Length','Petal_Width','Species']

lm = smf.ols("Sepal_Width ~ Sepal_Length + Petal_Length", data=iris).fit()

plot_lm_summary(lm);

lm.summary()

Dep. Variable:	Sepal_Width	R-squared:	0.456
Model:	OLS	Adj. R-squared:	0.449
Method:	Least Squares	F-statistic:	61.71
Date:	Mon, 08 Oct 2018	Prob (F-statistic):	3.48e-20
Time:	15:36:27	Log-Likelihood:	-42.058
No. Observations:	150	AIC:	90.12
Df Residuals:	147	BIC:	99.15
Df Model:	2
Covariance Type:	nonrobust

	coef	std err	t	P>\|t\|	[0.025	0.975]
Intercept	1.0381	0.288	3.602	0.000	0.469	1.608
Sepal_Length	0.5612	0.065	8.590	0.000	0.432	0.690
Petal_Length	-0.3353	0.031	-10.940	0.000	-0.396	-0.275

Omnibus:	0.483	Durbin-Watson:	1.730
Prob(Omnibus):	0.785	Jarque-Bera (JB):	0.269
Skew:	-0.095	Prob(JB):	0.874
Kurtosis:	3.085	Cond. No.	80.7