import numpy as np import statsmodels.api as sm import statsmodels.formula.api as smf # Import example dataset data = sm.datasets.get_rdataset( "Guerry" , "HistData" ).data # Fit regression model model_fit = smf.ols( "Lottery ~ Literacy + np.log(Pop1831)" , data = data).fit() # Show summary model_fit.summary() Copy to clipboard OLS Regression Results
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Dep. Variable: Lottery R-squared: 0.348
Model: OLS Adj. R-squared: 0.333
Method: Least Squares F-statistic: 22.20
Date: Tue, 17 Jan 2023 Prob (F-statistic): 1.90e-08
Time: 11:35:03 Log-Likelihood: -379.82
No. Observations: 86 AIC: 765.6
Df Residuals: 83 BIC: 773.0
Df Model: 2
Covariance Type: nonrobust
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coef std err t P>|t| [0.025 0.975]
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Intercept 246.4341 35.233 6.995 0.000 176.358 316.510
Literacy -0.4889 0.128 -3.832 0.000 -0.743 -0.235
np.log(Pop1831) -31.3114 5.977 -5.239 0.000 -43.199 -19.424
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Omnibus: 3.713 Durbin-Watson: 2.019
Prob(Omnibus): 0.156 Jarque-Bera (JB): 3.394
Skew: -0.487 Prob(JB): 0.183
Kurtosis: 3.003 Cond. No. 702.
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Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified. Copy to clipboard