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results = ols('quantitative~qualitative', data=data).fit()
print(results.summary())
OLS Regression Results
==============================================================================
Dep. Variable: quantitative R-squared: 0.320
Model: OLS Adj. R-squared: 0.318
Method: Least Squares F-statistic: 163.5
Date: Sat, 04 Apr 2020 Prob (F-statistic): 6.20e-59
Time: 22:34:14 Log-Likelihood: -5216.2
No. Observations: 698 AIC: 1.044e+04
Df Residuals: 695 BIC: 1.045e+04
Df Model: 2
Covariance Type: nonrobust
=============================================================================================
coef std err t P>|t| [0.025 0.975]
---------------------------------------------------------------------------------------------
Intercept 1540.3932 21.668 71.091 0.000 1497.851 1582.935
qualitative[T.AVANT PAYS] -955.9445 76.336 -12.523 0.000 -1105.822 -806.067
qualitative[T.ENTRE-DEUX] -508.1553 33.608 -15.120 0.000 -574.141 -442.170
==============================================================================
Omnibus: 164.275 Durbin-Watson: 1.930
Prob(Omnibus): 0.000 Jarque-Bera (JB): 337.886
Skew: 1.307 Prob(JB): 4.26e-74
Kurtosis: 5.189 Cond. No. 5.21
============================================================================== |
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