OLS Regression Results
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Dep. Variable: mpg R-squared: 0.073
Model: OLS Adj. R-squared: 0.070
Method: Least Squares F-statistic: 30.59
Date: Tue, 05 Dec 2023 Prob (F-statistic): 5.84e-08
Time: 15:17:43 Log-Likelihood: -1346.4
No. Observations: 392 AIC: 2697.
Df Residuals: 390 BIC: 2705.
Df Model: 1
Covariance Type: nonrobust
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coef std err t P>|t| [0.025 0.975]
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const 35.8015 2.266 15.800 0.000 31.347 40.257
speed -354.7055 64.129 -5.531 0.000 -480.788 -228.623
==============================================================================
Omnibus: 27.687 Durbin-Watson: 0.589
Prob(Omnibus): 0.000 Jarque-Bera (JB): 18.976
Skew: 0.420 Prob(JB): 7.57e-05
Kurtosis: 2.323 Cond. No. 169.
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Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.