OLS Regression Results
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Dep. Variable: y R-squared: 0.975
Model: OLS Adj. R-squared: 0.973
Method: Least Squares F-statistic: 693.1
Date: Sat, 02 Jan 2021 Prob (F-statistic): 8.00e-16
Time: 20:57:08 Log-Likelihood: -30.204
No. Observations: 20 AIC: 64.41
Df Residuals: 18 BIC: 66.40
Df Model: 1
Covariance Type: nonrobust
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coef std err t P>|t| [0.025 0.975]
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Intercept -0.1367 0.602 -0.227 0.823 -1.401 1.128
x 2.9906 0.114 26.327 0.000 2.752 3.229
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Omnibus: 1.344 Durbin-Watson: 1.699
Prob(Omnibus): 0.511 Jarque-Bera (JB): 0.972
Skew: -0.519 Prob(JB): 0.615
Kurtosis: 2.706 Cond. No. 12.7
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Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.