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util_lr.txt
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77 lines (75 loc) · 5.21 KB
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Results from util_lr.py
Results for 1 dataset:
Method Type Slope Intercept
============================================================
Std Lib Statistics LMS 2.05476190 -0.19642857
NumPy LMS 2.05476190 -0.19642857
SciPy LMS 2.05476190 -0.19642857
Statsmodels LMS 2.05476190 -0.19642857
Scikit-Learn LMS 2.05476190 -0.19642857
Scikit-Learn Huber Robust 2.05197016 -0.17748641
Scikit-Learn RANSAC Robust 2.05476190 -0.19642857
Results for 2 dataset:
Method Type Slope Intercept
============================================================
Std Lib Statistics LMS 9.02142857 -21.09642857
NumPy LMS 9.02142857 -21.09642857
SciPy LMS 9.02142857 -21.09642857
Statsmodels LMS 9.02142857 -21.09642857
Scikit-Learn LMS 9.02142857 -21.09642857
Scikit-Learn Huber Robust 2.16535342 -0.51725797
Scikit-Learn RANSAC Robust 2.03214286 -0.12857143
Detailed Summary for Statsmodels Linear Regression (Dataset 1):
/lib/python3.12/site-packages/scipy/stats/_stats_py.py:1971: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=8
k, _ = kurtosistest(a, axis)
OLS Regression Results
==============================================================================
Dep. Variable: y R-squared: 0.988
Model: OLS Adj. R-squared: 0.986
Method: Least Squares F-statistic: 508.1
Date: Wed, 12 Jun 2024 Prob (F-statistic): 4.99e-07
Time: 12:36:27 Log-Likelihood: -5.9901
No. Observations: 8 AIC: 15.98
Df Residuals: 6 BIC: 16.14
Df Model: 1
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const -0.1964 0.460 -0.427 0.684 -1.323 0.930
x1 2.0548 0.091 22.541 0.000 1.832 2.278
==============================================================================
Omnibus: 2.323 Durbin-Watson: 3.342
Prob(Omnibus): 0.313 Jarque-Bera (JB): 0.869
Skew: -0.281 Prob(JB): 0.647
Kurtosis: 1.486 Cond. No. 11.5
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
Detailed Summary for Statsmodels Linear Regression (Dataset 2):
/lib/python3.12/site-packages/scipy/stats/_stats_py.py:1971: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=8
k, _ = kurtosistest(a, axis)
OLS Regression Results
==============================================================================
Dep. Variable: y R-squared: 0.454
Model: OLS Adj. R-squared: 0.363
Method: Least Squares F-statistic: 4.996
Date: Wed, 12 Jun 2024 Prob (F-statistic): 0.0668
Time: 12:36:27 Log-Likelihood: -36.314
No. Observations: 8 AIC: 76.63
Df Residuals: 6 BIC: 76.79
Df Model: 1
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const -21.0964 20.382 -1.035 0.341 -70.970 28.777
x1 9.0214 4.036 2.235 0.067 -0.855 18.898
==============================================================================
Omnibus: 3.745 Durbin-Watson: 1.530
Prob(Omnibus): 0.154 Jarque-Bera (JB): 1.140
Skew: 0.922 Prob(JB): 0.566
Kurtosis: 3.148 Cond. No. 11.5
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.