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regression_data.txt
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HC0_se : Intercept 40.527194
Height 0.614598
dtype: float64 : <class 'pandas.core.series.Series'>
HC1_se : Intercept 42.252521
Height 0.640762
dtype: float64 : <class 'pandas.core.series.Series'>
HC2_se : Intercept 42.944118
Height 0.651167
dtype: float64 : <class 'pandas.core.series.Series'>
HC3_se : Intercept 45.539083
Height 0.690442
dtype: float64 : <class 'pandas.core.series.Series'>
aic : 206.71613744293103 : <class 'numpy.float64'>
bic : 209.15388909266744 : <class 'numpy.float64'>
bse : Intercept 45.412309
Height 0.669905
dtype: float64 : <class 'pandas.core.series.Series'>
centered_tss : 5442.24 : <class 'numpy.float64'>
compare_f_test : <bound method RegressionResults.compare_f_test of <statsmodels.regression.linear_model.OLSResults object at 0x0000017207017448>> : <class 'method'>
compare_lm_test : <bound method RegressionResults.compare_lm_test of <statsmodels.regression.linear_model.OLSResults object at 0x0000017207017448>> : <class 'method'>
compare_lr_test : <bound method RegressionResults.compare_lr_test of <statsmodels.regression.linear_model.OLSResults object at 0x0000017207017448>> : <class 'method'>
condition_number : 1058.6664565603846 : <class 'numpy.float64'>
conf_int : <bound method RegressionResults.conf_int of <statsmodels.regression.linear_model.RegressionResultsWrapper object at 0x0000017205451B48>> : <class 'method'>
conf_int_el : <bound method OLSResults.conf_int_el of <statsmodels.regression.linear_model.OLSResults object at 0x0000017207017448>> : <class 'method'>
cov_HC0 : [[ 1.64245348e+03 -2.48585548e+01]
[-2.48585548e+01 3.77730384e-01]] : <class 'numpy.ndarray'>
cov_HC1 : [[ 1.78527552e+03 -2.70201683e+01]
[-2.70201683e+01 4.10576504e-01]] : <class 'numpy.ndarray'>
cov_HC2 : [[ 1.84419731e+03 -2.79104946e+01]
[-2.79104946e+01 4.24019009e-01]] : <class 'numpy.ndarray'>
cov_HC3 : [[ 2.07380806e+03 -3.13844340e+01]
[-3.13844340e+01 4.76709524e-01]] : <class 'numpy.ndarray'>
cov_kwds : {'description': 'Standard Errors assume that the covariance matrix of the errors is correctly specified.'} : <class 'dict'>
cov_params : <bound method LikelihoodModelResults.cov_params of <statsmodels.regression.linear_model.RegressionResultsWrapper object at 0x0000017205451B48>> : <class 'method'>
cov_type : nonrobust : <class 'str'>
df_model : 1.0 : <class 'float'>
df_resid : 23.0 : <class 'numpy.float64'>
diagn : {'jb': 1.3529417217937338, 'jbpv': 0.5084080728307465, 'skew': 0.1475220362365999, 'kurtosis': 1.89919436401315, 'omni': 2.8971111936738976, 'omnipv': 0.23490934696978646, 'condno': 1058.6664565603846, 'mineigval': 0.10252659471531214} : <class 'dict'>
eigenvals : [1.14909210e+05 1.02526595e-01] : <class 'numpy.ndarray'>
el_test : <bound method OLSResults.el_test of <statsmodels.regression.linear_model.OLSResults object at 0x0000017207017448>> : <class 'method'>
ess : 578.1046206896572 : <class 'numpy.float64'>
f_pvalue : 0.11184630875881313 : <class 'numpy.float64'>
f_test : <bound method LikelihoodModelResults.f_test of <statsmodels.regression.linear_model.OLSResults object at 0x0000017207017448>> : <class 'method'>
fittedvalues : 0 76.222138
1 76.222138
2 80.652483
3 78.437310
4 88.405586
5 90.620759
6 90.620759
7 79.544897
8 89.513172
9 77.329724
10 84.529034
11 77.052828
12 79.544897
13 75.668345
14 88.405586
15 86.190414
16 75.668345
17 80.652483
18 85.082828
19 82.867655
20 83.975241
21 86.190414
22 85.082828
23 83.975241
24 79.544897
dtype: float64 : <class 'pandas.core.series.Series'>
fvalue : 2.7335600757369 : <class 'numpy.float64'>
get_influence : <bound method OLSResults.get_influence of <statsmodels.regression.linear_model.OLSResults object at 0x0000017207017448>> : <class 'method'>
get_prediction : <bound method RegressionResults.get_prediction of <statsmodels.regression.linear_model.OLSResults object at 0x0000017207017448>> : <class 'method'>
get_robustcov_results : <bound method RegressionResults.get_robustcov_results of <statsmodels.regression.linear_model.OLSResults object at 0x0000017207017448>> : <class 'method'>
het_scale : [ 9.69256136 48.62912689 20.7578819 84.45799524 332.82078744
129.51848621 375.40554994 381.31083676 355.48322766 181.92962453
144.96178235 4.5881002 173.67641989 120.78014109 332.82078744
947.00016958 265.38964304 48.60354271 494.29186529 4.93636921
353.43795552 250.24295738 10.56876453 481.25766433 176.18963216] : <class 'numpy.ndarray'>
info_criteria : <bound method RegressionResults.info_criteria of <statsmodels.regression.linear_model.OLSResults object at 0x0000017207017448>> : <class 'method'>
initialize : <bound method Results.initialize of <statsmodels.regression.linear_model.OLSResults object at 0x0000017207017448>> : <class 'method'>
k_constant : 1 : <class 'int'>
llf : -101.35806872146551 : <class 'numpy.float64'>
load : <bound method ResultsWrapper.load of <class 'statsmodels.regression.linear_model.RegressionResultsWrapper'>> : <class 'method'>
model : <statsmodels.regression.linear_model.OLS object at 0x000001720702EF88> : <class 'statsmodels.regression.linear_model.OLS'>
mse_model : 578.1046206896572 : <class 'numpy.float64'>
mse_resid : 211.48414692653662 : <class 'numpy.float64'>
mse_total : 226.76 : <class 'numpy.float64'>
nobs : 25.0 : <class 'float'>
normalized_cov_params : Intercept Height
Intercept 9.751454 -0.143554
Height -0.143554 0.002122 : <class 'pandas.core.frame.DataFrame'>
outlier_test : <bound method OLSResults.outlier_test of <statsmodels.regression.linear_model.OLSResults object at 0x0000017207017448>> : <class 'method'>
params : Intercept 7.551793
Height 1.107586
dtype: float64 : <class 'pandas.core.series.Series'>
predict : <bound method Results.predict of <statsmodels.regression.linear_model.OLSResults object at 0x0000017207017448>> : <class 'method'>
pvalues : Intercept 0.869379
Height 0.111846
dtype: float64 : <class 'pandas.core.series.Series'>
remove_data : <bound method LikelihoodModelResults.remove_data of <statsmodels.regression.linear_model.OLSResults object at 0x0000017207017448>> : <class 'method'>
resid : 0 2.777862
1 -6.222138
2 4.347517
3 8.562690
4 -16.405586
5 -9.620759
6 16.379241
7 18.455103
8 16.486828
9 -12.329724
10 11.470966
11 1.947172
12 12.455103
13 -9.668345
14 -16.405586
15 28.809586
16 14.331655
17 -6.652483
18 -21.082828
19 2.132345
20 -17.975241
21 14.809586
22 -3.082828
23 -20.975241
24 -12.544897
dtype: float64 : <class 'pandas.core.series.Series'>
resid_pearson : [ 0.19101689 -0.42785905 0.29895265 0.58880474 -1.12811362 -0.66156178
1.12630205 1.26904662 1.13370011 -0.84784107 0.7887894 0.13389535
0.85646265 -0.664834 -1.12811362 1.9810622 0.98550184 -0.45745128
-1.44973942 0.14662855 -1.23604938 1.01836629 -0.21198754 -1.44234136
-0.86263719] : <class 'numpy.ndarray'>
rsquared : 0.10622549183601926 : <class 'numpy.float64'>
rsquared_adj : 0.06736573061149842 : <class 'numpy.float64'>
save : <bound method ResultsWrapper.save of <statsmodels.regression.linear_model.RegressionResultsWrapper object at 0x0000017205451B48>> : <class 'method'>
scale : 211.48414692653662 : <class 'numpy.float64'>
ssr : 4864.135379310343 : <class 'numpy.float64'>
summary : <bound method RegressionResults.summary of <statsmodels.regression.linear_model.OLSResults object at 0x0000017207017448>> : <class 'method'>
summary2 : <bound method RegressionResults.summary2 of <statsmodels.regression.linear_model.OLSResults object at 0x0000017207017448>> : <class 'method'>
t_test : <bound method LikelihoodModelResults.t_test of <statsmodels.regression.linear_model.OLSResults object at 0x0000017207017448>> : <class 'method'>
t_test_pairwise : <bound method LikelihoodModelResults.t_test_pairwise of <statsmodels.regression.linear_model.OLSResults object at 0x0000017207017448>> : <class 'method'>
tvalues : Intercept 0.166294
Height 1.653348
dtype: float64 : <class 'pandas.core.series.Series'>
uncentered_tss : 175516.0 : <class 'numpy.float64'>
use_t : True : <class 'bool'>
wald_test : <bound method LikelihoodModelResults.wald_test of <statsmodels.regression.linear_model.OLSResults object at 0x0000017207017448>> : <class 'method'>
wald_test_terms : <bound method LikelihoodModelResults.wald_test_terms of <statsmodels.regression.linear_model.OLSResults object at 0x0000017207017448>> : <class 'method'>
wresid : 0 2.777862
1 -6.222138
2 4.347517
3 8.562690
4 -16.405586
5 -9.620759
6 16.379241
7 18.455103
8 16.486828
9 -12.329724
10 11.470966
11 1.947172
12 12.455103
13 -9.668345
14 -16.405586
15 28.809586
16 14.331655
17 -6.652483
18 -21.082828
19 2.132345
20 -17.975241
21 14.809586
22 -3.082828
23 -20.975241
24 -12.544897
dtype: float64 : <class 'pandas.core.series.Series'>