statsmodels.regression.linear_model.OLSResults¶
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class
statsmodels.regression.linear_model.OLSResults(model, params, normalized_cov_params=None, scale=1.0, cov_type='nonrobust', cov_kwds=None, use_t=None, **kwargs)[source]¶ Results class for for an OLS model.
Most of the methods and attributes are inherited from RegressionResults. The special methods that are only available for OLS are:
get_influence
outlier_test
el_test
conf_int_el
See also
Methods
HC0_se()See statsmodels.RegressionResults
HC1_se()See statsmodels.RegressionResults
HC2_se()See statsmodels.RegressionResults
HC3_se()See statsmodels.RegressionResults
aic()Akaike’s information criteria.
bic()Bayes’ information criteria.
bse()The standard errors of the parameter estimates.
The total (weighted) sum of squares centered about the mean.
compare_f_test(restricted)use F test to test whether restricted model is correct
compare_lm_test(restricted[, demean, use_lr])Use Lagrange Multiplier test to test whether restricted model is correct
compare_lr_test(restricted[, large_sample])Likelihood ratio test to test whether restricted model is correct
Return condition number of exogenous matrix.
conf_int([alpha, cols])Returns the confidence interval of the fitted parameters.
conf_int_el(param_num[, sig, upper_bound, …])Computes the confidence interval for the parameter given by param_num using Empirical Likelihood
cov_HC0()See statsmodels.RegressionResults
cov_HC1()See statsmodels.RegressionResults
cov_HC2()See statsmodels.RegressionResults
cov_HC3()See statsmodels.RegressionResults
cov_params([r_matrix, column, scale, cov_p, …])Returns the variance/covariance matrix.
Return eigenvalues sorted in decreasing order.
el_test(b0_vals, param_nums[, …])Tests single or joint hypotheses of the regression parameters using Empirical Likelihood.
ess()Explained sum of squares.
f_pvalue()p-value of the F-statistic
f_test(r_matrix[, cov_p, scale, invcov])Compute the F-test for a joint linear hypothesis.
The predicted values for the original (unwhitened) design.
fvalue()F-statistic of the fully specified model.
get an instance of Influence with influence and outlier measures
get_prediction([exog, transform, weights, …])compute prediction results
get_robustcov_results([cov_type, use_t])create new results instance with robust covariance as default
initialize(model, params, **kwd)Initialize (possibly re-initialize) a Results instance.
llf()Log-likelihood of model
load(fname)load a pickle, (class method)
Mean squared error the model.
Mean squared error of the residuals.
Total mean squared error.
nobs()Number of observations n.
See specific model class docstring
outlier_test([method, alpha, labels, order, …])Test observations for outliers according to method
predict([exog, transform])Call self.model.predict with self.params as the first argument.
pvalues()The two-tailed p values for the t-stats of the params.
remove data arrays, all nobs arrays from result and model
resid()The residuals of the model.
Residuals, normalized to have unit variance.
rsquared()R-squared of a model with an intercept.
Adjusted R-squared.
save(fname[, remove_data])save a pickle of this instance
scale()A scale factor for the covariance matrix.
ssr()Sum of squared (whitened) residuals.
summary([yname, xname, title, alpha])Summarize the Regression Results
summary2([yname, xname, title, alpha, …])Experimental summary function to summarize the regression results
t_test(r_matrix[, cov_p, scale, use_t])Compute a t-test for a each linear hypothesis of the form Rb = q
t_test_pairwise(term_name[, method, alpha, …])perform pairwise t_test with multiple testing corrected p-values
tvalues()Return the t-statistic for a given parameter estimate.
Uncentered sum of squares.
wald_test(r_matrix[, cov_p, scale, invcov, …])Compute a Wald-test for a joint linear hypothesis.
wald_test_terms([skip_single, …])Compute a sequence of Wald tests for terms over multiple columns
wresid()The residuals of the transformed/whitened regressand and regressor(s)