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:
See also
Methods
| HC0_se() | See statsmodels.RegressionResults |
| HC1_se() | See statsmodels.RegressionResults |
| HC2_se() | See statsmodels.RegressionResults |
| HC3_se() | See statsmodels.RegressionResults |
| aic() | |
| bic() | |
| bse() | |
| centered_tss() | |
| 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 |
| condition_number() | 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 |
| 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. |
| eigenvals() | 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() | |
| f_pvalue() | |
| f_test(r_matrix[, cov_p, scale, invcov]) | Compute the F-test for a joint linear hypothesis. |
| fittedvalues() | |
| fvalue() | |
| get_influence() | get an instance of Influence with influence and outlier measures |
| get_robustcov_results([cov_type, use_t]) | create new results instance with robust covariance as default |
| initialize(model, params, **kwd) | |
| llf() | |
| load(fname) | load a pickle, (class method) |
| mse_model() | |
| mse_resid() | |
| mse_total() | |
| nobs() | |
| normalized_cov_params() | |
| outlier_test([method, alpha]) | Test observations for outliers according to method |
| predict([exog, transform]) | Call self.model.predict with self.params as the first argument. |
| pvalues() | |
| remove_data() | remove data arrays, all nobs arrays from result and model |
| resid() | |
| resid_pearson() | Residuals, normalized to have unit variance. |
| rsquared() | |
| rsquared_adj() | |
| save(fname[, remove_data]) | save a pickle of this instance |
| scale() | |
| ssr() | |
| 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 |
| tvalues() | Return the t-statistic for a given parameter estimate. |
| uncentered_tss() | |
| wald_test(r_matrix[, cov_p, scale, invcov, ...]) | Compute a Wald-test for a joint linear hypothesis. |
| wresid() |
Attributes
| use_t |