statsmodels.regression.linear_model.OLSResults

class statsmodels.regression.linear_model.OLSResults(model, params, normalized_cov_params=None, scale=1.0, cov_type='nonrobust', cov_kwds=None, use_t=None)[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

RegressionResults

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
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()
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
mse_model()
mse_resid()
mse_total()
nobs()
outlier_test([method, alpha]) Test observations for outliers according to method
resid()
resid_pearson() Residuals, normalized to have unit variance.
rsquared()
rsquared_adj()
scale()
ssr()
summary([yname, xname, title, alpha]) Summarize the Regression Results
summary2([yname, xname, title, alpha, ...]) Experimental summary function to summarize the regression results
uncentered_tss()
wresid()

Attributes

use_t