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statsmodels.regression.linear_model.OLSResults

class statsmodels.regression.linear_model.OLSResults(model, params, normalized_cov_params=None, scale=1.0)[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

Methods

aic()
bic()
bse()
centered_tss()
compare_f_test(restricted) use F test to test whether restricted model is correct
compare_lr_test(restricted) Likelihood ratio test to test whether restricted model is correct
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_params([r_matrix, column, scale, cov_p, ...]) Returns the variance/covariance matrix.
df_model()
df_resid()
el_test(b0_vals, param_nums[, ...]) Tests single or joint hypotheses of the regression parameters.
ess()
f_pvalue()
f_test(r_matrix[, q_matrix, cov_p, scale, ...]) Compute an F-test for a joint linear hypothesis.
fittedvalues()
fvalue()
get_influence() get an instance of Influence with influence and outlier measures
initialize(model, params, **kwd)
llf()
load(fname) load a pickle, (class method)
mse_model()
mse_resid()
mse_total()
nobs()
norm_resid() Residuals, normalized to have unit length and unit variance.
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()
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[, q_matrix, cov_p, scale]) Compute a t-test for a joint linear hypothesis of the form Rb = q
tvalues() Return the t-statistic for a given parameter estimate.
uncentered_tss()
wresid()

Attributes

HC0_se See statsmodels.RegressionResults
HC1_se See statsmodels.RegressionResults
HC2_se See statsmodels.RegressionResults
HC3_se See statsmodels.RegressionResults

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