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# scikits.statsmodels.regression.RegressionResults

class `scikits.statsmodels.regression.``RegressionResults`(model, params, normalized_cov_params=None, scale=1.0)

  This class summarizes the fit of a linear regression model.

  It handles the output of contrasts, estimates of covariance, etc.

  Returns: | **Attributes** : aic : Aikake’s information criteria bic : Bayes’ information criteria pinv_wexog : See specific model class docstring centered_tss : The total sum of squares centered about the mean cov_HC0 : See HC0_se below. Only available after calling HC0_se. cov_HC1 : See HC1_se below. Only available after calling HC1_se. cov_HC2 : See HC2_se below. Only available after calling HC2_se. cov_HC3 : See HC3_se below. Only available after calling HC3_se. df_model : : Model degress of freedom. The number of regressors p - 1 for the constant Note that df_model does not include the constant even though the design does. The design is always assumed to have a constant in calculating results for now. df_resid : Residual degrees of freedom. n - p. Note that the constant is included in calculating the residual degrees of freedom. ess : Explained sum of squares. The centered total sum of squares minus the sum of squared residuals. fvalue : F-statistic of the fully specified model. Calculated as the mean squared error of the model divided by the mean squared error of the residuals. f_pvalue : p-value of the F-statistic fittedvalues : The predicted the values for the original (unwhitened) design. het_scale : Only available if HC#_se is called. See HC#_se for more information. HC0_se : White’s (1980) heteroskedasticity robust standard errors. Defined as sqrt(diag(X.T X)^(-1)X.T diag(e_i^(2)) X(X.T X)^(-1) where e_i = resid[i] HC0_se is a property. It is not evaluated until it is called. When it is called the RegressionResults instance will then have another attribute cov_HC0, which is the full heteroskedasticity consistent covariance matrix and also het_scale, which is in this case just resid**2. HCCM matrices are only appropriate for OLS. HC1_se : MacKinnon and White’s (1985) alternative heteroskedasticity robust standard errors. Defined as sqrt(diag(n/(n-p)*HC_0) HC1_se is a property. It is not evaluated until it is called. When it is called the RegressionResults instance will then have another attribute cov_HC1, which is the full HCCM and also het_scale, which is in this case n/(n-p)*resid**2. HCCM matrices are only appropriate for OLS. HC2_se : MacKinnon and White’s (1985) alternative heteroskedasticity robust standard errors. Defined as (X.T X)^(-1)X.T diag(e_i^(2)/(1-h_ii)) X(X.T X)^(-1) where h_ii = x_i(X.T X)^(-1)x_i.T HC2_se is a property. It is not evaluated until it is called. When it is called the RegressionResults instance will then have another attribute cov_HC2, which is the full HCCM and also het_scale, which is in this case is resid^(2)/(1-h_ii). HCCM matrices are only appropriate for OLS. HC3_se : MacKinnon and White’s (1985) alternative heteroskedasticity robust standard errors. Defined as (X.T X)^(-1)X.T diag(e_i^(2)/(1-h_ii)^(2)) X(X.T X)^(-1) where h_ii = x_i(X.T X)^(-1)x_i.T HC3_se is a property. It is not evaluated until it is called. When it is called the RegressionResults instance will then have another attribute cov_HC3, which is the full HCCM and also het_scale, which is in this case is resid^(2)/(1-h_ii)^(2). HCCM matrices are only appropriate for OLS. model : A pointer to the model instance that called fit() or results. mse_model : Mean squared error the model. This is the explained sum of squares divided by the model degrees of freedom. mse_resid : Mean squared error of the residuals. The sum of squared residuals divided by the residual degrees of freedom. mse_total : Total mean squared error. Defined as the uncentered total sum of squares divided by n the number of observations. nobs : Number of observations n. normalized_cov_params : See specific model class docstring params : The linear coefficients that minimize the least squares criterion. This is usually called Beta for the classical linear model. pvalues : The two-tailed p values for the t-stats of the params. resid : The residuals of the model. rsquared : R-squared of a model with an intercept. This is defined here as 1 - ssr/centered_tss rsquared_adj : Adjusted R-squared. This is defined here as 1 - (n-1)/(n-p)*(1-rsquared) scale : A scale factor for the covariance matrix. Default value is ssr/(n-p). Note that the square root of scale is often called the standard error of the regression. ssr : Sum of squared (whitened) residuals. stand_errors : The standard errors of the parameter estimates. uncentered_tss : Uncentered sum of squares. Sum of the squared values of the (whitened) endogenous response variable. wresid : The residuals of the transformed/whitened regressand and regressor(s)

  Methods

  `cov_params`([r_matrix, column, scale, other]) | Returns the variance/covariance matrix.
  `conf_int`([alpha, cols]) | Returns the confidence interval of the fitted parameters.
  `f_test`(r_matrix[, scale, invcov]) | Compute an Fcontrast/F-test for a contrast matrix.
  `norm_resid`() | Residuals, normalized to have unit length and unit variance.
  `summary`([yname, xname]) |
  Parameters: |
  `t`([column]) | Return the t-statistic for a given parameter estimate.
  `t_test`(r_matrix[, scale]) | Compute a tcontrast/t-test for a row vector array.

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