直接看return部分，举例

model = statsmodel.WLS(Y,X)
model = model.fit()
# 打印所有的结果
print(model.summary)
# 打印你想提取的结果，比如p值
print(model.pvalues)

class RegressionResults(base.LikelihoodModelResults):
    r"""
    This class summarizes the fit of a linear regression model.

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

    Returns
    -------
    **Attributes**

    aic
        Akaike's information criteria. For a model with a constant
        :math:`-2llf + 2(df\_model + 1)`. For a model without a constant
        :math:`-2llf + 2(df\_model)`.
    bic
        Bayes' information criteria. For a model with a constant
        :math:`-2llf + \log(n)(df\_model+1)`. For a model without a constant
        :math:`-2llf + \log(n)(df\_model)`
    bse
        The standard errors of the parameter estimates.
    pinv_wexog
        See specific model class docstring
    centered_tss
        The total (weighted) sum of squares centered about the mean.
    cov_HC0
        Heteroscedasticity robust covariance matrix. See HC0_se below.
    cov_HC1
        Heteroscedasticity robust covariance matrix. See HC1_se below.
    cov_HC2
        Heteroscedasticity robust covariance matrix. See HC2_se below.
    cov_HC3
        Heteroscedasticity robust covariance matrix. See HC3_se below.
    cov_type
        Parameter covariance estimator used for standard errors and t-stats
    df_model
        Model degrees of freedom. The number of regressors `p`. Does not
        include the constant if one is present
    df_resid
        Residual degrees of freedom. `n - p - 1`, if a constant is present.
        `n - p` if a constant is not included.
    ess
        Explained sum of squares.  If a constant is present, the centered
        total sum of squares minus the sum of squared residuals. If there is
        no constant, the uncentered total sum of squares is used.
    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 values for the original (unwhitened) design.
    het_scale
        adjusted squared residuals for heteroscedasticity robust standard
        errors. Is only available after `HC#_se` or `cov_HC#` is called.
        See HC#_se for more information.
    history
        Estimation history for iterative estimators
    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 cached property.
        When HC0_se or cov_HC0 is called the RegressionResults instance will
        then have another attribute `het_scale`, which is in this case is just
        resid**2.
    HC1_se
        MacKinnon and White's (1985) alternative heteroskedasticity robust
        standard errors.
        Defined as sqrt(diag(n/(n-p)*HC_0)
        HC1_see is a cached property.
        When HC1_se or cov_HC1 is called the RegressionResults instance will
        then have another attribute `het_scale`, which is in this case is
        n/(n-p)*resid**2.
    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_see is a cached property.
        When HC2_se or cov_HC2 is called the RegressionResults instance will
        then have another attribute `het_scale`, which is in this case is
        resid^(2)/(1-h_ii).
    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_see is a cached property.
        When HC3_se or cov_HC3 is called the RegressionResults instance will
        then have another attribute `het_scale`, which is in this case is
        resid^(2)/(1-h_ii)^(2).
    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.
    resid_pearson
        `wresid` normalized to have unit variance.
    rsquared
        R-squared of a model with an intercept.  This is defined here
        as 1 - `ssr`/`centered_tss` if the constant is included in the
        model and 1 - `ssr`/`uncentered_tss` if the constant is
        omitted.
    rsquared_adj
        Adjusted R-squared.  This is defined here as 1 -
        (`nobs`-1)/`df_resid` * (1-`rsquared`) if a constant is
        included and 1 - `nobs`/`df_resid` * (1-`rsquared`) if no
        constant is included.
    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.
    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)