class_docstr: Results from rolling regressions
  
  Parameters
  ----------
  model : RollingWLS
      Model instance
  store : RollingStore
      Container for raw moving window results
  k_constant : bool
      Flag indicating that the model contains a constant
  use_t : bool
      Flag indicating to use the Student's t distribution when computing
      p-values.
  cov_type : str
      Name of covariance estimator
code: def pvalues(self):
      if self.use_t:
          df_resid = getattr(self, "df_resid_inference", self.df_resid)
          df_resid = np.asarray(df_resid)[:, None]
          with np.errstate(invalid="ignore"):
              return stats.t.sf(np.abs(self.tvalues), df_resid) * 2
      else:
          with np.errstate(invalid="ignore"):
              return stats.norm.sf(np.abs(self.tvalues)) * 2
  
imports: from statsmodels.compat.numpy import lstsq
  from statsmodels.compat.pandas import Appender, Substitution, cache_readonly, call_cached_func, get_cached_doc
  from collections import namedtuple
  import numpy
  from pandas import DataFrame, MultiIndex, Series
  from scipy import stats
  from statsmodels.base import model
  from statsmodels.base.model import LikelihoodModelResults, Model
  from statsmodels.regression.linear_model import RegressionModel, RegressionResults
  from statsmodels.tools.validation import array_like, int_like, string_like
label_desc: Use image node_id 28 for calling the RollingRegressionResults obj's underlying member method code with example usage: obj.pvalues() and returns: unknown, unknown