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 _conf_int(self, alpha, cols):
      bse = np.asarray(self.bse)
  
      if self.use_t:
          dist = stats.t
          df_resid = getattr(self, "df_resid_inference", self.df_resid)
          df_resid = np.asarray(df_resid)[:, None]
          q = dist.ppf(1 - alpha / 2, df_resid)
      else:
          dist = stats.norm
          q = dist.ppf(1 - alpha / 2)
  
      params = np.asarray(self.params)
      lower = params - q * bse
      upper = params + q * bse
      if cols is not None:
          cols = np.asarray(cols)
          lower = lower[:, cols]
          upper = upper[:, cols]
      return np.asarray(list(zip(lower, upper)))
  
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 29 for calling the RollingRegressionResults obj's underlying member method code with example usage: obj._conf_int(alpha, cols) and returns: np