class_docstr: Result summary
  
  Construction does not take any parameters. Tables and text can be added
  with the `add_` methods.
  
  Attributes
  ----------
  tables : list of tables
      Contains the list of SimpleTable instances, horizontally concatenated
      tables are not saved separately.
  extra_txt : str
      extra lines that are added to the text output, used for warnings
      and explanations.
docstr: create and add a table for the parameter estimates
  
  Parameters
  ----------
  res : results instance
      some required information is directly taken from the result
      instance
  yname : {str, None}
      optional name for the endogenous variable, default is "y"
  xname : {list[str], None}
      optional names for the exogenous variables, default is "var_xx"
  alpha : float
      significance level for the confidence intervals
  use_t : bool
      indicator whether the p-values are based on the Student-t
      distribution (if True) or on the normal distribution (if False)
  
  Returns
  -------
  None : table is attached
docstr_tok: ["create","and","add","a","table","for","the","parameter","estimates","Parameters","--","--","--","--","--","res",":","results","instance","some","required","information","is","directly","taken","from","the","result","instance","yname",":","{","str",",","None","}","optional","name","for","the","endogenous","variable",",","default","is","``","y","''","xname",":","{","list","[","str","]",",","None","}","optional","names","for","the","exogenous","variables",",","default","is","``","var_xx","''","alpha",":","float","significance","level","for","the","confidence","intervals","use_t",":","bool","indicator","whether","the","p-values","are","based","on","the","Student-t","distribution","(","if","True",")","or","on","the","normal","distribution","(","if","False",")","Returns","--","--","--","-","None",":","table","is","attached"]
code: def add_table_params(
      self, res, yname=None, xname=None, alpha=0.05, use_t=True
  ):
      """create and add a table for the parameter estimates
  
      Parameters
      ----------
      res : results instance
          some required information is directly taken from the result
          instance
      yname : {str, None}
          optional name for the endogenous variable, default is "y"
      xname : {list[str], None}
          optional names for the exogenous variables, default is "var_xx"
      alpha : float
          significance level for the confidence intervals
      use_t : bool
          indicator whether the p-values are based on the Student-t
          distribution (if True) or on the normal distribution (if False)
  
      Returns
      -------
      None : table is attached
  
      """
      if res.params.ndim == 1:
          table = summary_params(
              res, yname=yname, xname=xname, alpha=alpha, use_t=use_t
          )
      elif res.params.ndim == 2:
          _, table = summary_params_2dflat(
              res,
              endog_names=yname,
              exog_names=xname,
              alpha=alpha,
              use_t=use_t,
          )
      else:
          raise ValueError("params has to be 1d or 2d")
      self.tables.append(table)
  
imports: from statsmodels.compat.python import lmap, lrange, lzip
  import copy
  from itertools import zip_longest
  import time
  import numpy
  from statsmodels.iolib.table import SimpleTable
  from statsmodels.iolib.tableformatting import fmt_2, fmt_2cols, fmt_params, gen_fmt
  from .summary2 import _model_types
label_desc: Use image node_id 7 for calling the Summary obj's underlying member method code with example usage: obj.add_table_params(res, yname, xname, alpha, use_t) without return types