class_docstr: This class summarizes the fit of a linear regression model.
  
  It handles the output of contrasts, estimates of covariance, etc.
class_docstr_tok: ["This","class","summarizes","the","fit","of","a","linear","regression","model",".","It","handles","the","output","of","contrasts",",","estimates","of","covariance",",","etc","."]
docstr: Residuals, normalized to have unit length.
  
  Notes
  -----
  Is this supposed to return "standardized residuals,"
  residuals standardized
  to have mean zero and approximately unit variance?
  
  d_i = e_i / sqrt(MS_E)
  
  Where MS_E = SSE / (n - k)
  
  See: Montgomery and Peck 3.2.1 p. 68
       Davidson and MacKinnon 15.2 p 662
code: def norm_resid(self):
      """
      Residuals, normalized to have unit length.
  
      Notes
      -----
      Is this supposed to return "standardized residuals,"
      residuals standardized
      to have mean zero and approximately unit variance?
  
      d_i = e_i / sqrt(MS_E)
  
      Where MS_E = SSE / (n - k)
  
      See: Montgomery and Peck 3.2.1 p. 68
           Davidson and MacKinnon 15.2 p 662
      """
      return self.resid * pos_recipr(np.sqrt(self.dispersion))
  
imports: import warnings
  import numpy
  import numpy.linalg
  import scipy.linalg
  from nibabel.onetime import auto_attr
  from scipy import stats
  from nipy.algorithms.utils.matrices import matrix_rank, pos_recipr
  from .model import LikelihoodModel, LikelihoodModelResults
label_desc: Use image node_id 3 for calling the RegressionResults obj's underlying member method code with example usage: obj.norm_resid() and returns: unknown