class_docstr: Summarize the fit of a linear regression model.
  
  It handles the output of contrasts, estimates of covariance, etc.
docstr: See LikelihoodModelResults constructor.
  
  The only difference is that the whitened Y and residual values
  are stored for a regression model.
docstr_tok: ["See","LikelihoodModelResults","constructor",".","The","only","difference","is","that","the","whitened","Y","and","residual","values","are","stored","for","a","regression","model","."]
code: def __init__(
      self,
      theta,
      Y,
      model,
      whitened_Y,
      whitened_residuals,
      cov=None,
      dispersion=1.0,
      nuisance=None,
  ):
      """See LikelihoodModelResults constructor.
  
      The only difference is that the whitened Y and residual values
      are stored for a regression model.
  
      """
      LikelihoodModelResults.__init__(
          self, theta, Y, model, cov, dispersion, nuisance
      )
      self.whitened_Y = whitened_Y
      self.whitened_residuals = whitened_residuals
      self.whitened_design = model.whitened_design
  
imports: import numpy
  import scipy.linalg
  from nibabel.onetime import auto_attr
  from numpy.linalg import matrix_rank
  from nilearn.glm._utils import positive_reciprocal
  from nilearn.glm.model import LikelihoodModelResults
label_desc: Use image node_id 1 to create a new RegressionResults object from inherited base classes: LikelihoodModelResults with example: obj = RegressionResults(theta, Y, model, whitened_Y, whitened_residuals, cov, dispersion, nuisance)