class_docstr: A simple ordinary least squares model.
  
  Parameters
  ----------
  design : array-like
      This is your design matrix.  Data are assumed to be column ordered
      with observations in rows.
  
  Methods
  -------
  model.__init___(design)
  model.logL(b=self.beta, Y)
  
  Attributes
  ----------
  design : ndarray
      This is the design, or X, matrix.
  
  whitened_design : ndarray
      This is the whitened design matrix.
      `design` == `whitened_design` by default for the OLSModel,
      though models that inherit from the OLSModel will whiten the design.
  
  calc_beta : ndarray
      This is the Moore-Penrose pseudoinverse of the whitened design matrix.
  
  normalized_cov_beta : ndarray
      ``np.dot(calc_beta, calc_beta.T)``
  
  df_residuals : scalar
      Degrees of freedom of the residuals.  Number of observations less the
      rank of the design.
  
  df_model : scalar
      Degrees of freedome of the model.  The rank of the design.
docstr: Construct instance.
  
  Parameters
  ----------
  design : array-like
      This is your design matrix.
      Data are assumed to be column ordered with
      observations in rows.
docstr_tok: ["Construct","instance",".","Parameters","--","--","--","--","--","design",":","array-like","This","is","your","design","matrix",".","Data","are","assumed","to","be","column","ordered","with","observations","in","rows","."]
code: def __init__(self, design):
      """Construct instance.
  
      Parameters
      ----------
      design : array-like
          This is your design matrix.
          Data are assumed to be column ordered with
          observations in rows.
  
      """
      super().__init__()
      self.initialize(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 OLSModel object  with example: obj = OLSModel(design)