code: def f_regression(X, y, center=True):
      """Univariate linear regression tests.
  
      Quick linear model for testing the effect of a single regressor,
      sequentially for many regressors.
  
      This is done in 2 steps:
  
      1. The cross correlation between each regressor and the target is computed,
         that is, ((X[:, i] - mean(X[:, i])) * (y - mean_y)) / (std(X[:, i]) *
         std(y)).
      2. It is converted to an F score then to a p-value.
  
      Read more in the :ref:`User Guide <univariate_feature_selection>`.
  
      Parameters
      ----------
      X : {array-like, sparse matrix}  shape = (n_samples, n_features)
          The set of regressors that will be tested sequentially.
  
      y : array of shape(n_samples).
          The data matrix
  
      center : True, bool,
          If true, X and y will be centered.
  
      Returns
      -------
      F : array, shape=(n_features,)
          F values of features.
  
      pval : array, shape=(n_features,)
          p-values of F-scores.
  
      See also
      --------
      f_classif: ANOVA F-value between label/feature for classification tasks.
      chi2: Chi-squared stats of non-negative features for classification tasks.
      """
      if issparse(X) and center:
          raise ValueError("center=True only allowed for dense data")
      X, y = check_X_y(X, y, ["csr", "csc", "coo"], dtype=np.float64)
      if center:
          y = y - np.mean(y)
          X = X.copy("F")  # faster in fortran
          X -= X.mean(axis=0)
  
      # compute the correlation
      corr = safe_sparse_dot(y, X)
      corr /= row_norms(X.T)
      corr /= norm(y)
  
      # convert to p-value
      degrees_of_freedom = y.size - (2 if center else 1)
      F = corr**2 / (1 - corr**2) * degrees_of_freedom
      pv = stats.f.sf(F, 1, degrees_of_freedom)
      return F, pv
  
imports: import numpy
  import warnings
  from scipy import special, stats
  from scipy.sparse import issparse
  from ..base import BaseEstimator
  from ..preprocessing import LabelBinarizer
  from ..utils import as_float_array, check_array, check_X_y, safe_sqr, safe_mask
  from ..utils.extmath import norm, safe_sparse_dot, row_norms
  from ..utils.validation import check_is_fitted
  from .base import SelectorMixin
label_desc: Use image node_id 6 for calling a global function with example usage: f_regression(X, y, center) and returns: F,  pv