mlinregress

numeric.stats.mlinregress(y, x)

Implements ordinary least squares (OLS) to estimate the parameters of a multiple linear regression model.

Parameters:
  • y – (array_like) Y sample data - one dimension array.
  • x – (array_like) X sample data - two dimension array.
Returns:

Estimated regression parameters and residuals.

Examples:

y = array([11.0, 12.0, 13.0, 14.0, 15.0, 16.0])
x = array([[0, 0, 0, 0, 0], [2.0, 0, 0, 0, 0], [0, 3.0, 0, 0, 0],
    [0, 0, 4.0, 0, 0], [0, 0, 0, 5.0, 0], [0, 0, 0, 0, 6.0]])
byta, residuals = np.stats.mlinregress(y, x)
print byta.astype('float')
print residuals.astype('float')
print 'y = %.2f + %.2fx1 + %.2fx2 + %.2fx3 + %.2fx4 + %.2fx5' % \
    (byta[0], byta[1], byta[2], byta[3], byta[4], byta[5])

Result:

>>> run script...
array([11.0, 0.5, 0.6666667, 0.75, 0.8, 0.8333333])
array([-3.5527137E-15, -1.7763568E-15, 0.0, 0.0, 0.0, 0.0])
y = 11.00 + 0.50x1 + 0.67x2 + 0.75x3 + 0.80x4 + 0.83x5