scipy.stats.linregress

scipy.stats.linregress(x, y=None)

Calculate a regression line

This computes a least-squares regression for two sets of measurements.

Parameters:y (x,) -- two sets of measurements. Both arrays should have the same length. If only x is given (and y=None), then it must be a two-dimensional array where one dimension has length 2. The two sets of measurements are then found by splitting the array along the length-2 dimension. Returns:

• slope (float) -- slope of the regression line
• intercept (float) -- intercept of the regression line
• r-value (float) -- correlation coefficient
• p-value (float) -- two-sided p-value for a hypothesis test whose null hypothesis is that the slope is zero.
• stderr (float) -- Standard error of the estimate

Examples

>>> from scipy import stats
>>> import numpy as np
>>> x = np.random.random(10)
>>> y = np.random.random(10)
>>> slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)

# To get coefficient of determination (r_squared)

>>> print "r-squared:", r_value**2
r-squared: 0.15286643777