Linear regression with matplotlib and numpy

import matplotlib.pyplot as plt

import numpy as np

x # x axis data (list)

y # y axis data (list)

xp = np.linspace(min(x),max(x),100) # points on x axis for the fitted line

z = np.polyfit(x,y,1) # the third argument is to specify the dimension in the polynomial fitting.

p = np.poly1d(z) # p(xp) gives the function of the regression for values in xp

plt.plot(x, y, '.', xp, p(xp), '-')

See here about how to compute p-value of the coefficients (and F-statistics for multiple regressions)!