Linear regression

Date:2018-03-12 (last modified), 2006-02-05 (created)

Linear Regression Example

In [5]:
# IPython magic to plot interactively on the notebook
%matplotlib notebook
In [7]:
from scipy import linspace, polyval, polyfit, sqrt, stats, randn
from matplotlib.pyplot import plot, title, show, legend

# Linear regression example
# This is a very simple example of using two scipy tools 
# for linear regression, polyfit and stats.linregress

# Sample data creation

# number of points 
n = 50
t = linspace(-5,5,n)
# parameters
a = 0.8
b = -4
x = polyval([a, b], t)
# add some noise
xn = x + randn(n)

# Linear regressison -polyfit - polyfit can be used other orders polys
(ar, br) = polyfit(t, xn, 1)
xr = polyval([ar, br], t)
# compute the mean square error
err = sqrt(sum((xr - xn)**2)/n)
print('Linear regression using polyfit')
print('parameters: a=%.2f b=%.2f \nregression: a=%.2f b=%.2f, ms error= %.3f' % (a, b, ar, br, err))

# Linear regression using stats.linregress
(a_s, b_s, r, tt, stderr) = stats.linregress(t, xn)
print('Linear regression using stats.linregress')
print('parameters: a=%.2f b=%.2f \nregression: a=%.2f b=%.2f, std error= %.3f' % (a,b,a_s,b_s,stderr))

# matplotlib ploting
title('Linear Regression Example')
plot(t, x,'g.--')
plot(t, xn, 'k.')
plot(t, xr, 'r.-')
legend(['original','plus noise', 'regression'])
Linear regression using polyfit
parameters: a=0.80 b=-4.00
regression: a=0.77 b=-4.10, ms error= 0.880

Linear regression using stats.linregress
parameters: a=0.80 b=-4.00
regression: a=0.77 b=-4.10, std error= 0.043

In [ ]:

Section author: Unknown[1], Unknown[66], TimCera, Nicolas Guarin-Zapata