Linear Regression Example {{{#!python from scipy import linspace, polyval, polyfit, sqrt, stats, randn from pylab 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)) #matplotlib ploting title('Linear Regression Example') plot(t,x,'g.--') plot(t,xn,'k.') plot(t,xr,'r.-') legend(['original','plus noise', 'regression']) show() #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)) }}} attachment:linregress.png Another example: [http://www2.warwick.ac.uk/fac/sci/moac/currentstudents/peter_cock/python/lin_reg using scipy (and R) to calculate Linear Regressions]