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# Linear Regression Using Numpy

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Deploying code to production can be filled with uncertainty. Reduce the risks, and deploy earlier and more often. Download this free guide to learn more. Brought to you in partnership with Rollbar.

A few posts ago, we saw how to use the function numpy.linalg.lstsq(...) to solve an over-determined system. This time, we'll use it to estimate the parameters of a regression line.

A linear regression line is of the form w 1x+w 2=y and it is the line that minimizes the sum of the squares of the distance from each data point to the line. So, given n pairs of data (x i, y i), the parameters that we are looking for are w 1 and w 2 which minimize the error

and we can compute the parameter vector w = (w 1 , w 2) T as the least-squares solution of the following over-determined system

Let's use numpy to compute the regression line:
```from numpy import arange,array,ones,random,linalg
from pylab import plot,show

xi = arange(0,9)
A = array([ xi, ones(9)])
# linearly generated sequence
y = [19, 20, 20.5, 21.5, 22, 23, 23, 25.5, 24]
w = linalg.lstsq(A.T,y)[0] # obtaining the parameters

# plotting the line
line = w[0]*xi+w[1] # regression line
plot(xi,line,'r-',xi,y,'o')
show()```
We can see the result in the plot below.

You can find more about data fitting using numpy in the following posts:

Deploying code to production can be filled with uncertainty. Reduce the risks, and deploy earlier and more often. Download this free guide to learn more. Brought to you in partnership with Rollbar.

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