Overview of this book

Learning SciPy for Numerical and Scientific Computing Second Edition
Credits
www.PacktPub.com
Preface
Free Chapter
Introduction to SciPy
Working with the NumPy Array As a First Step to SciPy
SciPy for Linear Algebra
SciPy for Numerical Analysis
SciPy for Signal Processing
SciPy for Data Mining
SciPy for Computational Geometry
Interaction with Other Languages
Index

Regression

Regression is similar to interpolation. In this case, we assume that the data is imprecise, and we require an object of predetermined structure to fit the data as closely as possible. The most basic example is univariate polynomial regression to a sequence of points. We obtain that with the `polyfit` command, which we discussed briefly in the Univariate polynomials section of this chapter. For instance, if we want to compute the regression line in the least-squares sense for a sequence of 10 uniformly spaced points in the interval (0, π/2) and their values under the `sin` function, we will issue the following commands:

```>>> import numpy
>>> import scipy
>>> import matplotlib.pyplot as plt
>>> x=numpy.linspace(0,1,10)
>>> y=numpy.sin(x*numpy.pi/2)
>>> line=numpy.polyfit(x,y,deg=1)
>>> plt.plot(x,y,'.',x,numpy.polyval(line,x),'r')
>>> plt.show()
```

This gives the following plot that shows linear regression with `polyfit...`