Given a set of points
(*x*_{i}, *y*_{i})
for *i* = 0, 1, 2, ..., *n*, we may not be able
(or may not want) to find a function which passes through
all points, but rather, we may want to find a function
of a particular form which passes as closely as possible
to the points. For example, in Figure 1, it would make
much more sense to try to find the straight line which
passes as closely as possible to each of the points.

Figure 1. Linear regression of a straight line on a set of points.

We will look at three techniques for finding
functions which are *closest* to a given curve:

- Linear regression using linear polynomials (matching straight lines),
- General linear regression (polynomials, etc.), and
- Transformations to linear regression (for matching exponential functions).

As well, we will discuss how we can use regression
curves for extrapolation and an efficient
method (QR decomposition) for calculating least
squares curves.

# Terminology

This processes is called *regression* because the
*y* values are *regressing* (or moving towards)
the value on the curve which we find.

The term *linear* in *linear regression* refers
to the coefficients of the matching function. As a special
case, we begin by looking at linear regression using
**linear polynomials** (i.e., *y* = *ax* + *b*).