# Regression and Recursive Estimation

Up until this point, we’ve kept things pretty abstract. How does solving $Ax=b$ for the least squares solution relate to estimation? Let’s start with an application you’ve probably used before - fitting a line to some noisy experimental data, also known as linear regression.

# Linear Regression

Let’s say you have a bunch of data points in two dimensions (for simplicity, though the result is the same for any number of dimensions). This data could be anything - for example, number of auto accidents vs miles driven, cost of soy vs annual rainfall, or even motion of planets vs time (as was the original application of linear regression) - anything in which you hope to find some trend in the data.