Linear least squares

Linear least squares is a way of finding the best straight line that fits a set of data points that you might have. Imagine a bunch of points all scattered across a graph like cars in a parking lot. Linear least squares shows you which line can go through, or 'best fit', all of the points. Sometimes this line might even go through most of the points, and the points that it does not go through are called 'residuals'. The residuals are like the cars that are outside the lines of the parking lot - they don't fit perfectly, but they still count. The purpose of linear least squares is to find a line that fits the data points as closely as possible, so that if you want to make a prediction based of the data points, the line you fit should be a good one that you can trust.