Introduction - If you have any usage issues, please Google them yourself
Least-squares is a way of approximating a set of data points by an equation, allowing you to predict intermediate values or calculate some measure of the data. You may have approximated a trend-line by drawing a straight line through a number of data points plotted on a graph. Least-squares does just this "fitting" but in a mathematical way that minimises the errors that result. My unit extends least-squares from just a straight-line fit into higher order polynomials for more complex data (where there may be a physical reason for a square-law fit, for example) or for a better match to the data.