When the data points are equally spaced, an analytical solution to the least-squares equations can be found. This solution forms the basis of the convolution method of numerical smoothing and differentiation. Suppose that the data consists of a set of "n" points ("x""j", "y""j") ("j" = 1, ..., "n"), where "x" is an independent variable and "y""j" is a datum value. A polynomial will be fitted by linear least squares to a set of "m" (an odd number) adjacent data points, each separated by an interval "h". Firstly, a change of variable is made