Many spatially distributed data exhibit anisotropic spatial variation, especially when the data are distributed over a large area. The Semivariogram, or commonly (and inaccurately) just variogram is a measure of spatial correlation. It simply plots the semivariance (which is half the variance) of two points separated by a vector h against the magnitude of h. Easy right? Well there is a little bit more to it...

The method of least squares, or even simply maximum likelihood is one of the more powerful tools available to a statistician. It is powerful because its simplicity means it can be used in a variety of regression problems.

Regression simply means line fitting, and lines are just a graphical way to represent a model, which is the mathematical way to describe the relationship between an independent variable and one or more dependent variables. There is a lot of text on linear straight line fitting, so I’m not going to go into too much detail. I will however briefly discuss the principle behind least squares.