This guide is for an old version of Prism. Browse the latest version or update Prism

It is often useful to differentially weight the data points. Learn why.

Prism offers four choices on the Weight tab of multiple regression:

No weighting. Regression is most often done by minimizing the sum-of-squares of the vertical distances of the data from the line or curve. Points further from the curve contribute more to the sum-of-squares. Points close to the curve contribute little. This makes sense, when you expect experimental scatter to be the same, on average, in all parts of the curve.

Weight by 1/Y^2. In many experimental situations, you expect the average distance (or rather the average absolute value of the distance) of the points from the curve to be higher when Y is higher. The points with the larger scatter will have much larger sum-of-squares and thus dominate the calculations. If you expect the relative distance (residual divided by the height of the curve) to be consistent, then you should weight by 1/Y2.

Weight by 1/Y. This weighting is sometimes used when the scatter follows a Poisson distribution -- when Y represents the number of objects in a defined space or the number of events in a defined interval. Since Prism offers Poisson regression (a choice on the Model tab), there is little use for 1/Y weighting.

Weight by 1/YK. Also called "General weighting". Read more.