Model building with multiple dependent variables and constraints

The most widely used method for finding relationships between several quantities is multiple regression. This, however, is restricted to a single dependent variable. We present a more general method which allows models to be constructed with multiple variables on both sides of an equation and which can be computed easily by using a spreadsheet program. The underlying principle (originating from canonical correlation analysis) is that of maximizing the correlation between the two sides of the model equation. This paper presents a fitting procedure which makes it possible to force the estimated model to satisfy constraint conditions which it is required to have; these may arise from theory or prior knowledge or be intuitively obvious. We also show that the least squares approach to the problem is inadequate as it produces models which are not scale invariant.