It is well established that the standard measure of correlation (Pearson’s product-moment) is very sensitive to outliers. It can give extremely misleading results when a few or even a single outlier is present. A number of robust correlation measures have been proposed. We do not consider...

This paper presents a way of modelling relationships between multiple dependent and multiple independent variables. The method involves fitting coefficients to functions of these two sets of variables such that the resulting ‘aggregate’ functions have maximum correlation. The inclusion of...

While multiple regression is widely used for the construction of models relating a single response variable to multiple explanatory variables (or inputs), satisfactory methods for modelling the relationship between multiple responses or outputs as well as inputs in a single equation are not...

Surveys show that the mean absolute percentage error (MAPE) is the most widely used measure of forecast accuracy in businesses and organizations. It is however, biased: When used to select among competing prediction methods it systematically selects those whose predictions are too low. This is...

When using a model for prediction, or for representing the data, the percentage error may be more important than the absolute error. We therefore present the method of least squares regression based on percentage errors. Exact expressions are derived for the coefficients, and we show how models...