A general theory for the construction of best-fit correlation equations for multi-dimensioned numerical data

A general theory for the construction of best-fit correlation equations for multi-dimensioned sets of numerical data is presented. This new theory is based on the mathematics of n-dimensional surfaces and goodness-of-fit statistics. It is shown that orthogonal best-fit analytical trend lines for each of the independent parameters of the data can be used to construct an overall best-fit correlation equation that satisfies both physical boundary conditions and best-of-fit statistical measurements. Application of the theory is illustrated by fitting a three-parameter set of numerical finite-element maximum-stress data obtained earlier by Dr. Moffat for pressure vessel nozzles and/or piping system branch connections.