Weighting Data

The data table can be weighted in one of two ways. You may specify a weights column in any multi-column read operation, including multi-column ASCII files, Lotus 123 files, Excel files, Quattro Pro files, SigmaPlot files, dBase, and DIF files. You may also use the TableCurve 3D Editor to enter weights for individual data points. By default all data points have a floating point weight of 1.0.

Full Floating Point Weighting

The data table weights are true floating point multipliers. A weight value is thus the inverse square of the standard deviation. If your weights are true standard deviations, apply a 1/sigma^2 conversion in your spreadsheet or enter the weight value followed by ^-2 in the editor. If you are entering a significant number of data points, you may wish to enter the weight values as standard deviations and apply a W=1/W^2 calculation using the main menu's Calculation operation.

Automatic Normalization of Weights

The weights used in the program are normalized so that the sum of the weights equals the number of active data points. This conserves the degree of freedom relative to unweighted data, and results in coefficient standard errors which better reflect the impact expected from a true floating point weighting scheme. This type of weighting differs from statistical programs which use integer weights to specify the number of identical X,Y,Z triplets. If you are entering identical X,Y,X triplets and need to see the degree of freedom increased by one for each identical triplet, you will have to enter each identical triplet separately.

Automatic Weighting of Z-transformed Selective Subset Equations

The Z-transformed selective subset equations use a hidden secondary weighting that is automatically applied in order to produce the best-possible least squares fit. This weighting consists of the inverse square of the derivative of the function of z.