User`s guide

Excluding and Sectioning Data
2-25
Excluding and Sectioning Data
If there is justification, you might want to exclude part of a data set from a fit.
Typically, you exclude data so that subsequent fits are not adversely affected.
For example, if you are fitting a parametric model to measured data that has
been corrupted by a faulty sensor, the resulting fit coefficients will be
inaccurate.
The Curve Fitting Toolbox provides two methods to exclude data:
• Marking Outliers — Outliers are defined as individual data points that you
exclude because they are inconsistent with the statistical nature of the bulk
of the data.
• Sectioning — Sectioning excludes a window of response or predictor data.
For example, if many data points in a data set are corrupted by large
systematic errors, you might want to section them out of the fit.
For each of these methods, you must create an exclusion rule, which captures
the range, domain, or index of the data points to be excluded.
To exclude data while fitting, you use the Fitting GUI to associate the
appropriate exclusion rule with the data set to be fit. Refer to “Example: Robust
Fit” on page 3-61 for more information about fitting a data set using an
exclusion rule.