#### Available Best Fit Types

There are several options available when creating a Best Fit Alignment. There are three different types of Best Fit options:

1. Least Squares (default) – minimizes the average squared error of the fit among all of the features included in the best fit. This is the most common best fit type. The error value is the sum of the squared distances. Outliers don't have as bad an effect and direction of error plays no role in this fit.

2. Vector (also known as Projected Least Squares) – also minimizes the average squared error among the features being fit; however, it first snaps (or projects) the points back onto the nominal vectors of the feature. Therefore, all error will be along the nominal vectors. The error value is the sum of the squared projected distances. This type of fit is typically used when a set of points is being fit to curves and / or surfaces. With this type of fit, PC-DMIS allows points to 'slide' along the surface but discourages them from pulling away from the surface.
   Example: If the Nominal point is: 1.1.1 with the vector: 0,0,1, and Measure values 4,2,0.95, then the measured data will be adjusted to 1,1,0.95, snapped to the vector of 0,0,1.

3. Min/Max – minimizes the maximum error (the largest distance) among the features being fit. Bad measurements can affect this error value drastically. You can use this kind of alignment to determine if there is an alignment that will allow all features input to the Best Fit alignment to be within the given tolerances. This type of fit is specified by Y14.5.

Note: The Min/Max option is only available for 2D Best Fit alignments.

See "Comparing Best Fit Alignments" and "Understanding Best Fit Alignments" for more information on these types.