From 04:00 PM CDT – 08:00 PM CDT (09:00 PM UTC – 01:00 AM UTC) Tuesday, April 16, ni.com will undergo system upgrades that may result in temporary service interruption.

We appreciate your patience as we improve our online experience.

LabVIEW

cancel
Showing results for 
Search instead for 
Did you mean: 

Need a method to determine weight for data points

I am trying to determine a best fit for a project of mine, however I do have some outling data points that affect the resulting best line fit.  It may not be a problem if I had many data points but I am working with only about 12-16 points to determine a non-linear best line fit. 

 

My stat class in college was rather limited and did not discuss any method for determing weighted values and I am struggling with coming up with an effective method myself.  Does anyone have a method, VI or link they could provide so I could read more upon the subject? 

 

Thanks,

 

Branden

0 Kudos
Message 1 of 4
(2,168 Views)
What kind of fit (linear, polynomial, nonlinear, etc.). Have you looked at the fitting palette? There are several options that deal well with outliers.
0 Kudos
Message 2 of 4
(2,155 Views)

I am using the nonlinear curve fit LM bound.  I did see the outliers VI but do not want to remove the data entirely, but want to reduce their affect on the best line fit.  These data points have a correlation to another function which is linear, and the outliers appear there as well.  I was going to use this correlation to determine a weight for the points used in the best line fit.

 

Branden 

0 Kudos
Message 3 of 4
(2,146 Views)

Try to play with the "method" input, e.g. bisquare.

 

A more detailed description is on the help page for linear fit or general polynomial fit, see the following image comparing how the methods deal with outliers.

 

0 Kudos
Message 4 of 4
(2,134 Views)