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Briefly, it first fits a model to the data using a robust method where outliers have little impact. Then it uses a new outlier detection method, based on the ...

https://www.graphpad.com/guides/prism/latest/statistics/

How to identify or eliminate outliers Prism can identify outliers from the curve fit, and you can choose whether these should simply be plotted or also ...

https://www.graphpad.com/guides/prism/latest/curve-fitting/

Statisticians have devised several methods for detecting outliers. All the methods first quantify how far the outlier is from the other values. This can be the ...

https://www.graphpad.com/guides/prism/latest/statistics/

Outlier elimination is misleading when you chose incorrect weighting factors. The left panel above shows data fit to a dose-response model. Four outliers were ...

https://www.graphpad.com/guides/prism/latest/curve-fitting/

If Z is large enough (considering the sample size), that point is declared to be an outlier. Note that the mean and standard deviation are computed from all the ...

GraphPad FAQs

When analyzing data, you'll sometimes find that one value is far from the others. Such a value is called an "outlier", a term that is usually not defined ...

GraphPad FAQs

In Prism's nonlinear regression, an outlier is a point that is far from the best-fit curve defined by robust regression.

https://www.graphpad.com/guides/prism/latest/curve-fitting/

1. Motulsky HM and Brown RE, Detecting outliers when fitting data with nonlinear regression – a new method based on robust nonlinear regression and the false ...

GraphPad FAQs

However, experimental mistakes can lead to erroneous values – outliers. Even a single outlier can dominate the sum-of-the-squares calculation, and lead to ...

https://www.graphpad.com/guides/prism/latest/curve-fitting/

When Prism 5 fits a curve with nonlinear regression, it can identify outliers using the new ROUT method. There are two ways to choose this method in Prism 5.

GraphPad FAQs