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We compared the two methods for data with no outliers, with one outlier and with two outliers. •All simulations assumed a Gaussian distribution with a mean ...

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/

While Grubb's test does a good job of finding one outlier in a data set, it is known to not work so well with multiple outliers. The presence of a second ...

GraphPad FAQs

If robust regression cannot find a best-fit curve, then outlier detection can not proceed. A definition of an outlier is a point that is 'too far' from the ...

GraphPad FAQs

But Grubbs' test doesn't find any outliers in the data set on the right. The presence of the second outlier prevents the outlier test from finding the first one ...

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

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

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

... outlier. Then after removing those outliers, it it performs a standard least-squares fit on the remaining points. How robust regression works. Based on a ...

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

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 ...

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

If you select automatic outlier elimination (on the Method tab), Prism first fits each model separately to determine which points (if any) are outliers. Then it ...

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