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Navigation: PRINCIPLES OF REGRESSION > Principles of nonlinear regression

Outlier elimination and robust nonlinear regression

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Nonlinear regression, like linear regression, assumes that the scatter of data around the ideal curve follows a Gaussian or normal distribution. Outliers can violate this assumption and invalidate nonlinear regression results. To deal with outliers, Prism offers robust regression and automatic outlier removal.


What are the advantages of removing outliers. But isn't it cheating?

Outlier removal has to be used carefully. It must be avoided in many situations.

Once you've identified an outlier, beware or removing it from graphs and analyses. An outlier is not always a 'bad' point.

Prism uses an outlier removal method created by GraphPad Software --the ROUT method. Learn how it works.

When identifying outliers, the step is to fit a curve using a robust method designed so outliers won't affect the curve much. Learn about robust nonlinear regression.

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