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Winsorization

Winsorization is a data transformation that replaces extreme observations beyond selected thresholds with less-extreme boundary values rather than removing them.

How 2 Manning books explain it

Statistics Every Programmer Needs

Winsorization is a data-treatment technique that replaces extreme observations with less extreme boundary values.

How it is used
Supports a documented choice for handling potential outliers before fitting a linear regression.
How deep it goes
Defines replacement and discusses alternatives, but stops short of showing a winsorization implementation.
Examples
Uses stage1 and stage2 measurements, three-standard-deviation thresholds, and empty exceedance results.
What you can do after
Evaluate outlier-treatment alternatives and document a transparent, reproducible decision.

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Statistics Slam Dunk

Winsorization is a data-treatment technique that replaces extreme observations with less extreme boundary values rather than removing them.

How it is used
Uses winsorization during regression data preparation to limit outlier influence without shortening a small dataset.
How deep it goes
Explains visual detection, whisker-based replacement, verification, and post-treatment plotting, but does not develop alternative statistical capping methods.
Examples
Demonstrates capping two extreme deflections at 17.8 in a 90-row NBA hustle dataset.
What you can do after
Apply winsorization in base R by replacing values above a chosen boundary and checking the revised maximum and boxplot.

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Related concepts

These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.