Outlier Detection Techniques in R

Outliers are data the points that deviate noticeably far from other data points in a dataset. They can arise for a variety of causes, including measurement errors, experimental errors, or normal data variance. Outliers can significantly affect statistical analysis since they might distort results and make it challenging to draw reliable conclusions.
In statistical analysis, outliers must be correctly identified and handled. There are several techniques for detecting outliers, including statistical techniques like the z-score, cooks distances and interquartile range as well as pictorial techniques like box plots and scatter plots.

[@MB-sh9ur]

Simple and easy 👍

[@armanhossainchowdhury5593]

Thank you 😊

[@Salty0]

why didn't you test a single variable using 3 methods? these results are not comparable and does not explain when to perform what.

[@armanhossainchowdhury5593]

Thank you for your query. Each method have their own assumptions and criterion to apply. You can apply boxplot and z score to single variable but not cooks distance. In this tutorial, I tried to show how to use these methods in R. To know more, you have to learn the theory. Thank you.