The detection and the treatment of outliers (individuals with unusual values) is an important task of data preparation. Unusual values can mislead results of subsequent data analysis.
Outliers can be detected on one variable (a man with 158 years old) or on a combination of variables (a boy with 12 years old crosses the 100 yards in 10 seconds). In this tutorial, we show how to use the UNIVARIATE OUTLIER DETECTION component. It is intended to univariate detection of outliers i.e. taking into account individually the variables.
The approaches implemented in the component come from the NIST website (see reference). We use also an additional rule based on the x-sigma deviation from the mean of the variable.
Keywords: outlier, influential point
Components: MORE UNIVARIATE CONT STAT, SCATTERPLOT WITH LABEL, UNIVARIATE OUTLIER DETECTION, UNIVARIATE CONT STAT
Tutorial: en_Tanagra_Outliers_Detection.pdf
Dataset: body_mass_index.xls
References:
NIST/SEMATECH, « e-Handbook of Statistical Methods », Section 7.1.6, « What are outliers in the data ? »
R. High, "Dealing with 'Outliers': How to Maintain Your Data's Integrity"
