Explore Outliers
Find Outliers in Univariate and Multivariate Data
The Explore Outliers platform enables you to identify, explore, and manage outliers. Exploring and understanding outliers in your data is an important part of analysis. Outliers in data can be due to mistakes in data collection or reporting, measurement systems failure, the inclusion of error or missing value codes in the data set, or simply an unusual value. The presence of outliers can distort estimates and bias results toward those outliers.
Outliers inflate the sample variance. Sometimes retaining outliers in data is necessary, however, and removing them could underestimate the sample variance and bias the data in the opposite direction.
Whether you remove or retain outliers, it is a good practice to locate them. There are many ways to visually inspect for outliers. For example, box plots, histograms, and scatter plots can easily display these extreme values. See Visualize Your Data in Discovering JMP.
Figure 21.1 Multivariate k-Nearest Neighbor Outlier Example
