3.4 Detecting, Marking, and Excluding Outliers in JMP

Not all outliers should be removed. Sometimes outliers are data errors that can be fixed. Sometimes they help tell part of the story. In any case, it is essential to be able to identify and mark them. Then you can examine them and use your judgment to decide what to do with them. JMP provides flexibility so that you can mark, omit, or delete outliers.

Below are three JMP instructional videos. Please watch these. The first helps you detect and mark outliers. The latter two show how to subset, mark, and exclude records. All three videos can can save lots of time when you are looking for outliers and cleaning up your data.

This video summarizes how to detect and remove outliers in JMP. You do not have to watch the entire video. Below is a breakdown of what we suggest to watch.

Suggested Portion 3:29 to 22:40 (19:11 Total)

Breakdown

  • Detecting Univariate Outliers – 3:39 to 11:15 (7:36 Total)

    • Creating and Analyzing Distributions – 3:39 to 6:00

    • Marking and Saving Data Points 6:01 to 10:13

    • Summary for Detecting Univariate Outliers – 10:14 to 11:14

  • Detecting Multivariate Outliers - 11:15 to 22:40 (11:25 Total)

    • What is Multivariate Analysis and Getting Started with Correlation and Scatterplot Matrix – 11:15 to 13:14

    • Detecting Outliers with Scatterplot Matrix 13:15 to 16:40

    • Mahalanobis Distance – 16:41 to 20:40

    • Jackknife Distance – 20:41 to 22:40

Data Preparation in JMP

Group, Filter and Subset Data in JMPGroup, Filter and Subset Data in JMP (2:49)

Selecting Cells with the Same Value in JMP (56 seconds)

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