Turn on suggestions

Auto-suggest helps you quickly narrow down your search results by suggesting possible matches as you type.

Showing results for

- JMP User Community
- :
- Discussions
- :
- Outliers in data

- Subscribe to RSS Feed
- Mark Topic as New
- Mark Topic as Read
- Float this Topic for Current User
- Bookmark
- Subscribe
- Printer Friendly Page

Highlighted
##

- Mark as New
- Bookmark
- Subscribe
- Mute
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

Outliers in data

Oct 23, 2018 2:33 PM
(2179 views)

Hi,

Is there a way in JMP to label outliers in a dataset. For example, I have 100 lots and I want to label the units that are observered on the lower side of the distrubution. I dont care if the outliers perform better than the distrubution, I am more intersted in the lower ones.

In other words for example, I have a lot with 100 units, 1 or 2 units are outliers. How can I label those units?

Thank you,

Rami

3 REPLIES 3

- Mark as New
- Bookmark
- Subscribe
- Mute
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

Re: Outliers in data

- Mark as New
- Bookmark
- Subscribe
- Mute
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

Re: Outliers in data

Thank you for the reply, but I dont see how I can do that for over 100 lots. Also how would you know those two points are outliers?

- Mark as New
- Bookmark
- Subscribe
- Mute
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

Re: Outliers in data

Labelling outliers and finding outliers are two completely different questions. There are lots of statitistical methods to determine outliers (Pierce, Grubbs, 3 sigma, box and whisker plots, ect) all of which vary and disagree. You can use the Analyze > Screening > Explore Outliers tool for this.

That said, I always put in a word of caution around outliers. For you a lot is really only an outlier if it experienced a processing different from the rest of your lots. Data shouldn't be thrown away just b/c it makes your "fit bad" or it "looks high" or it makes my "P-value significant".