How to Handle Outliers in your Dataset in Business Statistics (Week 6B)

Outliers can cause big problems in your data. We learn what causes outliers, how to identify them, the problems they cause, and options for dealign with them. Some outliers should stay in the data. Others should be corrected, winsorized, or sometimes discarded. We explore the difference between univariate outliers and multivariate outliers, when they occur, and what to do about them. Dr. Daniel uses Excel to show you how to identify outliers using z-scores. All examples use the DogToys2.xlsx (link below)
Lecture date: Tuesday, February 16, 2021
Missouri State University
Snow day - lecture recorded at home office
Statistics Instructors: you are free to link to this video and the playlist for your seated or online statistics course or for other educational purposes.
Edited in Camtasia 2020
Visual and audio content from DigitalJuice.com
Music:
248609_theBlues_15_Standard
Source: Digital Juice Royalty Free Music
Link to a Google Drive folder with any files that I use in the videos including spreadsheets, the Bear Handout, and the DogToys2.xlsx dataset. As I add new files, they will appear here, as well.
drive.google.com/drive/folder...
To download, hover your cursor over the file icon and a blue download icon will appear. You do not need to request access to a file.

[@chunpongcheung465]

How can we identify whether it cause the problems or not before we do the step of the data analysis?

[@dr.hanawamin6294]

hello sir, are outliers extrem values?

[@ResearchByDesign]

Yes they are...defined as 3+ SD or 1.5 IQR (interquartile range).