Join Mike Chapple for an in-depth discussion in this video Handling outliers, part of Cleaning Bad Data in R.
- [Instructor] There's no one size fits all approach…for handling outliers.…You'll need to think through each situation…that you encounter and apply subject manner expertise…and common sense to resolve them.…When you encounter outliers in your dataset,…your first step should be…to investigate them in greater detail.…Look at the specific data points that are outliers…and try to figure out what's going on.…You might find that the data points are correct…and depending on the analytic technique that you're using,…it might be appropriate to leave them in the data set.…
If you find that you do have outliers that are…of the result of incorrect data,…you can treat them in several different ways.…Let's talk about three ways that you can manipulate…your data set by removing or correcting outlying values.…First, you might decide to remove records containing…outliers from the dataset entirely.…If you have no way of coming up with a reasonable value…for the outlier and that variable is critical…to your analysis, you might decide…
Where possible, instructor Mike Chapple shows how to correct the issues using R, but the same principles can be applied to any statistical programing language.
- Missing data
- Duplicate rows and values
- Converting data
- Formatting data
- Working with tidy data
- Tidying data sets
- Dealing with suspicious data
Skill Level Beginner
1. Missing Data
2. Duplicated Data
3. Formatting Data
5. Tidy Data
6. Red Flags
What's next?1m 5s
- Mark as unwatched
- Mark all as unwatched
Are you sure you want to mark all the videos in this course as unwatched?
This will not affect your course history, your reports, or your certificates of completion for this course.Cancel
Take notes with your new membership!
Type in the entry box, then click Enter to save your note.
1:30Press on any video thumbnail to jump immediately to the timecode shown.
Notes are saved with you account but can also be exported as plain text, MS Word, PDF, Google Doc, or Evernote.