Athlete Monitoring: Data Analysis and Visualization – Missing data
1 Comment
Athlete Monitoring: Data Analysis and Visualization
Missing Data
In real-life, missing data are the norm. Unfortunately, not all missing data should be treated the same. It is of utmost importance to understand the types of missing data, but more importantly to understand the process behind the data collection as well as context.
In this lecture, I will explain the three major types of missing values:
- Missing completely at random (MCAR)
- Missing at random (MAR)
- Missing not at random (MAR)
And I will explain how these types manifest themselves in the most common athlete monitoring data.
Ready to watch this course?
Take advantage of our promotional offer
Related Articles
-
Create custom set and rep schemes with {STMr} – Module 7: Estimation and Individualization
-
Create custom set and rep schemes with {STMr} – Module 7: Estimation and Individualization Part 2
-
Create custom set and rep schemes with {STMr} – Module 7: Estimation and Individualization Part 3
-
Create custom set and rep schemes with {STMr} – Module 7: Estimation and Individualization Part 4
This topic contains 1 reply, has 2 voices, and was last updated by 
Viewing 1 post (of 1 total)
-
Great overview in terms of missing values and how to deal with different types of missing data. Mladen again is using practical real lie examples in order to explain the topic in more intuitive way.
Viewing 1 post (of 1 total)
You must be logged in to reply to this topic.
