A Mean for All Seasons: Trimmed, Weighted, and Geometric Means (5-7)

The arithmetic mean works most of the time, but for all the other times, we have variations on the mean. The trimmed mean deals with outliers. The weighted mean deals with circumstances when two values must be considered, such as purchasing costs per unit. The geometric mean is useful in financial data. Although they are used rarely, these three alternatives are important for times when the arithmetic mean is inadequate to describe the data.
Chapters
0:00 We need more means
0:39 The trimmed mean
2:36 The weighted mean
4:24 The geometric mean
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This video is part of a series for an introductory statistics course. It supports an academic course QBA 237 Basic Business Statistics at Missouri State University, College of Business, Department of Information Technology and Cybersecurity. Posted beginning in Fall semester 2022.
This series was designed, written, produced, recorded, edited, and posted by Dr. Todd Daniel, a Ph.D. statistician and researcher with extensive experience in academic statistical research and statistics instruction. Dr. Daniel directed a research institute for many years before doing private consulting for Research by Design, LLC. He remains active in teaching and research.
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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 2022
Visual and audio content from DigitalJuice.com
Music: Digital Juice Royalty Free Music
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[@alternativepotato]

I just wanna say that even tho, you dont get a lot of recognition
I should say you are a great contributor to mankind as a whole.
Cheers mate

[@ResearchByDesign]

Thanks for the great comment...yeah, I am a better statistician than video promoter, but I get to use the videos for my courses and eventually people tend to find them. Maybe some day people will flock to statistics videos, but until then I have the recognition of viewers like you who appreciate the content. Thank you very much for the kind words.