Lookup NU author(s): Dr Shirley Coleman
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Compositional data analysis formed a main focus of statistical activities at Hong Kong University when Professor John Aitchison was head of the Statistics department. It was part of a new Master’s degree in Statistics that he set up, and as this was the ﬁrst such post graduate degree to be oﬀered in Hong Kong, it attracted many gifted statisticians from the Government Statistical Service and other employments making it a very lively program. Acknowledging the constrained nature of many types of data led to a new way of looking at proportions and percentages of components making up data items. Professor Aitchison’s seminal book. The statistical analysis of compositional data contains background theory and many examples of data arising from a wide variety of applications such as geology, economics and human behaviour. One example was an analysis of the daily activities of a statistician. This prompted an analysis of classroom activities in a range of classes and schools encountered during teacher training within the Professional Educational Studies department of Hong Kong University. It was found that the nature of the target class and the school level aﬀected the pattern of lesson activities with more listening carried out in the higher target classes and higher level schools. More time was spent dealing with educational equipment in lower level schools. Data analytics is increasingly popular in all walks of life and many small and medium enterprises are realising the beneﬁts. Compositional data forms a large part of internal company operational data and its analysis can provide useful insights. For example, the change in proportions of diﬀerent activities undertaken over time is important information for a service provider. In addition to biplots and coordinate representations of isometric log ratios, using ternary diagrams to illustrate proportions is an informative way to share ﬁndings with company staﬀ.
Author(s): Coleman S
Publication type: Article
Publication status: Published
Journal: Austrian Journal of Statistics
Print publication date: 01/09/2018
Online publication date: 08/09/2018
Acceptance date: 23/04/2018
Date deposited: 16/12/2018
ISSN (print): 1026-597X
Publisher: Austrian Statistical Society
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