Volume 6, Issue 2, June 2020, Page: 23-35
Estimating Average Variation About the Population Mean Using Geometric Measure of Variation
Troon John Benedict, Department of Mathematics and Physical Science, Maasai Mara University, Narok, Kenya
Karanjah Anthony, Department of Mathematic, Multimedia University, Nairobi, Kenya
Alilah Anekeya David, Department of Mathematics and Statistics, Masinde Muliro University of Science and Technology, Kakamega, Kenya
Received: Sep. 17, 2019;       Accepted: Oct. 16, 2019;       Published: Aug. 25, 2020
DOI: 10.11648/j.ijsd.20200602.11      View  173      Downloads  38
Measures of dispersion are important statistical tool used to illustrate the distribution of datasets. These measures have allowed researchers to define the distribution of various datasets especially the measures of dispersion from the mean. Researchers and mathematicians have been able to develop measures of dispersion from the mean such as mean deviation, variance and standard deviation. However, these measures have been determined not to be perfect, for example, variance give average of squared deviation which differ in unit of measurement as the initial dataset, mean deviation gives bigger average deviation than the actual average deviation because it violates the algebraic laws governing absolute numbers, while standard deviation is affected by outliers and skewed datasets. As a result, there was a need to develop a more efficient measure of variation from the mean that would overcome these weaknesses. The aim of the paper was to estimate the average variation about the population mean using geometric measure of variation. The study was able to use the geometric measure of variation to estimate the average variation about the population mean for un-weighted datasets, weighted datasets, probability mass and probability density functions with finite intervals, however, the function faces serious integration problems when estimating the average deviation for probability density functions as a result of complexity in the integrations by parts involved and also integration on infinite intervals. Despite the challenge on probability density functions, the study was able to establish that the geometric measure of variation was able to overcome the challenges faced by the existing measures of variation about the population mean.
Standard Deviation, Geometric Measure of Variation, Deviation About the Mean, Average, Mean, Absolute Deviation, Estimation
To cite this article
Troon John Benedict, Karanjah Anthony, Alilah Anekeya David, Estimating Average Variation About the Population Mean Using Geometric Measure of Variation, International Journal of Statistical Distributions and Applications. Vol. 6, No. 2, 2020, pp. 23-35. doi: 10.11648/j.ijsd.20200602.11
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Ahn, S., & Fessler, J. A., (2003). Standard Errors of Mean, Variance, and Standard Deviation Estimators. EECS Department. The University of Michigan. U.S.A.
Altman, D. G., & Bland, J. M. (2005). Standard deviations and standard errors. BMJ Volume 331.
Bhardwaj, A., (2013). Comparative Study of Various Measures of Dispersion. Journal of Advances in Mathematics. Vol 1, No 1.
Buckland, S. T., A. C. Studeny, A. E. Magurran, J. B. Illian, and S. E. Newson. (2011). The geometric mean of relative abundance indices: a biodiversity measure with a difference. Ecosphere 2 (9): 100. doi: 10.1890/ES11-00186.1.
Clark, P. L., (2012). Number Theory: A Contemporary Introduction. Available at http://math.uga.edu/~pete/4400FULL.pdf.
Deshpande, S., Gogtay, N. J., Thatte, U. M., (2016). Measures of Central Tendency and Dispersion. Journal of the Association of Physicians of India. Vol. 64. July 2016.
Grechuk, B., Molyboha, A., & Zabarankin M., (2011). Mean-Deviation Analysis in The Theory of Choice. Risk Analysis.
Hu, S., (2010). Simple Mean, Weighted Mean, or Geometric Mean?. Presented at the 2010 ISPA/SCEA Joint Annual Conference and Training Workshop.
Kum, S., & Lim, Y., (2012). A Geometric Mean of Parameterized Arithmetic and Harmonic Means of Convex Functions. Hindawi Publishing Corporation. Volume 2012, Article ID 836804.
Lawson, J. D., & Lim, Y., (2001). The Geometric Mean, Matrices, Metrics, and More. The American Mathematical Monthly.
Lee, D., In, J., & Lee, S., (2015). Standard deviation and standard error of the mean. Korean journal of anesthesiology. 68. 220-3. 10.4097/kjae.2015.68.3.220.
Leys, C., Klein, O., Bernard, P., & Licata, L., (2013). Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median. Journal of Experimental Social Psychology 49 (2013) 764–766.
Manikandan, S., (2016). Measures of dispersion. Journal of Pharmacology and Pharmacotherapeutics. October-December 2011. Vol 2. Issue 4.
McAlister, D., (1879). The Law of Geometric Mean. The Royal Society is collaborating with JSTOR to digitize, preserve, and extend access to Proceedings of the Royal Society of London.
Mindlin, D., (2011). On the Relationship between Arithmetic and Geometric Returns. Cdi Advisors Research. LLC.
Mohini, P. B., &Prajakt, J. B., (2012). What to use to express the variability of data: Standard deviation or standard error of mean?. Perspectives in clinical research July 2012.
Raymondo, J., (2015). Measures of Variation from Statistical Analysis in the Behavioral Sciences. Kendall Hunt Publishing.
Roberson, Q. M., Sturman, M. C., & Simons, T. L., (2007). Does the Measure of Dispersion Matter in Multilevel Research? A Comparison of the Relative Performance of Dispersion Indexes. Cornell University School of Hotel Administration. The Scholarly Commons.
Roenfeldt, K., (2018). Better than average: Calculating Geometric Means Using SAS. Henry. M. Foundation for the Advancement of Military Medicine.
Schuetter, J. (2007). Chapter 1. In J. Schuetter, measures of dispersation (pp. 45-54).
Thenwall, M. (2018). The precision of the arithmetic mean, geometric mean and percentiles for citation data: An experimental simulation modelling approach. Statistical Cybermetrics Research Group, School of Mathematics and Computer Science, University of Wolverhampton, Wulfruna Street, Wolverhampton, UK.
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