An analytical and historical comparison between the standard deviation and the average deviation - an applied study on premature infants in Kirkuk Governorate
Keywords:
Standard deviation, mean deviationAbstract
This paper discusses the reliance of numerical analysis on the concept of the standard deviation (SD) which is widely used in Different statistical research, it’s very important in the statistical analytical. Most importantly from that, however many traditional statistics depend on it, such as (F test, analysis of variance, the effect sizes, ets). Considered the mean deviation (MAD) of the significant measures of Dispersion and reasonable alternatives competition for standard deviation (SD) and it has many uses. In this research, conducted compared historical between (SD) and (MAD) as it mention his research by the researcher Eddington [3] confirm parameter efficiency (MAD) and researcher Fisher [5] supported the efficiency parameter (SD), and supporters and opponents of each of them. Moreover, here we would like to mention that the statistical Verdict for (SD), who confirmed his support full superiority by the researcher Fisher hadn't always have the best. But we argued here, that the absolute mean deviation (AMD), has many advantages over the standard deviation, he is more efficient as an estimate of a population parameter in the real-life situation where the data contain tiny errors, or do not form a completely perfect normal distribution. Finally the research adopted technique simulation and Real data to compare (SD), and (MAD) using a normal distribution of each of them and an upnormal distribution of each of them.
References
References
Aldrich, J. 1997. RA Fisher and the making of maximum likelihood 1912-1922. Statistical Science12(3): 162-176.
Barnett, V., V. Barnett & T. Lewis. 1978. Outliers in statistical data. Laporan.
Eddington, A. S. 1914. Stellar Movements and the Structure of the Universe Ed.: Macmillan and Company, limited.
Elsayed, K. 2015. Mean Absolute Deviation: Analysis and Applications. International Journal of Business and Statistical Analysis2: 63-74.
Fisher, R. A. 1920. A mathematical examination of the methods of determining the accuracy of an observation by the mean error, and by the mean square error.
Hampel, F. & E. Zurich 1997. Is statistics too difficult? Canadian Journal of Statistics26(3): 497-513.
HINTON, P. 1995. Statistics Explained (London, Routledge).
HUBER, J. 1981. Robust Statistic (New York,Wiley and Sons).
Koltz, S., T. Kozubowski & K. Podgorski 2001. The Laplace Distribution and Generalizations: A Revisit with Applications to Communications, Economics, Engineering, and Finance, Springer-Verlag New York, Birkhauser.
MacKenzie, D. A. 1981. Statistics in Britain: 1865-1930; the social construction of scientific knowledge Ed.: Edinburgh University Press.
1Porter, T. M. 1986. The rise of statistical thinking, 1820-1900 Ed.: Princeton University Press.
Stigler, S. M. 1973. Studies in the History of Probability and Statistics. xxxii Laplace, Fisher, and the Discovery of the Concept of Sufficiency. Biometrika60(3): 439-445.
Tukey, J. W. 1960. A survey of sampling from contaminated distributions. Contributions to probability and statistics2: 448-485.
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