Naslov
Nonparametric methods

Autori
Sporiš, Goran

Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, sažetak, znanstveni

Izvornik
6th INSHS International Christmas Sport Scientific Conference : abstracts / Hughes , Mike ; Dancs, Henriette - Szombathely : INSHS, 2011, 37-37

Skup
6th INSHS International Christmas Sport Scientific Conference

Mjesto i datum
Szombathely, Mađarska, 11.12.2011. - 14.12.2011

Vrsta sudjelovanja
Plenarno

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
nonparametric methods

Sažetak
In Performance Analysis the data gathered and processed are, in most cases, nonparametric by nature. A clear understanding of these different processes, and their respective advantages and disadvantages must be mastered by all Performance Analysts. These processes should be used for both testing the reliability of our data gathering systems, and also for comparing sets of data to determine significant differences or otherwise. The following tests will be discussed:- Sign Test, Wilcoxon Signed-Rank Test, Mann-Whitney-Wilcoxon Test, Kruskal-Wallis Test and Rank Correlation. Most of the statistical methods referred to as parametric require the use of interval- or ratio-scaled data. Nonparametric methods are often the only way to analyze nominal or ordinal data and draw statistical conclusions. Nonparametric methods require no assumptions about the population probability distributions. Nonparametric methods are often called distribution-free methods. In general, for a statistical method to be classified as nonparametric, it must satisfy at least one of the following conditions:  The method can be used with nominal data.  The method can be used with ordinal data.  The method can be used with interval or ratio data when no assumption can be made about the population probability distribution. All these principles will be discussed with each of the tests, examples from sport science introduced and analysed, the the respective pitfalls and advantages explained.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

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