Of parametric and nonparametric statistical procedures gebruiksaanwijzing

The key difference between parametric and nonparametric test is that the parametric test relies on statistical distributions in data whereas nonparametric do not depend on any distribution. Non-parametric does not make any assumptions and measures the central tendency with the median value. Some examples of non-parametric tests include Mann-Whitney, Kruskal-Wallis, etc. fileslib. This can be considered a "semi-parametric" test because it is held under the assumption that the two samples have the same scale parameter value. References Conover, W.J. (1999): Practical Nonparametric Statistics, 3rd Edition. New York: John Wiley & Sons. Sheskin, D.J. (2011): Handbook of Parametric and Nonparametric Statistical Procedures Since then, many statistical procedures including parametric and nonparametric procedures based on the RSS have been investigated in the literature by many others, namely, Takahasi and Wakimoto (1968), Dell and Clutter (1972), Stokes (1980, 1995), Stokes and Sager (1988), Bohn and Wolfe (1992, 1994), Chen (1999), and Balakrishnan and Li (2005, 2008). With more than 500 pages of new material, the Handbook of Parametric and Nonparametric Statistical Procedures, Fourth Edition carries on the esteemed tradition of the previous editions, providing up-to-date, in-depth coverage of now more than 160 statistical procedures.
Called the "bible of applied statistics," the first edition of the bestselling Handbook of Parametric and Nonparametric Statistical Procedures was unsurpassed in its scope. The Second Edition goes even further - more tests, more examples, more than 250 pages of new material.
The fourth one contains eight additional chapters (tests) concerning multivariate statistical methods and some medical statistics. Their titles are: multivariate regression, Hotelling's T2, multivariate analysis of variance, multivariate analysis of covariance, discriminant function analysis, canonical correlational, logistic regression, and principal components analysis and factor analysis.

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