 SampleSTAT - Toolbox for statistics of normal distributed univariate samples
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 Toolbox for statistics of normal distributed univariate samples for scientist & engineers
 
 This toolbox provides elementary tests for the evaluation of univariate measuring 
 data which were generated by natural scientists and engineers in the first place. 
 These data have to be normal distributed.
 
 SampleSTAT is focused on small sample sizes but offers routines for bigger 
 distributions (>30 values), too. It offers functions for calculates the range 
 of dispersion of the values and the mean regarding a given statistical 
 confidence level. Furthermore it provides tests on outliers and a method for 
 testing the data for normality. 
 
 FUNCTIONS - Measures of Variation:
 
   Gives you more information of your data as the standard deviation (S.D.) can 
   do with just 68% confidence. These tests provide confidence level of 95%, 99% 
   and 99.9% and calculate the range of dispersion not only for values but for 
   the mean, too. It extends the internal functions: mean, stdev, median.
 
   - ST_strayarea: 
     Calculates the stray area (range of dispersion of the values). Determines 
     the range in which the values are expected to fall within the specified 
     statistical confidence level.
   - ST_trustarea: 
     Calculates the trust area (range of dispersion of the mean or S.D. of the 
     mean). Determines the range in which the mean stray within the specified 
     statistical confidence level.
   - ST_studentfactor: 
     Determines the student factor for an amount of numbers, service 
     function for ST_staryarea and ST_trustarea

 FUNCTIONS - Tests on Outliers
 
   It is not always easy to distinguish whether a value is a valid part of a
   sample distribution or not. These outlier tests provides quick hints.
 
   - ST_grubbs:
     Grubbs outlier Test. Ideal for small and medium sample sizes <50.
   - ST_esd:
     Generalized Extreme Studentized Deviate (ESD) outlier test. For sample
     sizes >=25.
   - ST_nalimov: 
     Nalimov test for small and medium sample sizes (<30). Very common in 
     chemistry and quality control in Eastern Europe and Germany.
   - ST_deandixon: 
     Dean-Dixon outlier test for small sample sizes (<30). Grubbs is more
     common. 
   - ST_pearsonhartley: 
     Pearson-Hartley outlier test for bigger sample sizes (>30). ESD is more 
     common.
   - ST_outlier: 
     Basic and often used tests for medium to large sample sizes, based
     on S.D. (standard deviation) or IQR (inter-quartile range).
 
 FUNCTIONS - Distribution Tests
 
   All routines above rely on a normal distributed data. To test for normality
   a powerful test is provided.
 
   - ST_shapirowilk: 
     Shapiro-Wilk test for normality is powerful even for small
     sample sizes.
   - ST_ivplot:
     Individual value plot to examine and compare the distributions of sample 
     data. In a scatter plot, a point is plotted for the actual value of each 
     observation in a group. The spread of the distribution can be slearly seen. 
 
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 CHANGELOG:
    
    2.1.0  - Grubbs outlier test (classic and iterated) added
           - Generalized Extreme Studentized Deviate (ESD) outlier test added
             according to Rosner
           - Individual Value Plot ST_ivplot completed & extended
             - Multiple dataset capability 
             - Same or similar values are plotted beside each others
             - Tolerance threshold for similar values
             - Setting markers and colors
           - Shapiro-Wilk test extended (test statistic output added)
           - Checks for NaN and INF in the input data 
           - More demos added
           - All documentation updated 
           - Bug fixes
             - Preserve orientation of imput vectors in the output in ST_nalinv
               and ST_personhartley
             - ST_shapirowilk typos fixed
             - ST_nalimov table typo fixed (had no impact on results)
             - ST_deandixon table mapping fixed (severe bug)
 
    2.0.2  - Help-Bugfix
    
    2.0.1  - Bug and compatibility fixes related to Scilab 6

    2.0.0  - Outlier tests (Dean-Dixon, Pearson-Hartley, Nalimov) and 
             a basic test added
           - Distribution tests (Shapiro-Wilk, Skewness) added
           - Individual Value Plot added (EXPERIMENTAL)
 
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 DEPENDENCIES:
 
 apifun  >= 0.4.0
 helptbx >= 0.6

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 LITERATURE:
 
 - R. Kaiser, G. Gottschalk; "Elementare Tests zur 
   Beurteilung von Meßdaten", BI Hochschultaschenbücher, Bd. 774, Mannheim 1972.
 - Lohringer, H., "Grundlagen der Statistik", Oct, 10th, 2012, 
   http://www.statistics4u.info/
 - Shapiro, Wilk: "An Analysis of Variance Test for Normality", Biometrika, 
   Vol. 52, No. 3/4. (Dec., 1965), pp. 591-611.
 - Grubbs, F. E. (1950). Sample criteria for testing outlying observations. 
   Annals of Mathematical Statistics, 21(1), 27-58.
 - Grubbs, F. E. (1969). Procedures for detecting outlying observations in samples.
   Technometrics, 11(1), 1-21.
 - NIST/SEMATECH e-Handbook of Statistical Methods.
 - Rosner, B. (1983). Percentage Points for a Generalized ESD Many-Outlier 
   Procedure. Technometrics, 25(2), 165-172.
