DESCRIPTION

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Toolbox: samplestat

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Title: SampleSTAT

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Summary: SampleSTAT is a tiny toolbox for statistics of normal distributed univariate data

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Version: 2.0.2

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Author: Hani A. Ibrahim

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Maintainer: Hani A. Ibrahim (hani.ibrahim@gmx.de)
 
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Category: Data Analysis - Statistics

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Entity: private individual

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WebSite: https://github.com/haniibrahim/scilab-samplestat

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License: GPL 3.0

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ScilabVersion: >= 5.5

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Depends: >= apifun 0.4.0

// Creation date
Date: 2020-0

Description: Toolbox for statistics of normal distributed univariate samples.
 
 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 to test the data for normality. 
 
 SampleSTAT is compatible to Scilab 5.5.x and 6.0.x.
 
 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) 
   * ST_trustarea: 
     Calculates the trust area (range of dispersion of the mean or S.D. of the mean)
   * ST_studentfactor: 
     Determines the student factor for an amount of numbers, service 
	 function for ST_staryarea and ST_trustarea
   * samplestat.dem: 
     Demo for mean evaluation via "ST_strayarea()" 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_deandixon: 
     Dean-Dixon outlier test for small sample sizes (<30) 
   * ST_pearsonhartley: 
     Pearson-Hartley outlier test for bigger sample sizes (>30) 
   * ST_nalimov: 
     Nalimov test for small and larger sample sizes
   * 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:
     Very basic individual value plot (EXPERIMENTAL) 
         
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 CHANGELOG:
 	
    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

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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.