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Toolbox for statistics of univariate normally distributed measured data
(8128 downloads for this version - 27616 downloads for all versions)
Hani A. Ibrahim
Owner Organization
private individual
Hani Ibrahim
Creation Date
May 1, 2020
Source created on
Scilab 6.0.x
Binaries available on
Scilab 5.5.x:
Windows 64-bit Windows 32-bit Linux 64-bit Linux 32-bit macOS
Scilab 6.0.x:
Windows 64-bit Windows 32-bit Linux 64-bit Linux 32-bit macOS
Scilab 6.1.x:
Windows 64-bit Windows 32-bit Linux 64-bit Linux 32-bit macOS
Scilab 2023.0.x:
Windows 64-bit Windows 32-bit Linux 64-bit Linux 32-bit macOS
Install command
--> atomsInstall("ST_2019")
            This toolbox provides elementary tests for the evaluation 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.


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
  * ST_studentfactor: 
    Determines the student factor for an amount of numbers, service 
    function for 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 provide 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 on small
	 sample sizes.  
  * ST_ivplot:
    Very basic individual value plot (EXPERIMENTAL)
2.0.2  - Help-bug fixed
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) added
       - Individual Value Plot added (EXPERIMENTAL)

* R. Kaiser, G. Gottschalk; "Elementare Tests zur 
  Beurteilung von Meßdaten", BI Hochschultaschenbücher, Bd. 774, 
  Mannheim 1972.
* Lohringer, H., "Grundlagen der Statistik", Oct, 10th, 
* Shapiro, Wilk: "An Analysis of Variance Test for Normality", 
  Biometrika, Vol. 52, No. 3/4. (Dec., 1965), pp. 591-611.            
Files (5)
[495.83 kB]
OS-independent binary for Scilab 2023.0.x

[100.98 kB]
Source code archive

[438.88 kB]
OS-independent binary for Scilab 5.5.x

[465.66 kB]
OS-independent binary for Scilab 6.0.x

[465.66 kB]
OS-independent binary for Scilab 6.1.x

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