Three Most useful Statistical Languages

The day to day flourishing and increased development of the industry of information technology and the programming languages in it has been challenging the programmers to choose the best and apt among the numerous languages available, to serve their customized objectives. The debates through the meet-ups and forums still continue to choose the best and appropriate statistical programming language by the programmers for statistical customized applications. Most of the discussions indicate the inclination towards the high prioritized and feature-rich statistical programming languages, SAS, R and SPSS.


SAS or Statistical Analysis System was developed by Jim Goodnight and Jim Bar from North California State University with an objective of providing the solutions towards analysis of larger amount of agricultural statistical data, through enhanced computerized statistical software. Its strong data handling capabilities enables to stand as a leading player in the commercial analytics space.

R is created by Ross Ihaka and Robert Gentleman from both University of Auckland in New Zeeland and the R foundation, in 1995. It is an implementation of the S programming language. It’s advancement in computer graphics has won fixed place in academics, research and commercial applications.

SPSS or Statistical Package for the Social Sciences is created by Normal H. Nie, dale h. Bent and Hadlai “Tex” Hull in 1968. It is easiest to learn among the three even for non-statisticians, as its interface is user-friendly that leads to penetrate in the social science applications.


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