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On New Perspectives for Statistical Computing in Business and Industry – A Solution with STATISTICA and R

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  • Weiß Christian H.

    (Darmstadt University of Technology, Department of Mathematics, Schlossgartenstraße 7, 64289 Darmstadt, Germany. E-mail: weiss@mathematik.tu-darmstadt.de)

Abstract

Companies in business and industry often make very different demands on statistical software packages, like flexibility concerning customized solutions, possibility of integrating non-standard stochastic approaches, use to validated applications, user-friendly interface, moderate costs, and many more. Usually, it is not possible to find a single software package that satisfies all such demands. Therefore, it would be attractive for entrepreneurs and statistical consultants if a collaboration among statistical software packages (e.g., leading commercial package extended by open source system) could be realized easily.In this article, we show how statistical procedures offered by R are easily integrated into the graphical user interface of STATISTICA, using the R DCOM Server of [Baier and Neuwirth, R/Scilab (D)COM Server V 3.0-1B5, 2008] and STATISTICA Visual Basic (SVB). We present solutions for different versions of STATISTICA, and illustrate all these approaches by an example from time series analysis: Using the tseries package of [Trapletti and Hornik, The tseries Package, Version 0.10-18, 2009], we tune STATISTICA with R by integrating R's ability for fitting GARCH models to given data into the user interface of STATISTICA.

Suggested Citation

  • Weiß Christian H., 2010. "On New Perspectives for Statistical Computing in Business and Industry – A Solution with STATISTICA and R," Stochastics and Quality Control, De Gruyter, vol. 25(1), pages 43-64, January.
    • Handle: RePEc:bpj:ecqcon:v:25:y:2010:i:1:p:43-64:n:5
    DOI: 10.1515/eqc.2010.004
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    References listed on IDEAS

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    1. Thomas Baier & Erich Neuwirth, 2007. "Excel :: COM :: $$\mathsf{R}$$," Computational Statistics, Springer, vol. 22(1), pages 91-108, April.
    2. Bera, Anil K & Higgins, Matthew L, 1993. "ARCH Models: Properties, Estimation and Testing," Journal of Economic Surveys, Wiley Blackwell, vol. 7(4), pages 305-366, December.
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