Gökhan Aydinli



Gökhan Aydinli, Wolfgang Härdle, and Erich Neuwirth (2003), Computational Statistics with Spreadsheets Towards Effiency, Reproducibility and Security, Computing Science and Statistics, 35, I2003Proceedings/AydinliGoekhan/AydinliGoekhan.paper.pdf ,



Efficient and Secure Statistical Computing in Office Applications
Gökhan Aydinli, (CASE, Humboldt-Universität zu Berlin), aydinli@wiwi.hu-berlin.de,
Wolfgang Härdle, (CASE, Humboldt-Universität zu Berlin), haerdle@wiwi.hu-berlin.de, and
Erich Neuwirth, (Department of Statistics, Universität Wien), erich.neuwirth@univie.ac.at

Abstract

'Let's not kid ourselves: the most widely used piece of software for statistics is Excel.'

This quote of B.D. Ripley quite soberly describes the state of demand for statistical software nowadays. Not only students of economics, management science and related fields but particularly the industry asks for intuitive, efficient and secure software for statistical data analysis. This applies especially but not exclusively to the financial sector, which heavily relies on the ability to apply statistical methods in a distributed environment. But not for the sake of high implementation costs and the overhead of a steep learning curve.

The use of electronic spreadsheets as the primary software tool for teaching management science modeling techniques and quantitative methods in economics and finance undoubtedly played a key role in the increasing impact of quantitative lectures given in graduate programs. Researchers suggest that the ability to extract data from various sources and embed analytical decision models within larger systems are two of the most valuable skills for business students entering today's IT dominated workplace.

In this paper we will try to contribute to this evolution and furthermore want to argue in favor of spreadsheet applications as appropriate interface solution to matrix oriented statistical languages. We provide the addins MD*ReX and RExcel, two statistical environments embedded in Excel via (D)COM clients, based on the XploRe client /server architecture and on R as a numerical-statistical


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