White Papers

On the Reliability of Microsoft Excel XP for Statistical Purposes

Overview The paper deals with the reliability of Microsoft Excel XP for statistical purposes. In this section they show that Excel can compute negative variances. In next section they see that the random numbers generated by Excel cannot suffice scientific statistical requirements, and in last section they have to report that unbearable errors with the computation of statistical distributions that have been reported to Microsoft and later on have been published (Knusel, 1998) are still to be found in Microsoft Excel XP. So one has again to warn the scientific community against using Microsoft Excel for statistical purposes.

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Publisher
University of Munich
File Format
PDF
Date Published
Oct 1, 2008
Format
White Papers
Topics
Spreadsheets, IT Reliability

University of Munich White Papers

Dynamic Duopoly Competition With Switching Costs and Network Externalities

Dynamic Duopoly Competition With Switching Costs and Network Externalities

This paper analyzes competition in a two-period differentiated-products duopoly in the presence of both switching costs

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A Research Note on Virtual Project Management Systems

A Research Note on Virtual Project Management Systems

The objective of this paper is to report intermediate research results on the comparison and benchmarking of two tools t

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Extending UML to Model Navigation and Presentation in Web Applications

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This paper presents a UML Profile for Web applications. It is a UML extension based on the general extension mechanisms

Publisher: University of Munich  |  Tags: applications

A Framework for Service Quality Assurance Using Event Correlation Techniques

A Framework for Service Quality Assurance Using Event Correlation Techniques

Due to the increasing use of service level agreements for the provisioning of IT services, providers have to ensure that

Publisher: University of Munich  |  Tags: infrastructure, itil, management, service management

Information Visualization and Visual Data Mining

Information Visualization and Visual Data Mining

Never before in history data has been generated at such high volumes as it is recently. Exploring and analyzing the vast

Publisher: University of Munich  |  Tags: data, data mining