Case Studies in Business, Industry and Government Statistics
News
Call for Papers
CSBIGS Volume 2(2)
CSBIGS Volume 2(1) is available!
The Case Studies in Business, Industry and Government Statistics (CSBIGS) journal welcomes submissions for cases to be considered for Volume 2 Number 2, expected to appear in November 2008.
Aims and Scope
The main objective of Case Studies in Business, Industry and Government Statistics - CSBIGS is to publish high-quality case studies in modern data analysis ready to use for instruction, training or self-study. The case studies will consist of an innovative and interesting well-written presentation of novel statistical techniques applied to known data or of known statistical techniques applied to novel data. The journal is designed to be of interest to anyone wishing to teach or learn modern data analysis - in academic as well as business, industry or government environments.
The peer-reviewed journal will serve as a forum for writers of data analysis case studies from any environment where statistical analysis is used. As such the journal will both foster a better communication between these different environments and help improve statistical training overall.
In particular, CSBIGS encourages consultants to submit their work. In this manner, the commissioning party will receive peer reviewed research, ensuring that the quality of the statistical work commissioned is of the latest international scientific standard. If the commissioning party so wishes, the research published will be rewritten by authors to preserve the privacy and anonymity of the commissioning party. To ensure confidentiality, the Editorial board will sign a non disclosure statement if requested (in cases where the true identity of the commissioning party is known to the Editorial Board).
The journal will seek to achieve a balance between originality and accessibility in its case studies. In order to facilitate usability, each article will identify its intended audience and will make available to readers in electronic form a data set - which can be a subset of that used in the case study, masked if necessary for confidentiality - to be used to apply the methods presented in the article. The case studies will also give sufficient instructions about the software needed for readers to run the analyses.
Dominique Haughton, editor-in-chief
Bentley College
Christine Thomas-Agnan, co-editor
Universite Toulouse I
Kai W. Ng, co-editor
University of Hong Kong
Editorial assistants
Guangying Hua, Maria Skaletsky, Guillaume Weisang
Bentley College
