An expert system on selection of appropriate nonparametric statistical tests
Date of Publication
1996
Document Type
Master's Thesis
Degree Name
Master in Computer Application
Subject Categories
Computer Sciences
College
College of Computer Studies
Department/Unit
Information Technology
Thesis Adviser
Arnulfo Azcarraga
Defense Panel Chair
Peter Fernandez
Defense Panel Member
Philip Chan
Kelsey Hartigan Go
Abstract/Summary
The Nonparametric Statistical Expert System (NOSES) is designed to assist users in selecting appropriate nonparametric statistical tests for analyzing data. The system consists of two (2) components. The first component involves utilization of pure raw data provided by users and other information, namely, variable measured by the data, unit of measurement, and the nonparametric statistical problem being addressed. Raw data supplied are processed to generate parameters or characteristics of data, including number of samples, number of observations, sum, and mean. The second component entails the process of recommending appropriate nonparametric statistical test(s) on the basis of the parameters produced and other information supplied by users. The system also provides a description of each nonparametric statistical test included in the study and a glossary of relevant statistical terms.
Abstract Format
html
Format
Accession Number
TG02547
Shelf Location
Archives, The Learning Commons, 12F Henry Sy Sr. Hall
Physical Description
46, [131] leaves; 28 cm.
Keywords
Expert systems (Computer science); Nonparametric statistics; Electronic data processing; Artificial intelligence; Statistics -- Computer programs
Recommended Citation
Sipin, G. C. (1996). An expert system on selection of appropriate nonparametric statistical tests. Retrieved from https://animorepository.dlsu.edu.ph/etd_masteral/1758