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Introducing Discriminant Analysis to the Business Statistics Curriculum *
Author(s) -
Ragsdale Cliff T.,
Stam Antonie
Publication year - 1992
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1992.tb00414.x
Subject(s) - business statistics , linear discriminant analysis , computer science , statistics education , curriculum , descriptive statistics , key (lock) , statistics , management science , data science , mathematics , sociology , artificial intelligence , economics , pedagogy , computer security
Recently a good deal of interest and effort has been directed toward making statistics courses more effective in business schools. It is believed that a key to success in this area involves giving a more prominent role to statistical tools which are useful in actual business practice. If the research literature is any indication, discriminant analysis (DA) has many potential applications in virtually all areas of business. Yet, DA is rarely taught in undergraduate business and/or M.B.A. statistics courses. This is partially due to the fact that most presentations of DA are relegated to multivariate statistics texts that assume an advanced knowledge of linear algebra. This paper attempts to rectify this situation by proposing a simplified pedagogical approach for introducing linear DA in undergraduate and/or M.B.A. business statistics courses.