Premium
A DECISION ANALYSIS APPROACH TO BUSINESS STATISTICS
Author(s) -
Brabb George J.,
Livingston E. Jeffery
Publication year - 1976
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.1540-5915.1976.tb00699.x
Subject(s) - computer science , statistical inference , context (archaeology) , business statistics , decision analysis , inference , bayesian probability , data science , bayesian inference , subject (documents) , descriptive statistics , business decision mapping , bayesian statistics , decision support system , management science , artificial intelligence , statistics , mathematics , engineering , paleontology , library science , biology
The general area of business statistics includes the coverage of three distinct subject areas: descriptive statistics, classical inference, and the more recently developed Bayesian decision procedures. Attempts at teaching these subjects has often produced confusion and a lack of confidence in statistical tools for many students. This article describes a unique way of teaching and presenting these three areas so that a student is given a framework for organizing and applying the different subject areas. The vehicle used to gain this framework is a simple decision model in which the various stages of the model provide excellent vantage points for introducing statistical tools. The key to the approach is to keep a student in a decision‐making environment. The tools and methods of the three areas are only introduced as they aid the analysis of the decision. This immediate application in a decision context motivates a student to learn the tool and visualize its use.