Synopses & Reviews
This monograph introduces elementary fuzzy statistics based on crisp (non-fuzzy) data. In the introductory chapters the book presents a very readable survey of fuzzy sets including fuzzy arithmetic and fuzzy functions. The book develops fuzzy estimation and demonstrates the construction of fuzzy estimators for various important and special cases of variance, mean and distribution functions. It is shown how to use fuzzy estimators in hypothesis testing and regression, which leads to a comprehensive presentation of fuzzy hypothesis testing and fuzzy regression as well as fuzzy prediction.
Table of Contents
Introduction.- Fuzzy Sets.- Estimate
µ, Variance Known.- Estimate
µ, Variance Unknown.- Estimate
p, Binomial Population.- Estimate
sigma² from a Normal Population.- Estimate
µ1 - µ2, Variances Known.- Estimate
µ1 - µ2, Variances Unknown.- Estimate
d = µ1 - µ2, Matched Pairs.- Estimate
p1 - p2, Binomial Populations.- Estimate
sigma sub one squared/sigma sub two squared, Normal Populations.- Tests on
µ, Variance Known.- Tests on
µ, Variance Unknown.- Tests on
p for a Binomial Population.- Tests on
sigma2, Normal Population.- Tests
µ1 vs.
µ2, Variances Known.- Test
µ1 vs.
µ2, Variances Unknown.- Test
p1 = p2, Binomial Populations.- Test
d = µ1 - µ2, Matched Pairs.- Test
sigma sub one squared vs.
sigma sub two squared, Normal Populations.- Fuzzy Correlation.- Estimation in Simple Linear Regression.- Fuzzy Prediction in Linear Regression.- Hypothesis Testing in Regression.- Estimation in Multiple Regression.- Fuzzy Prediction in Regression.- Hypothesis Testing in Regression.- Summary and Questions.- Maple Commands.