Fuzzy statistics This monograph introduces elementary fuzzy statistics based on crisp (non fuzzy) data. Inthe 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 whichleads to a comprehensive presentation of fuzzy hypothesis testing and fuzzy regression as well as fuzzy prediction. TOC:Introduction. Fuzzy Sets. Estimate M Variance Known. Estimate M Variance Unknown. Estimate p Binomial Population. Estimate sigma2 from a Normal Population. Estimate M1 M2 Variances Known. Estimate M1 M2 Variances Unknown. Estimate d = M1 M2 Matched Pairs. Estimate p1 p2 Binomial Populations. Estimate sigma sub one squared sigma sub two squared Normal Populations. Tests on M Variance Known. Tests on M Variance Unknown. Tests on p for a Binomial Population. Tests on sigma2 Normal Population. Tests M1 vs. M2 Variances Known. Test M1 vs. M2 Variances Unknown. Test p1 = p2 Binomial Populations. Test d = M1 M2 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