Goodness-of-Fit-Techniques

Conveniently grouping methods by techniques, such as chi-squared and empirical distributionfunction , and also collecting methods of testing for specific famous distributions, this usefulreference is the fust comprehensive.review of the extensive literature on the subject. It surveysthe leading methods of testing fit . .. provides tables to make the tests available . .. assessesthe comparative merits of different test procedures . .. and supplies numerical examples to aidin understanding these techniques.Goodness-of-Fit Techniques shows how to apply the techniques . .. emphasizes testing for thethree major distributions, normal, exponential, and uniform . .. discusses the handling of censoreddata .. . and contains over 650 bibliographic citations that cover the field.Illustrated with tables and drawings, this volume is an ideal reference for mathematical andapplied statisticians, and biostatisticians; professionals in applied science fields, including psychologists,biometricians , physicians, and quality control and reliability engineers; advancedundergraduate- and graduate-level courses on goodness-of-fit techniques; and professional seminarsand symposia on applied statistics, quality control, and reliability.

Overview, Ralph B. D'Agostino and Michael A. Stephens Graphical Analysis, Ralph B. D'Agostino Tests of Chi-Squared Type, David S. Moore Tests Based on EDF Statistics, Michael A. Stephens Tests Based on Regression and Correlation, Michael A. Stephens Some Transformation Methods in Goodness-of-Fit, Charles P. Quesenberry Moment (Öb1, b2 ) Techniques, K. O. Bowman and L. R. Shenton Tests for the Uniform Distribution, Michael A. Stephens Tests for the Normal Distribution, Ralph B. D,Agostino Tests for the Exponential Disrtibution, Michael A. Stephens Analysis of Data from Censored Samples, John R. Michael and William R. Schucany The Analysis and Detection of Outliers, Gary L. Tietjen Appendix Table 1: Cumulative Distribution Function of the Standard Normal Distribution Table 2: Critical Values of the Chi-Square Distribution Simulated Data Sets Real Data Sets