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The book brings together material on the analysis of limited and bounded variables that is scattered across the literature in several disciplines, and presents it in a style that is both more accessible and up-to-date. The authors provide worked examples in each chapter using real datasets from a variety of disciplines. The software used for the examples include R, SAS, and Stata. The data, software code, and detailed explanations of the example models are available on an accompanying website.
Introduces the basis of the confidence interval framework and provides the criteria for 'best' confidence intervals, along with the trade-offs between confidence and precision. This book covers topics such as the transformation principle, confidence intervals, and the relationship between confidence interval and significance testing frameworks.
Fuzzy set theory deals with sets or categories whose boundaries are blurry or, in other words, 'fuzzy.' This book presents an introduction to fuzzy set theory, focusing on its applicability to the social sciences. It provides a guide for researchers wishing to combine fuzzy set theory with standard statistical techniques and model-testing.
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