β Scribed by Hildebrand D., Laing J.D., Rosenthal H.
No coin nor oath required. For personal study only.
Ordinal data can be rank ordered but not assumed to have equal distances between categories. Using support by judges for civil rights measures and bussing as the primary example, this paper indicates how such data can best be analyzed.
π SIMILAR VOLUMES
Ordinal data can be rank ordered but not assumed to have equal distances between categories. Using support by judges for civil rights measures and bussing as the primary example, this paper indicates how such data can best be analyzed.
Statistical scienceβs first coordinated manual of methods for analyzing ordered categorical data, now fully revised and updated, continues to present applications and case studies in fields as diverse as sociology, public health, ecology, marketing, and pharmacy. <i>Analysis of Ordinal Categorical D
Data is constantly increasing and data analysts are in higher demand than ever. This book is an essential guide to the role of data analyst. Aspiring data analysts will discover what data analysts do all day, what skills they will need for the role, and what regulations they will be required to adhe
Ordinal Data Modeling is a comprehensive treatment of ordinal data models from both likelihood and Bayesian perspectives. A unique feature of this text is its emphasis on applications. All models developed in the book are motivated by real datasets, and considerable attention is devoted to the descr