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Applied Multivariate Data Analysis is an intermediate-level text ideal for students and research workers in the social and behavioural sciences.
After an introduction to data analysis and the statistical knowledge required to use the book, the authors go on to explain the analysis of complex data sets in detail. The up-to-date text includes modern techniques such as multidimensional scaling, cluster analysis, generalized linear models and structural...
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Description
Bringing together advanced statistical methods and good expository writing, The Chicago Guide to Writing about Multivariate Analysis offers much-needed help to academic researchers as well as analysts who write for general audiences. From general principles to specific examples, Jane Miller discusses how to use tables, charts, examples, and analogies to write a clear, compelling argument using multivariate results as evidence. Writers will repeatedly...
Author
Description
"Concise, complete, nontechnical--the ideal introduction to an increasingly important topic In recent years, the use of statistical methods for categorical data has increased dramatically in a variety of areas and applications. This book provides an applied introduction to the most important methods for analyzing categorical data. It summarizes methods that have long played a prominent role, such as chi-squared tests, but places special emphasis on...
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"This book is ideal for upper level undergraduate or graduate level courses on categorical data analysis taught in departments of biostatistics, statistics, epidemiology, psychology, sociology, political science, and education. A prerequisite of one year of calculus and statistics is recommended. The book has been class tested by graduate students in the department of biometry and epidemiology at the Medical University of South Carolina."--Jacket....
Author
Description
Categorical data arise often in many fields, including biometrics, economics, management, manufacturing, marketing, psychology, and sociology. This book provides an introduction to the analysis of such data. The coverage is broad, using the loglinear Poisson regression model and logistic binomial regression models as the primary engines for methodology. Topics covered include count regression models, such as Poisson, negative binomial, zero-inflated,...
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Description
This book shows how to apply statistical reasoning to real-world problems. This isn't simply memorizing formulas or using the tools in a spreadsheet. The author emphasizes the importance of clarifying questions, assumptions, and expectations, and - more importantly - knowing how to responsibly interpret the results the software generates.
Statistics are everywhere, as integral to science as they are to business, and in the popular media hundreds...
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