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An imaginative introduction to statistics, reorienting the course towards an understanding of statistical thinking and its meaning and use in daily life and work. Gudmund Iversen and Mary Gergen bring their years of experience and insight into teaching the subject, incorporating such innovations and insights as a sustained emphasis on the process of statistical analysis and what statistics can and cannot do as well as careful exposition of the ideas of developing statistical and graphical literacy. In the spirit of contemporary pedagogy and by using technology, the authors break down the traditional barriers of statistical formulas and lengthy computations encountered by students without strong quantitative skills. Further, formulas are grouped at the end of each chapter along with related problems, and, with only algebra as a prerequisite, the book is ideal for students in the liberal arts and the behavioural and social sciences.
Bringing to life the most widely used quantitative measurements and statistical techniques in marketing, this book is packed with user-friendly descriptions, examples and study applications. The process of making marketing decisions is frequently dependent on quantitative analysis and the use of specific statistical tools and techniques which can be tailored and adapted to solve particular marketing problems. Any student hoping to enter the world of marketing will need to show that they understand and have mastered these techniques. A bank of downloadable data sets to compliment the tables provided in the textbook are provided free for you here
"This book provides an outstanding introduction to. . . using association models developed primarily by Leo Goodman. . . . This well-written book provides a careful and generally clear introduction to association models. . . . the authors have achieved their aims well. They make a strong case for the usefulness of association models in a variety of applications. Clogg. . . and Shihadeh have provided sociologists with an introduction filled with wise advice about analyzing associations between ordinal variables." --Alan Agresti in Contemporary Sociology "This is a very useful book about. . . statistical models for ordinal variables. Reading this book. . . your reviewer was pleased to find a clear and succinct account explaining a variety of association models. . . . These models are the 'RC' models. . . . it is to statistical methods for the social sciences that this book. . . is aimed. . . . This is not a total beginner's book, however. . . and I thought the pace a little faster than leisurely. . . . a fine resource of clear description and explanation of the use of statistical models for ordinal data. . . ." --M. C. Jones in Journal of the Royal Statistical Society "This book is worthwhile reading for statisticians who have scattered training in ordinal data analysis and want to pull this training into a coherent overview. It is a fine supplement to other more mathematical books in the area. . . . After reading the book, the reader will have a clear understanding of the role of odds ratios in ordinal data analysis." --Technometrics "Includes a concise but clear review of criteria for assessing goodness-of-fit. . . . I found this volume an accessible unification of work in the area. I recommend it." --International Statistical Institute How should data involving response variables of many ordered categories be analyzed? What technique is the most useful in analyzing partially ordered variables regarded as dependent variables? Addressing these and other related concerns in social and survey research, this book carefully explores the statistical analysis of data involving dependent variables that can be coded into discrete, ordered categories. Through an analysis of ordinal variables, the authors cover the general procedures for assessing goodness-of-fit, review the independence model and the saturated model, define measures of association, demonstrate the logit version of the model and the jackknife method for contingency tables, and explain associated models for two-way tables as well as logit-type regression models.
Statistical science as organized in formal academic departments is relatively new. With a few exceptions, most Statistics and Biostatistics departments have been created within the past 60 years. This book consists of a set of memoirs, one for each department in the U.S. created by the mid-1960s. The memoirs describe key aspects of the department’s history -- its founding, its growth, key people in its development, success stories (such as major research accomplishments) and the occasional failure story, PhD graduates who have had a significant impact, its impact on statistical education, and a summary of where the department stands today and its vision for the future. Read here all about how departments such as at Berkeley, Chicago, Harvard, and Stanford started and how they got to where they are today. The book should also be of interests to scholars in the field of disciplinary history.
Offering a clear set of workable examples with data and explanations, Interaction Effects in Linear and Generalized Linear Models is a comprehensive and accessible text that provides a unified approach to interpreting interaction effects. The book develops the statistical basis for the general principles of interpretive tools and applies them to a variety of examples, introduces the ICALC Toolkit for Stata (downloadable from the Robert L. Kaufman’s website), and offers a series of start-to-finish application examples to show students how to interpret interaction effects for a variety of different techniques of analysis, beginning with OLS regression. The data sets and the Stata code to reproduce the results of the application examples are available online.
A unified treatment of the most useful models for categorical and limited dependent variables (CLDVs) is provided in this book. Throughout, the links among the models are made explicit, and common methods of derivation, interpretation and testing are applied. In addition, the author explains how models relate to linear regression models whenever possible.

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