Amstat News asked three review editors to rate their topfive favorite books in the September 2003 issue. CategoricalData Analysis was among those chosen. A valuable new edition of a standard reference "A 'must-have' book for anyone expecting to do research and/orapplications in categorical data analysis." –Statistics in Medicine on Categorical Data Analysis,First Edition The use of statistical methods for categorical data hasincreased dramatically, particularly for applications in thebiomedical and social sciences. Responding to new developments inthe field as well as to the needs of a new generation ofprofessionals and students, this new edition of the classicCategorical Data Analysis offers a comprehensiveintroduction to the most important methods for categorical dataanalysis. Designed for statisticians and biostatisticians as well asscientists and graduate students practicing statistics,Categorical Data Analysis, Second Edition summarizes thelatest methods for univariate and correlated multivariatecategorical responses. Readers will find a unified generalizedlinear models approach that connects logistic regression andPoisson and negative binomial regression for discrete data withnormal regression for continuous data. Adding to the value in thenew edition is coverage of: Three new chapters on methods for repeated measurement andother forms of clustered categorical data, including marginalmodels and associated generalized estimating equations (GEE)methods, and mixed models with random effects Stronger emphasis on logistic regression modeling of binaryand multicategory data An appendix showing the use of SAS for conducting nearly allanalyses in the book Prescriptions for how ordinal variables should be treateddifferently than nominal variables Discussion of exact small-sample procedures More than 100 analyses of real data sets to illustrateapplication of the methods, and more than 600 exercises An Instructor's Manual presenting detailed solutions to allthe problems in the book is available from the Wiley editorialdepartment.

The use of statistical methods for categorical data is ever increasing in today's world. An Introduction to Categorical Data Analysis, Second Edition provides an applied introduction to the most important methods for analyzing categorical data. This new edition summarizes methods that have long played a prominent role in data analysis, such as chi-squared tests, and also places special emphasis on logistic regression and other modeling techniques for univariate and correlated multivariate categorical responses. This Second Edition features: Two new chapters on the methods for clustered data, with an emphasis on generalized estimating equations (GEE) and random effects models A unified perspective based on generalized linear models An emphasis on logistic regression modeling An appendix that demonstrates the use of SAS for all methods An entertaining historical perspective on the development of the methods Specialized methods for ordinal data, small samples, multicategory data, and matched pairs More than 100 analyses of real data sets and nearly 300 exercises Written in an applied, nontechnical style, the book illustrates methods using a wide variety of real data, including medical clinical trials, drug use by teenagers, basketball shooting, horseshoe crab mating, environmental opinions, correlates of happiness, and much more. An Introduction to Categorical Data Analysis, Second Edition is an invaluable tool for social, behavioral, and biomedical scientists, as well as researchers in public health, marketing, education, biological and agricultural sciences, and industrial quality control.

Praise for the First Edition "This is a superb text from which to teach categorical data analysis, at a variety of levels. . . [t]his book can be very highly recommended." —Short Book Reviews "Of great interest to potential readers is the variety of fields that are represented in the examples: health care, financial, government, product marketing, and sports, to name a few." —Journal of Quality Technology "Alan Agresti has written another brilliant account of the analysis of categorical data." —The Statistician The use of statistical methods for categorical data is ever increasing in today's world. An Introduction to Categorical Data Analysis, Second Edition provides an applied introduction to the most important methods for analyzing categorical data. This new edition summarizes methods that have long played a prominent role in data analysis, such as chi-squared tests, and also places special emphasis on logistic regression and other modeling techniques for univariate and correlated multivariate categorical responses. This Second Edition features: Two new chapters on the methods for clustered data, with an emphasis on generalized estimating equations (GEE) and random effects models A unified perspective based on generalized linear models An emphasis on logistic regression modeling An appendix that demonstrates the use of SAS(r) for all methods An entertaining historical perspective on the development of the methods Specialized methods for ordinal data, small samples, multicategory data, and matched pairs More than 100 analyses of real data sets and nearly 300 exercises Written in an applied, nontechnical style, the book illustrates methods using a wide variety of real data, including medical clinical trials, drug use by teenagers, basketball shooting, horseshoe crab mating, environmental opinions, correlates of happiness, and much more. An Introduction to Categorical Data Analysis, Second Edition is an invaluable tool for social, behavioral, and biomedical scientists, as well as researchers in public health, marketing, education, biological and agricultural sciences, and industrial quality control.

The book presents an introduction to statistical methods for students majoring in social science disciplines. No previous knowledge of statistics is assumed, and mathematical background is assumed to be minimal (lowest-level high-school algebra). The book contains sufficient material for a two-semester sequence of courses. Such sequences are commonly required of social science graduate students in sociology, political science, and psychology. Students in geography, anthropology, journalism, and speech also are sometimes required to take at least one statistics course.

Basic results for cross-classification tables. Cross-classification tables. Chi-squared statistics. Partitions fo chi-squared. Odds ratios. Measuring association. Longlinear models for two dimensions. Notes. Complements. Associations in multidimensional tables. Partial association. Association and interation structure. Loglinear models for three dimensions. Longlinear models for higher dimensions. Notes. Complements. Data analysis using loglimear models. Fitting models for three variables. Comparison fo models. Model-building. Interative proportional fitting. Loglimear models and ordinal information. Notes. Complements. Loglinear models for ordinal variables. Loglimear model for ordinal-ordinal tables. Loglinear model for ordinal-nominal tables. Ordinal loglinear models for higher dimensions. Interaction models. Notes. Complement. Logit models. Logistic regression. Logit models and loglinear models. Logits for an ordinal response. Notes. Complements. Logit models for an ordinal response. Logit model for ordinal-ordinal tables. Logit model for ordinal-nominal tables. Ordinal logit models for higher dimensions. Notes. Complements. Other models for ordinal variables. Log-multiplicative models. Global odds ratio models. Mean response models. Proportional hazards models. Other model-building approaches. Notes. Complements. Measures of association for ordinal variables. Concordance and discordance. Ordinal-ordinal measures of association. Ordinal-nominal measures of association. Partial association measures. Category choice for ordinal variables. Notes. Complements. Inference for ordinal measures of association. Testing for association: ordinal-ordinal tables. Testing for association: ordinal-nominal tables. Confidence intervals for measures of association. Comparing and pooling measures. Testing for partial association. Notes. Complements. Square tables with ordered categories. Models for square tables. Marginal homogeneity. A final example. Notes. Complements. Comparison of ordinal methods. Summary of methods and results. Comparison of strategies. Weighted least squares. Maximum likelihood estimation. Delta method. Computer packages for cross-classification tables. Chi-squared distribution values for various right-hand tail probabilities.

A valuable overview of the most important ideas and results in statistical modeling Written by a highly-experienced author, Foundations of Linear and Generalized Linear Models is a clear and comprehensive guide to the key concepts and results of linearstatistical models. The book presents a broad, in-depth overview of the most commonly usedstatistical models by discussing the theory underlying the models, R software applications,and examples with crafted models to elucidate key ideas and promote practical modelbuilding. The book begins by illustrating the fundamentals of linear models, such as how the model-fitting projects the data onto a model vector subspace and how orthogonal decompositions of the data yield information about the effects of explanatory variables. Subsequently, the book covers the most popular generalized linear models, which include binomial and multinomial logistic regression for categorical data, and Poisson and negative binomial loglinear models for count data. Focusing on the theoretical underpinnings of these models, Foundations ofLinear and Generalized Linear Models also features: An introduction to quasi-likelihood methods that require weaker distributional assumptions, such as generalized estimating equation methods An overview of linear mixed models and generalized linear mixed models with random effects for clustered correlated data, Bayesian modeling, and extensions to handle problematic cases such as high dimensional problems Numerous examples that use R software for all text data analyses More than 400 exercises for readers to practice and extend the theory, methods, and data analysis A supplementary website with datasets for the examples and exercises An invaluable textbook for upper-undergraduate and graduate-level students in statistics and biostatistics courses, Foundations of Linear and Generalized Linear Models is also an excellent reference for practicing statisticians and biostatisticians, as well as anyone who is interested in learning about the most important statistical models for analyzing data.

Alan Agresti and Chris Franklin have merged their research and classroom experience to develop this successful introductory statistics text. Statistics: The Art and Science of Learning from Data, Third Edition, helps students become statistically literate by encouraging them to ask and answer interesting statistical questions. It takes the ideas that have turned statistics into a central science in modern life and makes them accessible and engaging to students without compromising necessary rigor. The Third Edition has been edited for conciseness and clarity to keep students focused on the main concepts. The data-rich examples that feature intriguing human-interest topics now include topic labels to indicate which statistical topic is being applied. New learning objectives for each chapter appear in the Instructora s Edition, making it easier to plan lectures and Chapter 7 (Sampling Distributions) now incorporates simulations in addition to the mathematical formulas."

Gain the statistics skills you need for the social sciences with this accessible introductory guide Statistical Methods for the Social Sciences, 5th Edition, Global Edition, by Alan Agresti, introduces you to statistical methods used in social science disciplines with no previous knowledge of statistics necessary. With an emphasis on concepts and applications, the book requires only a minimal mathematical background, maintaining a low technical level throughout to make it accessible to beginners. The 5th edition has a strong focus on real examples to help you learn the fundamental concepts of sampling distributions, confidence intervals, and significance tests. This approach also helps you understand how to apply your learning to the real world. This edition also emphasises the interpretation of software output rather than the formulas for performing analysis, reflecting advances in statistical software - which are more frequently used by social scientists to analyse data today. Other updates include: Numerous homework exercises included in each chapter. Updated data in most exercises. New sections, such as that on maximum likelihood estimation in chapter 5 New examples ask students to use applets to help them learn the fundamental concepts of sampling distributions, confidence intervals, and significance tests. The text also relies more on applets for finding tail probabilities from distributions such as the Normal, t, and chi-squared. With a wide array of learning features and the latest available information, this text will equip you with the knowledge you need to succeed in your course - an ideal companion for students majoring in social science disciplines.

Foundations of Statistics for Data Scientists: With R and Python is designed as a textbook for a one- or two-term introduction to mathematical statistics for students training to become data scientists. It is an in-depth presentation of the topics in statistical science with which any data scientist should be familiar, including probability distributions, descriptive and inferential statistical methods, and linear modeling. The book assumes knowledge of basic calculus, so the presentation can focus on "why it works" as well as "how to do it." Compared to traditional "mathematical statistics" textbooks, however, the book has less emphasis on probability theory and more emphasis on using software to implement statistical methods and to conduct simulations to illustrate key concepts. All statistical analyses in the book use R software, with an appendix showing the same analyses with Python. Key Features: Shows the elements of statistical science that are important for students who plan to become data scientists. Includes Bayesian and regularized fitting of models (e.g., showing an example using the lasso), classification and clustering, and implementing methods with modern software (R and Python). Contains nearly 500 exercises. The book also introduces modern topics that do not normally appear in mathematical statistics texts but are highly relevant for data scientists, such as Bayesian inference, generalized linear models for non-normal responses (e.g., logistic regression and Poisson loglinear models), and regularized model fitting. The nearly 500 exercises are grouped into "Data Analysis and Applications" and "Methods and Concepts." Appendices introduce R and Python and contain solutions for odd-numbered exercises. The book's website (http://stat4ds.rwth-aachen.de/) has expanded R, Python, and Matlab appendices and all data sets from the examples and exercises.

This edition features the exact same content as the traditional book in a convenient, three-hole- punched, loose-leaf version. Books a la Carte also offer a great value-this format costs significantly less than a new textbook. Statistics: The Art and Science of Learning from Data, Third Edition, helps students become statistically literate by encouraging them to ask and answer interesting statistical questions. This book takes the ideas that have turned statistics into a central science in modern life and makes them accessible without compromising necessary rigor. Authors Alan Agresti and Christine Franklin believe that it's important for students to learn and analyze both quantitative and categorical data. As a result, the book pays greater attention to the analysis of proportions than many other introductory statistics books. Concepts are introduced first with categorical data, and then with quantitative data. The Third Edition has been edited for conciseness and clarity to keep students focused on the main concepts. The data-rich examples that feature intriguing human-interest topics now include topic labels to indicate which statistical topic is being applied. New learning objectives for each chapter appear in the Instructor's Edition, making it easier to plan lectures and Chapter 7 (Sampling Distributions) now incorporates simulations in addition to the mathematical formulas. This package contains: Books a la Carte for Statistics: The Art and Science of Learning from Data, Third Edition, plus the CD-ROM that comes with the bound version of the textbook.