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## Métodos Estatísticos Para as Ciências Sociais seleção on-line livro

- Autor: Alan Agresti
- Editor: Penso
- Data de publicação: 2012-02-09
- ISBN: 8563899570
- Número de páginas: 664 pages
- Tag: metodos, estatisticos, ciencias, sociais

## Métodos Estatísticos para as Ciências Sociais seleção on-line livro

- Autor: Alan Agresti
- Editor: Penso
- Data de publicação: 2017-08-03
- Tag: metodos, estatisticos, ciencias, sociais

## Statistical Methods for the Social Sciences: Pearson New International Edition seleção on-line livro

- Autor: Alan Agresti
- Editor: Pearson
- Data de publicação: 2013-08-27
- Número de páginas: 568 pages
- Tag: statistical, methods, social, sciences, pearson, international, edition

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.

Datasets and other resources (where applicable) for this book are available here.

## Statistical Methods for the Social Sciences, Global Edition seleção on-line livro

- Autor: Alan Agresti
- Editor: Pearson
- Data de publicação: 2018-02-13
- Número de páginas: 568 pages
- Tag: statistical, methods, social, sciences, global, edition

*For courses in Statistical Methods for the Social Sciences.*

**Statistical methods applied to social sciences, made accessible to all through an emphasis on concepts**introduces statistical methods to students majoring in social science disciplines. With an emphasis on concepts and applications, this book assumes no previous knowledge of statistics and only a minimal mathematical background. It contains sufficient material for a two-semester course. The

*Statistical Methods for the Social Sciences***5th Edition**uses examples and exercises with a variety of “real data.” It includes more illustrations of statistical software for computations and takes advantage of the outstanding applets to explain key concepts, such as sampling distributions and conducting basic data analyses. It continues to downplay mathematics—often a stumbling block for students—while avoiding reliance on an overly simplistic recipe-based approach to statistics.

## Foundations of Linear and Generalized Linear Models seleção on-line livro

- Autor: Alan Agresti
- Editor: Wiley-Blackwell
- Data de publicação: 2015-04-03
- ISBN: 1118730038
- Número de páginas: 480 pages
- Tag: foundations, linear, generalized, linear, models

**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 of**Linear 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

*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.

## Analysis of Ordinal Categorical Data seleção on-line livro

- Autor: Alan Agresti
- Editor: John Wiley & Sons
- Data de publicação: 2010-04-19
- ISBN: 0470082895
- Número de páginas: 396 pages
- Tag: analysis, ordinal, categorical

*Analysis of Ordinal Categorical Data, Second Edition*provides an introduction to basic descriptive and inferential methods for categorical data, giving thorough coverage of new developments and recent methods. Special emphasis is placed on interpretation and application of methods including an integrated comparison of the available strategies for analyzing ordinal data. Practitioners of statistics in government, industry (particularly pharmaceutical), and academia will want this new edition.

## Statistics: The Art and Science of Learning from Data (4th Edition) seleção on-line livro

- Autor: Alan Agresti
- Editor: Pearson
- Data de publicação: 2016-01-13
- ISBN: 9780321997838
- Número de páginas: 816 pages
- Tag: statistics, science, learning, edition

*For courses in introductory statistics.*

**The Art and Science of Learning from Data**

** Statistics: The Art and Science of Learning from Data, Fourth Edition,** takes a conceptual approach, helping students understand what statistics is about and learning the right questions to ask when analyzing data, rather than just memorizing procedures. This book takes the ideas that have turned statistics into a central science in modern life and makes them accessible, without compromising the necessary rigor. Students will enjoy reading this book, and will stay engaged with its wide variety of real-world data in the examples and exercises.

The authors believe that it’s important for students to learn and analyze both quantitative and categorical data. As a result, the text pays greater attention to the analysis of proportions than many other introductory statistics texts. Concepts are introduced first with categorical data, and then with quantitative data.

**Also available with MyStatLab**

MyStatLab^{™} is an online homework, tutorial, and assessment program designed to work with this text to engage students and improve results. Within its structured environment, students practice what they learn, test their understanding, and pursue a personalized study plan that helps them absorb course material and understand difficult concepts. For this edition, new web apps with complementary exercises, a tightly integrated video program, and strong exercise coverage enhance student learning.

**Note:** You are purchasing a standalone product; MyLab^{™} & Mastering^{™} does not come packaged with this content. Students, if interested in purchasing this title with MyLab & Mastering, ask your instructor for the correct package ISBN and Course ID. Instructors, contact your Pearson representative for more information.

If you would like to purchase boththe physical text and MyLab & Mastering, search for:

0134101677 / 9780134101675 * Statistics Plus New MyStatLab with Pearson eText -- Access Card Package

Package consists of:

0321847997 / 9780321847997 * My StatLab Glue-in Access Card

032184839X / 9780321848390 * MyStatLab Inside Sticker for Glue-In Packages

0321997832 / 9780321997838 * Statistics: The Art and Science of Learning from Data

## Statistics: The Art and Science of Learning from Data, Global Edition seleção on-line livro

- Autor: Alan Agresti
- Editor: Pearson
- Data de publicação: 2017-01-18
- Tag: statistics, science, learning, global, edition

*For courses in introductory statistics.*

**The Art and Science of Learning from Data**

* *

** Statistics: The Art and Science of Learning from Data, Fourth Edition,** takes a conceptual approach, helping students understand what statistics is about and learning the right questions to ask when analyzing data, rather than just memorizing procedures. This book takes the ideas that have turned statistics into a central science in modern life and makes them accessible, without compromising the necessary rigor. Students will enjoy reading this book, and will stay engaged with its wide variety of real-world data in the examples and exercises.

The authors believe that it’s important for students to learn and analyze both quantitative and categorical data. As a result, the text pays greater attention to the analysis of proportions than many other introductory statistics texts. Concepts are introduced first with categorical data, and then with quantitative data.

**MyStatLab™ not included. **Students, if MyStatLab is a recommended/mandatory component of the course, please ask your instructor for the correct ISBN and course ID. MyStatLab should only be purchased when required by an instructor. Instructors, contact your Pearson representative for more information.

**MyStatLab** is an online homework, tutorial, and assessment product designed to personalize learning and improve results. With a wide range of interactive, engaging, and assignable activities, students are encouraged to actively learn and retain tough course concepts.

## An Introduction to Categorical Data Analysis seleção on-line livro

- Autor: Alan Agresti
- Editor: John Wiley & Sons
- Data de publicação: 2007-04-17
- ISBN: 0471226181
- Número de páginas: 400 pages
- Tag: introduction, categorical, analysis

**Praise for the First Edition**

"This is a superb text from which to teach categorical dataanalysis, at a variety of levels. . . [t]his book can be veryhighly recommended."

—*Short Book Reviews*

"Of great interest to potential readers is the variety of fieldsthat 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 theanalysis of categorical data."

—The Statistician

The use of statistical methods for categorical data is everincreasing in today's world. *An Introduction to Categorical DataAnalysis, Second Edition* provides an applied introduction tothe most important methods for analyzing categorical data. This newedition summarizes methods that have long played a prominent rolein data analysis, such as chi-squared tests, and also placesspecial emphasis on logistic regression and other modelingtechniques for univariate and correlated multivariate categoricalresponses.

This Second Edition features:

- Two new chapters on the methods for clustered data, with anemphasis on generalized estimating equations (GEE) and randomeffects 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 allmethods
- An entertaining historical perspective on the development ofthe methods
- Specialized methods for ordinal data, small samples,multicategory data, and matched pairs
- More than 100 analyses of real data sets and nearly 300exercises

Written in an applied, nontechnical style, the book illustratesmethods using a wide variety of real data, including medicalclinical trials, drug use by teenagers, basketball shooting,horseshoe crab mating, environmental opinions, correlates ofhappiness, and much more.

*An Introduction to Categorical Data Analysis, SecondEdition* is an invaluable tool for social, behavioral, andbiomedical scientists, as well as researchers in public health,marketing, education, biological and agricultural sciences, andindustrial quality control.

## Foundations of Linear and Generalized Linear Models (Wiley Series in Probability and Statistics) seleção on-line livro

- Autor: Alan Agresti
- Editor: Wiley
- Data de publicação: 2015-01-15
- Número de páginas: 480 pages
- Tag: foundations, linear, generalized, linear, models, wiley, series, probability, statistics

**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 of**Linear 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

*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.