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Applied Regression Models in the Social Sciences, Dudley L. Poston, Jr, Eugenia Conde, Layton M. Field


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Цена: 95040.00T
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Автор: Dudley L. Poston, Jr, Eugenia Conde, Layton M. Field
Название:  Applied Regression Models in the Social Sciences
ISBN: 9781108831024
Издательство: Cambridge Academ
Классификация:

ISBN-10: 1108831028
Обложка/Формат: Hardback
Страницы: 400
Вес: 1.31 кг.
Дата издания: 17.08.2023
Язык: English
Иллюстрации: Worked examples or exercises
Размер: 260 x 182 x 33
Читательская аудитория: General (us: trade)
Ключевые слова: Social research & statistics, SOCIAL SCIENCE / Research
Ссылка на Издательство: Link
Рейтинг:
Поставляется из: Англии
Описание: This accessible and practical textbook gives students the perfect guide to the use of regression models in testing and evaluating hypotheses dealing with social relationships. A range of statistical methods suited to a wide variety of dependent variables is explained, which will allow students to read, understand, and interpret complex statistical analyses of social data. Each chapter contains example applications using relevant statistical methods in both Stata and R, giving students direct experience of applying their knowledge. A full suite of online resources - including statistical command files, datasets and results files, homework assignments, class discussion topics, PowerPoint slides, and exam questions - supports the student to work independently with the data, and the instructor to deliver the most effective possible course. This is the ideal textbook for advanced undergraduate and beginning graduate students taking courses in applied social statistics.

Regression Analysis and Linear Models: Concepts, Applications, and Implementation

Автор: Darlington Richard B., Hayes Andrew F.
Название: Regression Analysis and Linear Models: Concepts, Applications, and Implementation
ISBN: 1462521134 ISBN-13(EAN): 9781462521135
Издательство: Taylor&Francis
Рейтинг:
Цена: 83690.00 T
Наличие на складе: Невозможна поставка.
Описание: Ephasizing conceptual understanding over mathematics, this user-friendly text introduces linear regression analysis to students and researchers across the social, behavioral, consumer, and health sciences. Coverage includes model construction and estimation, quantification and measurement of multivariate and partial associations, statistical control, group comparisons, moderation analysis, mediation and path analysis, and regression diagnostics, among other important topics. Engaging worked-through examples demonstrate each technique, accompanied by helpful advice and cautions. The use of SPSS, SAS, and STATA is emphasized, with an appendix on regression analysis using R. The companion website (www.afhayes.com) provides datasets for the book`s examples as well as the RLM macro for SPSS and SAS. Pedagogical Features: *Chapters include SPSS, SAS, or STATA code pertinent to the analyses described, with each distinctively formatted for easy identification. *An appendix documents the RLM macro, which facilitates computations for estimating and probing interactions, dominance analysis, heteroscedasticity-consistent standard errors, and linear spline regression, among other analyses. *Students are guided to practice what they learn in each chapter using datasets provided online. *Addresses topics not usually covered, such as ways to measure a variable`s importance, coding systems for representing categorical variables, causation, and myths about testing interaction.

Regression for health and social science

Автор: Zelterman, Daniel (yale University, Connecticut)
Название: Regression for health and social science
ISBN: 1108478182 ISBN-13(EAN): 9781108478182
Издательство: Cambridge Academ
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Цена: 52790.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Using every-day examples and numerous exercises, this text covers the basics of linear models with a minimum of mathematics. The emphasis is on issues involved in the analysis and the interpretation of computer output. R code is provided and explained allowing readers to apply the methods to their own data.

Applied regression models in the social sciences

Автор: Poston, Jr, Dudley L. (texas A&m University) Conde, Eugenia (university Of North Carolina, Chapel Hill) Field, Layton M. (mount St. Mary`s University)
Название: Applied regression models in the social sciences
ISBN: 1108926266 ISBN-13(EAN): 9781108926263
Издательство: Cambridge Academ
Рейтинг:
Цена: 47510.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This accessible and practical textbook gives students the perfect guide to the use of regression models in testing and evaluating hypotheses dealing with social relationships. A range of statistical methods suited to a wide variety of dependent variables is explained, which will allow students to read, understand, and interpret complex statistical analyses of social data. Each chapter contains example applications using relevant statistical methods in both Stata and R, giving students direct experience of applying their knowledge. A full suite of online resources - including statistical command files, datasets and results files, homework assignments, class discussion topics, PowerPoint slides, and exam questions - supports the student to work independently with the data, and the instructor to deliver the most effective possible course. This is the ideal textbook for advanced undergraduate and beginning graduate students taking courses in applied social statistics.

Regression Models for Categorical, Count, and Related Variables: An Applied Approach

Автор: Hoffmann John P.
Название: Regression Models for Categorical, Count, and Related Variables: An Applied Approach
ISBN: 0520289293 ISBN-13(EAN): 9780520289291
Издательство: Wiley
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Цена: 58080.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Social science and behavioral science students and researchers are often confronted with data that are categorical, count a phenomenon, or have been collected over time. This book provides an introduction and overview of several statistical models designed for these types of outcomes.

Applied Regression Analysis

Автор: Thrane Christer
Название: Applied Regression Analysis
ISBN: 1138335487 ISBN-13(EAN): 9781138335486
Издательство: Taylor&Francis
Рейтинг:
Цена: 59190.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book is an introduction to regression analysis which focuses on the practical aspects of conducting regression analysis and the real-world applications of this tool.

Applied Regression Analysis

Автор: Thrane, Christer
Название: Applied Regression Analysis
ISBN: 1138335479 ISBN-13(EAN): 9781138335479
Издательство: Taylor&Francis
Рейтинг:
Цена: 158230.00 T
Наличие на складе: Невозможна поставка.
Описание: This book is an introduction to regression analysis which focuses on the practical aspects of conducting regression analysis and the real-world applications of this tool.

Applied linear regression for longitudinal data

Автор: Tan, Frans E.s. Jolani, Shahab
Название: Applied linear regression for longitudinal data
ISBN: 0367634317 ISBN-13(EAN): 9780367634315
Издательство: Taylor&Francis
Рейтинг:
Цена: 100030.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book introduces best practices in longitudinal data analysis at intermediate level, with a minimum number of formulas without sacrificing depths. It meets the need to understand statistical concepts of longitudinal data analysis by visualizing important techniques instead of using abstract mathematical formulas.

Active Statistics: Stories, Games, Problems, and Hands-on Demonstrations for Applied Regression and Causal Inference

Автор: Aki Vehtari, Andrew Gelman
Название: Active Statistics: Stories, Games, Problems, and Hands-on Demonstrations for Applied Regression and Causal Inference
ISBN: 100943621X ISBN-13(EAN): 9781009436212
Издательство: Cambridge Academ
Рейтинг:
Цена: 21110.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides statistics instructors and students with complete classroom material for a one- or two-semester course on applied regression and causal inference. It is built around 52 stories, 52 class-participation activities, 52 hands-on computer demonstrations, and 52 discussion problems that allow instructors and students to explore in a fun way the real-world complexity of the subject. The book fosters an engaging 'flipped classroom' environment with a focus on visualization and understanding. The book provides instructors with frameworks for self-study or for structuring the course, along with tips for maintaining student engagement at all levels, and practice exam questions to help guide learning. Designed to accompany the authors' previous textbook Regression and Other Stories, its modular nature and wealth of material allow this book to be adapted to different courses and texts or be used by learners as a hands-on workbook.

Spatial regression models for the social sciences

Автор: Chi, Guangqing Zhu, Jun
Название: Spatial regression models for the social sciences
ISBN: 154430207X ISBN-13(EAN): 9781544302072
Издательство: Sage Publications
Рейтинг:
Цена: 88710.00 T
Наличие на складе: Поставка под заказ.
Описание: Focusing on the methods that are commonly used by social scientists, this text introduces the regression methods for analysing spatial data.

Mixed-Effects Regression Models in Linguistics

Автор: Dirk Speelman; Kris Heylen; Dirk Geeraerts
Название: Mixed-Effects Regression Models in Linguistics
ISBN: 3319888501 ISBN-13(EAN): 9783319888507
Издательство: Springer
Рейтинг:
Цена: 121110.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:

When data consist of grouped observations or clusters, and there is a risk that measurements within the same group are not independent, group-specific random effects can be added to a regression model in order to account for such within-group associations. Regression models that contain such group-specific random effects are called mixed-effects regression models, or simply mixed models. Mixed models are a versatile tool that can handle both balanced and unbalanced datasets and that can also be applied when several layers of grouping are present in the data; these layers can either be nested or crossed. In linguistics, as in many other fields, the use of mixed models has gained ground rapidly over the last decade. This methodological evolution enables us to build more sophisticated and arguably more realistic models, but, due to its technical complexity, also introduces new challenges. This volume brings together a number of promising new evolutions in the use of mixed models in linguistics, but also addresses a number of common complications, misunderstandings, and pitfalls. Topics that are covered include the use of huge datasets, dealing with non-linear relations, issues of cross-validation, and issues of model selection and complex random structures. The volume features examples from various subfields in linguistics. The book also provides R code for a wide range of analyses.

Data analysis using regression and multilevel/hierarchical models

Автор: Gelman
Название: Data analysis using regression and multilevel/hierarchical models
ISBN: 052168689X ISBN-13(EAN): 9780521686891
Издательство: Cambridge Academ
Рейтинг:
Цена: 57030.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Data Analysis Using Regression and Multilevel/Hierarchical Models, first published in 2007, is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. It introduces and demonstrates a variety of models and instructs the reader in how to fit these models using freely available software packages.

Mixed-Effects Regression Models in Linguistics

Автор: Dirk Speelman; Kris Heylen; Dirk Geeraerts
Название: Mixed-Effects Regression Models in Linguistics
ISBN: 3319698281 ISBN-13(EAN): 9783319698281
Издательство: Springer
Рейтинг:
Цена: 93160.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: When data consists of grouped observations or clusters, and there is a risk that measurements within the same groups are not independent, group-specific random effects can be added to a regression model in order to account for such within-group associations.


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