"Applied Regression Modeling" - второе издание книги, которая рассказывает о применении статистических методов, в частности методов регрессионного анализа и моделирования, для анализа и интерпретации многомерных данных в бизнесе, науке и социальных науках. Автор использует множество примеров из реальной жизни, кейсов, иллюстраций и графиков, чтобы познакомить читателей с миром регрессионного анализа, используя различные программные пакеты, включая R, SPSS, Minitab, SAS, JMP и S-PLUS. Книга включает расширенные техники моделирования, такие как логистическая регрессия, регрессия Пуассона, модели выбора с дискретными вариантами, многоуровневые модели и байесовское моделирование.
Второе издание книги содержит уточнение и расширение сложных тем, таких как преобразования, индикаторные переменные, тестирование предположений модели, нестационарная дисперсия, автокорреляция, методы выбора переменных и построения модели и графическая интерпретация. Все данные и примеры в книге обновлены, а в конце каждой главы включены дополнительные задачи, позволяющие читателям проверить свое понимание материала.
Книга предназначена для студентов статистического анализа на уровне магистратуры, а также для профессионалов и исследователей, которые используют статистические методы для принятия решений в своей работе. Она имеет интуитивно понятный подход, не перегруженный математическими деталями. В дополнение к книге, на сайте книги представлены наборы данных, презентационные слайды, подробные инструкции по статистическому программному обеспечению и дополнительные обучающие материалы, включая дополнительные задачи и обучающие видео.
If you are new to this book then here’s a quick summary: "Applied Regression Modeling," by Iain Pardoe, fully revised to highlight latest methodologies, remains a must-have resource for those interested in regression analysis, specifically in business and social science. Using a wealth of real-world examples and contemporary datasets, this is the ideal cookbook for seasoned students, as well as beginners in the field. Clear and concise, Pardoe’s Second Edition uses rich illustrations and graphs to illustrate various methods of regression, from logistic and Poisson regressions to multilevel and Bayesian models. Specifically pertinent topics have been expanded, including interaction terms, testing model assumptions, non-constant variances, and variable selection. This authoritative and relaxing read will be a handy reference for any regression enthusiast, whether undergraduate, graduate, data scientist, biostatistician or STEM educator in need of contemporary, practical application expertise.
Revised to incorporate recent approaches and forthcoming developments, this book shows why regression modeling remains of paramount importance in analyzing multivariate data. Whether you are an undergraduate student, a graduate student, or professional in business, social science, or the natural sciences, you will find Applied Regression Modeling by Iain Pardoe to be a useful guide throughout your expertise journey.
From simple regression through advanced topics as diverse as Bayesian prediction and model assessment, this authoritative resource elucidates the major concepts underpinning regression analyses in plain English while providing integral insights into their applications within academic spheres as well as routine surveying. Numerous real-world examples tuning the way to enhance proficiency in applying these vital analytical processes make this resource, by far, among the best books to familiarize beginners with regression. Like many notable texts, it provides R code to understand the theory in practice. Finally, the set of datasets utilized to illustrate the approach have been attractively updated with an integrated data website for future assignments and exercises.
Overall, overwhelmed by learning about regression models, those preparing for their first formal lesson in regression at any level- from elementary statistics in graduate school to MBA programs- will surely embrace this read. This book makes life-changing.
Электронная Книга «Applied Regression Modeling» написана автором Iain Pardoe в году.
Минимальный возраст читателя: 0
Язык: Английский
ISBN: 9781118345023
Описание книги от Iain Pardoe
Praise for the First Edition «The attention to detail is impressive. The book is very well written and the author is extremely careful with his descriptions . . . the examples are wonderful.» —The American Statistician Fully revised to reflect the latest methodologies and emerging applications, Applied Regression Modeling, Second Edition continues to highlight the benefits of statistical methods, specifically regression analysis and modeling, for understanding, analyzing, and interpreting multivariate data in business, science, and social science applications. The author utilizes a bounty of real-life examples, case studies, illustrations, and graphics to introduce readers to the world of regression analysis using various software packages, including R, SPSS, Minitab, SAS, JMP, and S-PLUS. In a clear and careful writing style, the book introduces modeling extensions that illustrate more advanced regression techniques, including logistic regression, Poisson regression, discrete choice models, multilevel models, and Bayesian modeling. In addition, the Second Edition features clarification and expansion of challenging topics, such as: Transformations, indicator variables, and interaction Testing model assumptions Nonconstant variance Autocorrelation Variable selection methods Model building and graphical interpretation Throughout the book, datasets and examples have been updated and additional problems are included at the end of each chapter, allowing readers to test their comprehension of the presented material. In addition, a related website features the book's datasets, presentation slides, detailed statistical software instructions, and learning resources including additional problems and instructional videos. With an intuitive approach that is not heavy on mathematical detail, Applied Regression Modeling, Second Edition is an excellent book for courses on statistical regression analysis at the upper-undergraduate and graduate level. The book also serves as a valuable resource for professionals and researchers who utilize statistical methods for decision-making in their everyday work.
