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Generalized Additive Models: An Introduction with R, Second Edition (Chapman & Hall/CRC Texts in Statistical Science)
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The first edition of this book has established itself as one of the leading references on generalized additive models (GAMs), and the only book on the topic to be introductory in nature with a wealth of practical examples and software implementation.
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產品詳情
- The first edition of this book has established itself as one of the leading references on generalized additive models (GAMs), and the only book on the topic to be introductory in nature with a wealth of practical examples and software implementation. It is self-contained, providing the necessary background in linear models, linear mixed models, and generalized linear models (GLMs), before presenting a balanced treatment of the theory and applications of GAMs and related models. The author bases his approach on a framework of penalized regression splines, and while firmly focused on the practical aspects of GAMs, discussions include fairly full explanations of the theory underlying the methods. Use of R software helps explain the theory and illustrates the practical application of the methodology. Each chapter contains an extensive set of exercises, with solutions in an appendix or in the book’s R data package gamair, to enable use as a course text or for self-study.
| Publisher | Chapman and Hall/CRC |
| Publication date | 30 May 2017 |
| Edition | 2nd |
| Language | English |
| Print length | 496 pages |
| ISBN-10 | 1498728332 |
| ISBN-13 | 978-1498728331 |
| Item weight | 1.05 kg |
| Dimensions | 15.6 x 2.69 x 23.39 cm |
Who Should Buy?
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Data Scientists
Ideal for data scientists seeking to use generalized additive models for complex data visualization and analysis.
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Graduate Students
Beneficial for graduate students studying statistics or data science looking to deepen their understanding of modeling techniques.
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Statisticians
Great for professional statisticians needing a comprehensive guide to generalized additive models and their applications.
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Beginners
May not be suitable for beginners lacking basic statistical knowledge or experience with R programming.
產品敘述
About This Item
Introducing the "Generalized Additive Models: An Introduction with R, Second Edition" - your comprehensive guide to mastering generalized additive models using R programming! Whether you're a beginner or an experienced statistician, this textbook is a must-have for anyone interested in delving into the world of statistical modeling and analysis in R. Packed with practical examples and step-by-step instructions, this book will equip you with the skills and knowledge necessary to confidently apply generalized additive models to your data analysis projects. With the second edition of this popular Chapman & HallCRC Texts in Statistical Science series, you'll explore the foundation and theory behind additive models, along with their implementation in the R programming language. The book offers a systematic and accessible approach, making it suitable for students, professionals, and researchers alike. Through clear explanations and real-world examples, you'll learn how to leverage the power of R packages to perform regression analysis, manipulate data, and create visually appealing graphics.
Whether you're analyzing survey data, conducting social network analysis, or predicting sales trends, this book will guide you through the entire process. Key features of "Generalized Additive Models: An Introduction with R, Second Edition": 1. Comprehensive Coverage: From the basics of statistical modeling to advanced topics like smoothing and model selection, this book covers it all. Gain a deep understanding of generalized additive models and their practical applications. 2.
Hands-on Examples: Learn by doing with numerous examples and exercises throughout the book. Apply what you've learned to real-world scenarios to solidify your understanding and develop your skills. 3. R Programming: Master the R programming language and take advantage of its powerful statistical analysis and data manipulation capabilities.
Enhance your data analysis workflow and produce high-quality visualizations with ease. 4. Practical Tips and Tricks: Benefit from the author's expertise as they share practical tips and tricks for successful model building and interpretation. Avoid common pitfalls and optimize your modeling techniques. 5.
Updated Second Edition: This revised edition includes updated content, additional examples, and revised exercises to ensure you have the most up-to-date knowledge and skills in the field of generalized additive models. Unlock the power of statistical modeling in R with the "Generalized Additive Models: An Introduction with R, Second Edition." Take your data analysis and statistical modeling skills to the next level and gain a competitive edge in your field. Order your copy today and embark on a journey of mastering regression models, statistical modeling, and more using the R programming language!.
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Higher Education Editorial Review
Generalized Additive Models An Introduction With R, Second Edition Chapman & Hall/CRC Texts In Statistical Science offers a comprehensive exploration of additive models, ideal for statisticians and data scientists seeking advanced statistical approaches. Published by Chapman and Hall/CRC on 30 May 2017, this second edition encompasses 496 pages filled with practical insights and clear R programming examples, making it easily accessible for readers. With its emphasis on clarity of explanations, many users appreciate how the text breaks down complex concepts, allowing for effective learning and application. This updated edition includes new topics and chapters that enhance the understanding of generalized additive models, which many readers find beneficial.
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優點
- Comprehensive exploration of additive models
- Practical insights with clear R examples
- Easily accessible for statisticians and data scientists
- Includes new topics and chapters
- Clear explanations of complex concepts
缺點
- Some readers may prefer a more concise format
Product Price History
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特色和優勢
- Leading reference on generalized additive models
- Introductory with practical examples
- Self-contained with necessary background in linear models, linear mixed models, and generalized linear models
- Based on a framework of penalized regression splines
- Includes extensive set of exercises with solutions
- Use of R software for practical application
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