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Item #:
62643
An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
Item #:
62643
TZS 215128
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An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets.
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What Stands Out
Comprehensive Learning
Offers a thorough understanding of statistical learning concepts, making it accessible for beginners and valuable for advanced learners seeking practical applications.
R Application Focus
Incorporates hands-on examples using R, providing readers with the tools to apply statistical methods in real-world scenarios, enhancing both learning and practical skills.
Clear Explanations
Uses straightforward language and illustrative graphics to simplify complex topics, ensuring readers can easily grasp concepts and effectively apply them in various fields.
Product Details
- Accessible overview of statistical learning
- Covers modeling and prediction techniques
- Illustrates methods with color graphics and real-world examples
- Includes tutorials on implementing analyses in R
- Suitable for statisticians and non-statisticians
- Assumes only previous linear regression course knowledge
| Publisher | SPRINGER |
| Publication date | January 1, 2013 |
| Edition | 1st |
| Language | English |
| Print length | 426 pages |
| ISBN-10 | 1461471370 |
| ISBN-13 | 978-1461471370 |
| Item Weight | 2.31 pounds (1.05 kg) |
| Dimensions | 6.1 x 1 x 9.3 inches (15.5 x 2.5 x 23.6 cm) |
| Part of series | Springer Texts in Statistics |
Who Should Buy?
-
Students of Statistics
Ideal for undergraduate and graduate students seeking foundational knowledge in statistical learning techniques and R applications.
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Data Analysts
Beneficial for data analysts looking to enhance their understanding of statistical methods and R for data analysis.
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Researchers in R
Useful for researchers who need to apply statistical learning methods in R for their data-driven research projects.
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Beginners in Statistics
Not suitable for complete beginners, as it assumes a basic understanding of statistics and familiarity with R.
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Non-technical Users
May frustrate non-technical users unfamiliar with programming or statistical methodologies presented through R.
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Advanced Statisticians
Advanced statisticians may find the content too basic and not sufficiently challenging or comprehensive for their expertise.
Product Description
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Customer Questions & Answers
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Question:
What is 'An Introduction to Statistical Learning: with Applications in R' about?
Answer: This book serves as a comprehensive introduction to statistical learning, covering essential techniques such as linear regression, classification, and resampling methods. It uses R, a powerful statistical programming language, to demonstrate applications in real-world scenarios. Ideal for beginners and professionals, this text emphasizes practical applications alongside theoretical foundations. Readers will benefit from clear explanations, abundant examples, and hands-on exercises that enhance understanding and proficiency in statistical analysis. -
Question:
Who is the target audience for this book?
Answer: The book is intended for a diverse audience, including graduate students, researchers, and professionals in fields such as statistics, data science, and machine learning. It serves as an introductory text for those new to statistical learning, as well as a reference for seasoned practitioners seeking to refresh their knowledge. Its practical approach, combined with accessible R applications, makes it suitable for anyone interested in mastering statistical data analysis techniques. -
Question:
What programming language does this book focus on for practical applications?
Answer: This book utilizes R, a widely-used programming language and environment specifically designed for statistical computing and graphics. R provides an extensive array of tools for data manipulation, statistical modeling, and visualization, making it ideal for implementing the statistical learning techniques discussed. Readers will find numerous code examples and practical exercises that help cement their understanding of statistical concepts while applying them in R. -
Question:
Does the book include real-world applications of statistical methods?
Answer: Yes, the book features numerous real-world applications that illustrate how statistical methods can be employed in various fields such as finance, healthcare, and social sciences. By integrating practical case studies alongside theoretical teachings, the authors ensure that readers can see the relevance of statistical learning in everyday life. This hands-on approach helps learners connect abstract concepts to tangible outcomes, thereby enhancing their analytical skills. -
Question:
What types of statistical methods are covered in this book?
Answer: The book covers a broad spectrum of statistical methods, including linear regression, classification techniques, clustering, and more advanced topics like tree-based methods and support vector machines. Each method is explained in terms of its theoretical underpinnings and practical implementation in R. This comprehensive coverage makes the book a valuable resource for anyone looking to deepen their understanding of statistical learning methodologies. -
Question:
Are there exercises included for practice?
Answer: Absolutely! Each chapter includes exercises designed to reinforce the concepts presented and to encourage hands-on practice with R. These exercises provide opportunities to apply the statistical techniques learned, helping to solidify the reader's understanding. Furthermore, solving these practical problems equips readers with the skills necessary to tackle real-world data analysis challenges. -
Question:
Can beginners without a strong statistical background benefit from this book?
Answer: Yes, beginners will find this book approachable, as it starts with fundamental concepts and gradually builds up to more complex topics. The authors take a pedagogical approach, using clear language and illustrative examples to make complicated ideas accessible. Moreover, the emphasis on practical applications using R offers beginners a straightforward pathway to understanding and applying statistical learning techniques. -
Question:
Is 'An Introduction to Statistical Learning' suitable for self-study?
Answer: Yes, the book is well-suited for self-study, as it is structured in a way that allows readers to progressively work through the material at their own pace. Each chapter introduces key concepts, followed by practical R applications and exercises, making it an effective resource for independent learners. Those who prefer self-learning will benefit from the engaging examples and clear explanations that facilitate understanding of the content. -
Question:
What is the significance of using R in statistical learning?
Answer: Using R enhances the learning experience by allowing readers to implement statistical techniques directly, enabling hands-on engagement with the material. R offers a rich ecosystem of packages and tools specifically designed for data analysis, making it easier to explore, visualize, and model data. This practical approach equips readers with the skills to analyze datasets, derive insights, and apply statistical methods in their respective fields effectively. -
Question:
Where can I buy 'An Introduction to Statistical Learning: with Applications in R' in Tanzania?
Answer: You can purchase 'An Introduction to Statistical Learning: with Applications in R' from Ubuy. Ubuy is an established e-commerce platform that offers a vast range of products, including academic texts. Shopping with Ubuy ensures you have access to reliable service and a straightforward purchasing process for your educational needs.
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TZS 215128
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Features & Benefits
- Accessible overview of statistical learning.
- Important modeling and prediction techniques are presented.
- Real-world examples are used to illustrate the methods.
- Each chapter contains a tutorial on implementing the analyses and methods presented in R.
- Targeted to statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data.
- Assumes only a previous course in linear regression and no knowledge of matrix algebra.
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