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An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
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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
Produkta informācija
- 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.
PRODUKTA APRAKSTS
An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
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Probability & Statistics Editorial Review
An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) is a pivotal textbook for anyone eager to dive into statistical learning. This book stands out by being approachable and highly readable, making complex algorithms intuitive. It features numerous well-made plots that aid comprehension, which many readers find enjoyable. Particularly beneficial for undergraduate students and those with minimal statistical background, it serves as a robust introduction and even reignites interest in more rigorous texts. The authors present practical applications alongside essential theory, ensuring readers receive a holistic understanding necessary for data science, while also providing ample R coding examples to solidify learning.
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Pros
- Clear and approachable for beginners
- Engaging well-crafted visualizations
- Strong emphasis on linear regression
- Practical applications in data science
- Provides useful R coding examples
Mīnusi
- More depth could benefit advanced learners
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Funkcijas un priekšrocības
- 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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