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Total Size:
14.1 MB
Info Hash:
BC4FE6213BA0E126FB85F05A64FA0C777DD65E22
Added By:
Added:
Dec. 7, 2025, 11:31 a.m.
Stats:
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(Last updated: Dec. 7, 2025, 11:32 a.m.)
| File | Size |
|---|---|
| Rathi M. Introduction to Statistical Computing and Visualization Using R 2025.pdf | 14.1 MB |
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19.3 MB
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| Uploaded by indexFroggy | Size 19.3 MB | Health [ 34 /11 ] | Added 2023-07-01 |
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| Uploaded by indexFroggy | Size 50.5 MB | Health [ 24 /3 ] | Added 2023-11-03 |
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| Uploaded by indexFroggy | Size 18.7 MB | Health [ 33 /3 ] | Added 2023-07-01 |
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| Uploaded by krishh1337 | Size 907.3 MB | Health [ 93 /28 ] | Added 2023-06-02 |
NOTE
SOURCE: Rathi M. Introduction to Statistical Computing and Visualization Using R 2025
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COVER
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MEDIAINFO
Textbook in PDF format
The book provides a foundational guide to statistical computing and visualisation Using R programming with an emphasis on practical data analysis skills that are directly applicable to diverse fields like finance, defence, health, and education. It uniquely combines a thorough explanation of basic constructs with advanced topics such as data visualisation, statistical modeling, and probability, making it accessible yet comprehensive for learners across disciplines. This approach allows readers not only to build essential R skills but also to apply them to real-world scenarios, equipping students and professionals from various disciplines with versatile analytical tools. It offers a comprehensive yet approachable introduction for students and scholars from various disciplines using R.
Includes practical and interactive elements such as quizzes, coding exercises, and hands-on projects can provide an engaging and effective learning experience for readers.
Provides complete code solutions to every problem presented, including detailed answers to even the most complex questions.
Presents case studies that can help contextualize the concepts covered in the book by showing how they are used in specific industries, fields, or contexts.
Offers application-based practical data analysis with cases in various fields and sectors, such as finance, healthcare, and marketing.
Focuses on best practices and efficient coding techniques, improving productivity and maintainability of R code
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