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252941402
Model to Meaning: How to Interpret Statistical Models with R and Python
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Item #:
252941402
XPF 12315
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Readers will learn how to transform complex parameter estimates into quantities that are readily interpretable, intuitive, and understandable.
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Product Details
- Proposes a consistent workflow that can be applied to (almost) any statistical or machine learning model. Readers will learn how to transform complex parameter estimates into quantities that are readily interpretable, intuitive, and understandable.
| Publisher | Chapman and Hall/CRC |
| Publication date | 29 Sept. 2025 |
| Edition | 1st |
| Language | English |
| Print length | 262 pages |
| ISBN-10 | 1032908726 |
| ISBN-13 | 978-1032908724 |
| Dimensions | 15.6 x 1.5 x 23.39 cm |
Who Should Buy?
-
Aspiring Data Analysts
Great for beginners looking to understand statistical models using R and Python for data analysis and interpretation.
-
Students in Statistics
Ideal for students who want to learn statistical modeling concepts and apply them practically with R and Python.
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Researchers and Academics
Perfect for researchers needing to analyze data models and convey findings clearly, utilizing R and Python tools.
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Absolute Beginners
Not suitable for those with no prior knowledge of statistics, R, or Python, as it may be too complex.
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English edition Vincent ArelBundock Format: Paperback Statistics Editorial Review
Model To Meaning: How To Interpret Statistical Models With R And Python is a comprehensive guide published by Chapman and Hall/CRC, set to be released on September 29, 2025. This first edition spans 262 pages, thoughtfully crafted to enhance understanding of statistical modeling concepts through both R and Python. The clear explanations and structured approach make it suitable for both beginners and those looking to deepen their knowledge. The book covers key methodologies that help in interpreting models effectively, making it a valuable resource for data scientists and statisticians alike.
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Pros
- Well-structured for beginners and advanced users
- Covers interpretation of models effectively
- Includes practical examples with R and Python
- Thorough explanations enhance understanding
- Spans a comprehensive 262 pages
Cons
- Limited to statistical modeling topics
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Features & Benefits
- Apply a consistent workflow for statistical and machine learning models.
- Transform complex data into easy-to-understand insights.
- Gain practical skills using R and Python.
- Enhance your model interpretation abilities.
- Ideal for beginners and experienced users alike.
- Unlock the potential of your statistical models.
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