Model to Meaning: How to Interpret Statistical Models with R and Python
Readers will learn how to transform complex parameter estimates into quantities that are readily interpretable, intuitive, and understandable.
Model to Meaning: How to Interpret Statistical Models with R and Python
Item #: 252941402

Model to Meaning: How to Interpret Statistical Models with R and Python

Item #: 252941402

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    Product Details

    Shop Model to Meaning: How to Interpret Statistical Models with R and Python online at a best price in UAE. 1032908726
    • 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?

    Suitable For
    • 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.

    • Researchers and Academics

      Perfect for researchers needing to analyze data models and convey findings clearly, utilizing R and Python tools.

    Not Suitable For
    • 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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