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Probability and statistics are subjects fundamental to data analysis, making them essential for efficient artificial intelligence. Although the foundational concepts of probability and statistics remain constant, what needs to be taught is constantly evolving.The first half of the book introduces probability, conditional probability and the standard probability distributions in the traditional way. The second half considers the power of the modern computer and our reliance on technology to do the calculations for us.Offering a fresh presentation that builds on the author's previous book, Understanding Statistics, this book includes exercises (with solutions at the rear of the book) and worked examples. Chapters close with a brief mention of the relevant R commands and summary of the content. Increasingly difficult mathematical sections are clearly indicated, and these can be omitted without affecting the understanding of the remaining material.Aimed at first year graduates, this book is also suitable for readers familiar with mathematical notation.
This text investigates how foundational principles of probability and statistics can be effectively taught and applied within the context of modern computational power. Author Graham Upton leverages his academic background to bridge the gap between traditional mathematical theory and the practical reliance on technology in contemporary data science. The book presents a structured framework that balances theoretical probability distributions with the application of R programming to handle complex calculations. By clearly demarcating advanced mathematical sections, the author ensures the material remains accessible to first-year graduates and those with a basic grasp of mathematical notation.
What You Will Find
Experts and educators recognize this work as a bridge between classical statistical theory and modern computational practice. Readers frequently note the utility of the R command summaries, which help transition theoretical knowledge into practical application for data analysis.
Page Count:
384
Publication Date:
2025-10-01
Publisher:
Oxford University Press
ISBN-10:
019894313X
ISBN-13:
9780198943136
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