Probability and Statistics for Science and Engineering with Examples in R (Second Edition)
📘 About This Book
Probability and Statistics for Science and Engineering with Examples in R teaches students how to use R software to obtain summary statistics, calculate probabilities and quantiles, find confidence intervals, and conduct statistical testing. The first chapter introduces methods for describing statistics. Over the course of the subsequent eight chapters students will learn about probability, discrete and continuous distributions, multiple random variables, point estimation and testing, and inferences based on one and two samples. The book features a comprehensive table for each type of test to help students choose appropriate statistical tests and confidence intervals. Based on years of classroom experience and extensively class-tested, Probability and Statistics for Science and Engineering with Examples in R is designed for one-semester courses in probability and statistics, and specifically for students in the natural sciences or engineering. The material is also suitable for business and economics students who have studied calculus.
📖 Summary
Probability and Statistics for Science and Engineering with Examples in R, written by Hongshik Ahn and published in its second edition in 2018, serves as a comprehensive and practical guide for students navigating the quantitative demands of scientific and technical disciplines. Rooted in years of rigorous classroom experience and extensively class-tested, the textbook bridges the gap between theoretical statistical concepts and their modern computational applications. Rather than treating statistics as a purely abstract mathematical exercise, the book integrates the R programming language throughout its chapters, empowering learners to harness software tools for real-world data analysis. The structural journey of the book begins with an introductory chapter focused on fundamental methods for describing data and summarizing statistics. From there, the text systematically expands across eight subsequent chapters to cover the core pillars of probability and statistical inference. Readers are introduced to the foundational laws of probability, followed by deep dives into both discrete and continuous probability distributions. The curriculum progresses naturally into the complexities of multiple random variables, setting the stage for advanced inferential techniques. As the text shifts toward formal analysis, it thoroughly explores point estimation and hypothesis testing. Students learn how to derive inferences based on both single and dual samples, gaining the competence needed to evaluate experimental data critically. A standout pedagogical feature of the book is its inclusion of comprehensive reference tables for various types of statistical tests. These tables are specifically designed to guide students through the decision-making process, helping them select the most appropriate statistical tests and confidence intervals for specific scenarios. Ultimately, Hongshik Ahn has crafted a textbook that treats R not as an afterthought, but as an integral component of the learning process. By teaching students how to use R to compute summary statistics, calculate exact probabilities and quantiles, generate confidence intervals, and execute robust statistical testing, the book prepares the next generation of scientists and engineers to tackle data-driven challenges with confidence and technical fluency.
🎯 Key Lessons
⚖️ Pros & Cons
✅ Pros
Integrates practical R software examples directly into the learning process
Extensively class-tested based on years of actual classroom experience
Includes comprehensive tables to help students choose correct statistical tests
Covers a logical progression from basic description to advanced multi-sample inference
⚠️ Cons
May require prior basic familiarity with programming concepts for optimal pacing
Focuses strictly on R, which might not suit those looking for alternative software tools
❓ FAQ
Who is the author of the book? +
The book was written by Hongshik Ahn.
When was the second edition published? +
The second edition was published in 2018.
What programming language is featured in the book? +
The book features examples and applications using the R programming language.
What topics are covered in the opening chapter? +
The first chapter introduces methods for describing statistics.
How does the book help students choose statistical tests? +
It features a comprehensive reference table for each type of test to guide student selection.
