Introduction to Probability, Statistics & R: Foundations for Data-Based Sciences

This book is a comprehensive guide to learning statistical methods from scratch, with a focus on real-world applications across various scientific disciplines. It assumes a solid background in high school mathematics and covers basic statistics, probability, statistical inference, advanced probability, and statistical modelling using R. It includes numerous exercises and is ideal for year-long data science modules and courses, as well as the Actuarial Statistics 1 examination of the Institute and Faculty of Actuaries in London.

Format: Hardback
Length: 555 pages
Publication date: 02 April 2024
Publisher: Springer International Publishing AG

For various scientific disciplines that rely on data analysis, a solid understanding of fundamental statistics and probability, as well as fundamental R skills, is essential. This book acts as a beginner's guide to learning statistical methods from scratch, assuming a strong foundation in high school mathematics. Readers gradually progress from basic concepts to advanced statistical modeling, with examples from actuarial, biological, ecological, engineering, environmental, medical, and social sciences emphasizing the real-world relevance of the subject. An accompanying R package enables seamless practice and immediate application, making it ideal for beginners.

The book is divided into 19 chapters, divided into five parts. Part I introduces basic statistics and the R software package, teaching readers to calculate simple statistics and create basic data graphs. Part II delves into probability concepts, including rules and conditional probability, and introduces widely used discrete and continuous probability distributions (e.g., binomial, Poisson, normal, log-normal). It concludes with the central limit theorem and joint distributions for multiple random variables. Part III explores statistical inference, covering point and interval estimation, hypothesis testing, and Bayesian inference. This part is intentionally less technical, making it accessible to readers without an extensive mathematical background. Part IV addresses advanced probability and statistical distribution theory, assuming some familiarity with (or concurrent study of) mathematical methods like advanced calculus and linear algebra. Finally, Part V focuses on advanced statistical modeling using simple and multiple regression and analysis of variance, laying the foundation for further studies in machine learning and data science applications.

Dimension: 235 x 155 (mm)
ISBN-13: 9783031378645