商品描述
A strong grasp of elementary statistics and probability, along with basic skills in using R, is essential for various scientific disciplines reliant on data analysis. This book serves as a gateway to learning statistical methods from scratch, assuming a solid background in high school mathematics. Readers gradually progress from basic concepts to advanced statistical modelling, with examples from actuarial, biological, ecological, engineering, environmental, medicine, and social sciences highlighting the real-world relevance of the subject. An accompanying R package enables seamless practice and immediate application, making it ideal for beginners. The book comprises 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 modelling using simple and multiple regression and analysis of variance, laying the foundation for further studies in machine learning and data science applicable to various data and decision analytics contexts. Based on years of teaching experience, this textbook includes numerous exercises and makes extensive use of R, making it ideal for year-long data science modules and courses. In addition to university courses, the book amply covers the syllabus for the Actuarial Statistics 1 examination of the Institute and Faculty of Actuaries in London. It also provides a solid foundation for postgraduate studies in statistics and probability, or a reliable reference for statistics.
商品描述(中文翻譯)
對於依賴數據分析的各種科學學科，對基本統計學和概率的深入理解，以及使用 R 的基本技能是必不可少的。本書作為從零開始學習統計方法的入門書籍，假設讀者具備堅實的高中數學基礎。讀者將逐步從基本概念進展到高級統計建模，書中舉例來自精算、生物、生态、工程、環境、醫學和社會科學，突顯了該主題的現實相關性。隨書附帶的 R 套件使得練習和即時應用變得無縫，特別適合初學者。本書共包含 19 章，分為五個部分。第一部分介紹基本統計學和 R 軟體包，教導讀者計算簡單統計數據並創建基本數據圖表。第二部分深入探討概率概念，包括規則和條件概率，並介紹廣泛使用的離散和連續概率分佈（例如，二項分佈、泊松分佈、正態分佈、對數正態分佈）。最後以中心極限定理和多個隨機變數的聯合分佈作結。第三部分探討統計推斷，涵蓋點估計和區間估計、假設檢驗以及貝葉斯推斷。這一部分故意不那麼技術性，使得沒有廣泛數學背景的讀者也能理解。第四部分涉及高級概率和統計分佈理論，假設讀者對高級微積分和線性代數等數學方法有一定的熟悉度（或同時學習）。最後，第五部分專注於使用簡單和多重回歸及變異數分析的高級統計建模，為進一步學習機器學習和數據科學奠定基礎，這些知識可應用於各種數據和決策分析的情境。基於多年的教學經驗，本教材包含大量練習，並廣泛使用 R，特別適合為期一年的數據科學模塊和課程。除了大學課程外，本書充分涵蓋倫敦精算學院的精算統計 1 考試大綱。它還為研究生學習統計學和概率學提供了堅實的基礎，或作為統計學的可靠參考。
作者簡介
Sujit Sahu is a Professor of Statistics at the University of Southampton. He is the author of the book Bayesian Modeling of Spatio-Temporal Data with R published by Chapman and Hall/CRC Press. He has published more than 60 research papers on statistical methods and modelling.
作者簡介(中文翻譯)
Sujit Sahu 是南安普敦大學的統計學教授。他是由 Chapman and Hall/CRC Press 出版的書籍 Bayesian Modeling of Spatio-Temporal Data with R 的作者。他已發表超過 60 篇有關統計方法和建模的研究論文。
