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)
在依賴於數據分析的各種科學學科中，對基本統計學和概率的深入理解，以及使用R的基本技能至關重要。本書作為從零開始學習統計方法的入門，假設讀者具有扎實的高中數學基礎。讀者將逐步從基本概念進展到高級統計建模，並通過保險、生物學、生態學、工程學、環境學、醫學和社會科學的實例，突顯了該主題在現實世界中的相關性。配套的R軟件包使練習和應用無縫結合，非常適合初學者。本書共分為五個部分的19章。第一部分介紹基本統計學和R軟件包，教讀者計算簡單統計量並創建基本數據圖形。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、泊松、正態、對數正態）。最後介紹中心極限定理和多個隨機變量的聯合分佈。第三部分探討統計推斷，包括點估計和區間估計、假設檢驗和貝葉斯推斷。這一部分故意減少技術性，使其對沒有廣泛數學背景的讀者易於理解。第四部分涉及高級概率和統計分佈理論，假設讀者對（或同時學習）高級微積分和線性代數等數學方法有一定的熟悉。最後，第五部分專注於使用簡單和多重回歸以及變異數分析進行高級統計建模，為進一步研究機器學習和數據科學提供基礎，適用於各種數據和決策分析情境。基於多年的教學經驗，本教科書包含大量練習題，並廣泛使用R，非常適合為期一年的數據科學模塊和課程。除了大學課程外，本書還充分涵蓋了倫敦精算師學會的精算統計學1考試大綱。它還為統計學和概率的研究生學習提供了堅實的基礎，或者作為統計學的可靠參考資料。
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篇關於統計方法和建模的研究論文。
