An Introduction to Statistical Inference and Its Applications with R

Emphasizing concepts rather than recipes, An Introduction to Statistical Inference and Its Applications with R provides a clear exposition of the methods of statistical inference for students who are comfortable with mathematical notation. Numerous examples, case studies, and exercises are included. R is used to simplify computation, create figures, and draw pseudorandom samples--not to perform entire analyses.

After discussing the importance of chance in experimentation, the text develops basic tools of probability. The plug-in principle then provides a transition from populations to samples, motivating a variety of summary statistics and diagnostic techniques. The heart of the text is a careful exposition of point estimation, hypothesis testing, and confidence intervals. The author then explains procedures for 1- and 2-sample location problems, analysis of variance, goodness-of-fit, and correlation and regression. He concludes by discussing the role of simulation in modern statistical inference.

Focusing on the assumptions that underlie popular statistical methods, this textbook explains how and why these methods are used to analyze experimental data.

《統計推論與R應用導論》強調概念而非配方，為對數學符號感到舒適的學生提供了統計推論方法的清晰解說。書中包含了大量的例子、案例研究和練習題。R語言用於簡化計算、繪製圖表和生成偽隨機樣本，而非進行整個分析過程。

在討論實驗中機會的重要性後，本書介紹了基本的概率工具。插補原則提供了從母體到樣本的過渡，從而激發了各種摘要統計和診斷技術的應用。本書的核心是對點估計、假設檢驗和置信區間的仔細解說。作者還解釋了處理單樣本和雙樣本位置問題、變異數分析、適配度檢驗以及相關和迴歸分析的程序。最後，他討論了模擬在現代統計推論中的作用。

本教材著重解釋流行統計方法背後的假設，並解釋了這些方法如何以及為何用於分析實驗數據。

Michael W. Trosset is Professor of Statistics and Director of the Indiana Statistical Consulting Center at Indiana University.

Michael W. Trosset 是印第安納大學統計學教授，並擔任印第安納統計諮詢中心的主任。