This third edition of a classic textbook presents a comprehensive introduction to Bayesian data analysis. Written for students and researchers alike, the text is written in an easily accessible manner with chapters that contain many exercises as well as detailed worked examples taken from various disciplines. This third edition provides two new chapters on Bayesian nonparametrics and covers computation systems BUGS and R. It also offers enhanced computing advice. The book's website includes solutions to the problems, data sets, software advice, and other ancillary material.

分位数回归的思想起源于1760年，然而，这一回归方法计算的复杂性知道最近依然是一大挑战。虽然如今快速的计算机功能和统计软件的广发应用使得拟合分位数回归模型变得容易，但至今我们仍未提供任何关于分位数回归是什么的介绍。本书中，作者提出了分位数和分位数函数的概念，阐述了分位数回归模型，讨论了它们的估计和推断方法，并通过具体例子演示了对分位数回归估计值的解释。同时，他们也提供了应用分位数回归分析美国1991年和2001年收入不平等的完整例子，以此确定这一方法的思想和步骤。本书填补了该领域的空白并有助于社会科学研究者更加熟悉分位数回归。

An essential guide to the trouble spots and oddities of R. In spite of the quirks exposed here, R is the best computing environment for most data analysis tasks. R is free, open-source, and has thousands of contributed packages. It is used in such diverse fields as ecology, finance, genomics and music. If you are using spreadsheets to understand data, switch to R. You will have safer -- and ultimately, more convenient -- computations.

What is a p-value Anyway? offers a fun introduction to the fundamental principles of statistics, presenting the essential concepts in thirty-four brief, enjoyable stories. Drawing on his experience as a medical researcher, Vickers blends insightful explanations and humor, with minimal math, to help readers understand and interpret the statistics they read every day. What is a p-value Anyway? is the perfect complement to any introductory statistics textbook and will succeed in demonstrating the everyday importance of statistics to your class.

R is rapidly becoming the standard for developing statistical software, and R in a Nutshell provides a quick and practical way to learn this increasingly popular open source language and environment. You'll not only learn how to program in R, but also how to find the right user-contributed R packages for statistical modeling, visualization, and bioinformatics.