使用SAS与R检验统计假设 Statistical Hypothesis Testing With Sas And R

基本信息

Format:Hardback 312 pages

Publisher:John Wiley & Sons Inc

Imprint:John Wiley & Sons Inc

ISBN:9781119950219

Published:12 Mar 2014

Weight:626g

Dimensions:252 x 168 x 20 (mm)

页面参数仅供参考，具体以实物为准

内容简介

统计假设检验综合指南，附有 SAS 和 R 示例

分析数据集时，经常会出现以下问题：

SAS 或 R 中是否有统计检验的简写程序？

如果有，我该如何使用？

如果没有，我该如何自己编写测试程序？

本书回答了这些问题，并概述了常见的统计测试问题，让您可以轻松找到并执行

适当的统计测试。

本书概述了统计测试理论，并提供了每个测试的基本描述，包括必要的先决条件、假设、

正式测试问题和测试统计量。本书提供了 SAS 和 R 中的示例，

以及执行测试的程序代码、结果输出和解释必要程序参数的注释。

主要特点：

为所介绍的每个测试提供 SAS 和 R 中的示例。

以清晰易懂的方式展示常见的统计测试。

学者、从业人员以及 SAS 和 R 程序员会发现这本书是一份宝贵的资源。使用 SAS 和 R 的学生也会发现它是参考和数据分析的理想选择。

A comprehensive guide to statistical hypothesis testing with examples in SAS and R

When analyzing datasets the following questions often arise:

Is there a short hand procedure for a statistical test available in SAS or R?

If so, how do I use it?

If not, how do I program the test myself?

This book answers these questions and provides an overview of the most common

statistical test problems in a comprehensive way, making it easy to find and perform

an appropriate statistical test.

A general summary of statistical test theory is presented, along with a basic

description for each test, including the necessary prerequisites, assumptions, the

formal test problem and the test statistic. Examples in both SAS and R are provided,

along with program code to perform the test, resulting output and remarks

explaining the necessary program parameters.

Key features:

• Provides examples in both SAS and R for each test presented.

• Looks at the most common statistical tests, displayed in a clear and easy to follow way.

Academics, practitioners and SAS and R programmers will find this book a valuable resource. Students using SAS and R will also find it an excellent choice for reference and data analysis.

作者简介

Dirk Taeger，德国社会事故保险预防和职业医学研究所，波鸿鲁尔大学 (IPA) 研究所，德国波鸿

Sonja Kuhnt，多特蒙德应用科学与艺术大学计算机科学系，德国多特蒙德

Dirk Taeger, Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr-Universität Bochum (IPA), Bochum, Germany

Sonja Kuhnt, Department of Computer Science, Dortmund University of Applied Sciences and Arts, Dortmund, Germany