Reasoning with Data


Reasoning with Data
by Jeffrey M Stanton
Pages 页数: 325 pages
Publisher Finelybook 出版社: Guilford Press; 1 edition (15 May 2017)
Language 语言: English
ISBN-10 书号:1462530273
ISBN-13 书号:9781462530274
B0718ZX97T
Review
"What do R and traditional and Bayesian statistics have in common? They allow us to answer questions that are important for science and practice. Stanton has produced a wonderful book that will be useful for students as well as established scholars."--Herman Aguinis, PhD, Avram Tucker Distinguished Scholar and Professor of Management, George Washington University School of Business
"Reasoning with Data takes a careful and principled approach to guiding readers gracefully from the traditional moorings of frequentist statistics into Bayesian analyses and the functionality and frontiers of the R platform. Stanton provides a range of clear explanations, examples, and practice exercises, fueled by his unbounded enthusiasm and rock-solid expertise. This book is an indispensable resource for undergraduate and graduate students across disciplines--as well as researchers--who want to extend their thinking and their research into where the future is headed."--Frederick L. Oswald, PhD, Department of Psychology, Rice University
"This may be an uncommon thing to say about a book on statistics, but Reasoning with Data is enjoyable and entertaining--really! Stanton takes the reader on an experiential hands-on tour of random sampling, statistical inference, and drawing conclusions from numerical results. The concreteness of the presentation and examples will make it easy for the reader to intuitively grasp the fundamental concepts. The book is very timely because both Bayesian inference and R are becoming 'must-have' tools for social and behavioral scientists. At the same time, Stanton provides a solid grounding in the historical approach of null hypothesis significance testing, including both its strengths and weaknesses. This text should have a very wide audience, and would be appropriate as an upper-level undergraduate or entry-level graduate statistics text in any of the social sciences."--Emily A. Butler, PhD, Family Studies and Human Development, University of Arizona
About the Author
Jeffrey M. Stanton, PhD, is Associate Provost for Academic Affairs and Professor in the School of Information Studies at Syracuse University. Dr. Stanton's interests center on research methods, psychometrics, and statistics, with a particular focus on self-report techniques, such as surveys. He conduct research on a variety of substantive topics in organizational psychology, including the interactions of people and technology in institutional contexts. He is the author of numerous scholarly articles and several books, including Information Nation: Education and Careers in the Emerging Information Professions and An Introduction to Data Science. Dr. Stanton’s background also includes more than a decade of experience in business, both in established firms and startup companies.
Engaging and accessible, this book teaches readers how to use inferential statistical thinking to check their assumptions, assess evidence about their beliefs, and avoid overinterpreting results that may look more promising than they really are. It provides step-by-step guidance for using both classical (frequentist) and Bayesian approaches to inference. Statistical techniques covered side by side from both frequentist and Bayesian approaches include hypothesis testing, replication, analysis of variance, calculation of effect sizes, regression, time series analysis, and more. Students also get a complete introduction to the open-source R programming language and its key packages. Throughout the text, simple commands in R demonstrate essential data analysis skills using real-data examples. The companion website provides annotated R code for the book's examples, in-class exercises, supplemental reading lists, and links to online videos, interactive materials, and other resources. Pedagogical Features *Playful, conversational style and gradual approach; suitable for students without strong math backgrounds. *End-of-chapter exercises based on real data supplied in the free R package. *Technical explanation and equation/output boxes. *Appendices on how to install R and work with the sample datasets.

Contents


Chapter 1. Statistical Vocabulary
Chapter 2. Reasoning With Probability
Chapter 3. Probabilities In The Long Run
Chapter 4. Introducing The Logic Of Inference Using Confidence Intervals
Chapter 5. Bayesian And Traditional Hypothesis Testing
Chapter 6. Comparing Groups And Analyzing Experiments
Chapter 7. Associations Between Variables
Chapter 8. Linear Multiple Regression
Chapter 9. Interactions In Anova And Regression
Chapter 10. Logistic Regression
Chapter 11. Analyzing Change Over Time
Chapter 12. Dealing With Too Many Variables
Chapter 13. All Together Now
Appendix A. Getting Started with R
Appendix B. Working with Data Sets in R
Appendix C. Using dplyr with Data Frames
评论
“R和传统和贝叶斯统计学有什么共同之处?它们允许我们回答对科学和实践很重要的问题,斯坦顿出版了一本精彩的书,对学生和成熟的学者来说将是有用的。” - Herman Aguinis ,博士,阿瓦姆·塔克杰出学者和乔治华盛顿大学商学院管理教授
“数据推理”采用谨慎和有原则的方法,将读者从传统的频繁统计数据系统中顺利引导到贝叶斯分析和R平台的功能和前沿。斯坦顿提供了一系列清晰的解释,例子和练习,由这本书对于跨学科的本科和研究生以及研究人员来说是一个不可或缺的资源,他们希望把他们的思想和研究扩展到未来的领域。“ - Frederick赖斯大学心理系L. Oswald博士
“统计数据可能是一个不寻常的事情,但数据推理是愉快和有趣的 - 真的!斯坦顿为读者提供了随机抽样,统计推断和从数字得出结论的体验式实践之旅结果,演示和实例的具体性将使读者轻松掌握基本概念,这本书非常及时,因为贝叶斯推理和R都成为社会和行为科学家的“必备”工具,在同时,斯坦顿也提供了无效假设意义测试的历史方法,包括其优缺点,本文应具有广泛的受众群体,适合作为高级本科生或初级毕业生统计学任何社会科学中的文本。“ - 亚利桑那大学的Emily A. Butler博士,家庭研究与人类发展博士
关于作者
雪梨大学信息研究学院教授兼教授,副教授Jeffrey M. Stanton博士。 Stanton博士的研究方法,心理测量和统计学的兴趣中心,特别关注自我报告技术,如调查。他对组织心理学中的各种实质性话题进行研究,包括人与技术在制度环境中的相互作用。他是许多学术文章和几本书的作者,包括信息国家:新兴信息职业的教育和职业以及数据科学导论。 Stanton博士的背景还包括十多年的商业经验,既有在成立的公司和创业公司。
本书教导读者如何使用推理统计学思考来检查他们的假设,评估他们信仰的证据,避免过度诠释比现实看起来更有希望的结果。它提供了使用古典(频率)和贝叶斯方法推理的分步指导。从频繁和贝叶斯方法并列的统计技术包括假设检验,复制,方差分析,效应大小的计算,回归,时间序列分析等。学生还可以全面了解开放源代码的R编程语言及其关键包。在整个文本中,R中的简单命令演示了使用实际数据示例的基本数据分析技能。伴随网站为本书的示例提供了注释的R代码,课堂练习,补充阅读列表,以及在线视频,互动资料和其他资源的链接。教学特色*嬉戏,对话风格和渐进式;适合没有强大数学背景的学生。 *基于免费R包中提供的实际数据的章节结束练习。 *技术说明和方程/输出框。 *关于如何安装R并使用示例数据集的附录。
目录
第一章统计词汇
第二章推理推理
第三章长期的概率
第4章介绍使用置信区间的推理逻辑
贝叶斯和传统假设检验
第6章比较组和分析实验
第七章变量之间的关联
第八章线性多元回归
第九章Anova与回归的相互作用
第十章逻辑回归
第11章分析时间的变化
第十二章处理太多变量
第十三章现在一起
附录A.入门R
附录B.使用R中的数据集
附录C.使用dplyr与数据帧

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    http://finelybook.com/reasoning-with-data/ 失效了 麻烦重贴一下?谢谢

    jasonaspen152周前 (10-14)回复

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