Intro Stat with Randomization and Simulation, 1st Edition
This textbook takes a different approach, where the foundations for inference are provided using randomization and simulation methods. Once a solid foundation is formed, a transition is made to traditional approaches.
Tag(s): Statistics
Publication date: 18 Jul 2014
ISBN-10: 1500576697
ISBN-13: 9781500576691
Paperback: 354 pages
Views: 8,176
Type: Textbook
Publisher: CreateSpace Independent Publishing Platform
License: Creative Commons Attribution-ShareAlike 3.0 Unported
Post time: 14 Dec 2016 09:00:00
Intro Stat with Randomization and Simulation, 1st Edition
This textbook takes a different approach, where the foundations for inference are provided using randomization and simulation methods. Once a solid foundation is formed, a transition is made to traditional approaches.
Publication date: 18 Jul 2014
ISBN-10: 1500576697
ISBN-13: 9781500576691
Paperback: 354 pages
Views: 8,176
Document Type: Textbook
Publisher: CreateSpace Independent Publishing Platform
License: Creative Commons Attribution-ShareAlike 3.0 Unported
Post time: 14 Dec 2016 09:00:00
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Barr, Diez, and Rundel wrote:This textbook may be downloaded as a free PDF on the project's website, and the paperback is sold royalty-free. OpenIntro develops free textbooks and course resources for introductory statistics that exceeds the quality standards of traditional textbooks and resources, and that maximizes accessibility options for the typical student. The approach taken in this textbooks differs from OpenIntro Statistics in its introduction to inference. The foundations for inference are provided using randomization and simulation methods. Once a solid foundation is formed, a transition is made to traditional approaches, where the normal and t distributions are used for hypothesis testing and the construction of confidence intervals.
Table of Contents:
Chapter 1: Introduction to data.
Chapter 2: Foundations for inference.
Chapter 3: Inference for categorical data
Chapter 4: Inference for numerical data.
Chapter 5: Introduction to linear regression.
Chapter 6: Multiple and logistic regression.
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About The Author(s)
Christopher D. Barr was a Research Assistant Professor, in the Texas Institute for Measurement, Evaluation, and Statistics (TIMES), at the University of Houston. He now works as an Analyst at Varadero Capital. His research interests include application of multilevel and latent variable modelling techniques for studying nested data structures, particularly in the area of teacher and school level effects on students’ educational outcomes.
Christopher D. Barr was a Research Assistant Professor, in the Texas Institute for Measurement, Evaluation, and Statistics (TIMES), at the University of Houston. He now works as an Analyst at Varadero Capital. His research interests include application of multilevel and latent variable modelling techniques for studying nested data structures, particularly in the area of teacher and school level effects on students’ educational outcomes.
Mine Çetinkaya-Rundel is the Director of Undergraduate Studies and an Assistant Professor of the Practice in the Department of Statistical Science at Duke University. Her research focuses on innovation in statistics pedagogy, with an emphasis on student-centered learning, computation, reproducible research, and open-source education.
Mine Çetinkaya-Rundel is the Director of Undergraduate Studies and an Assistant Professor of the Practice in the Department of Statistical Science at Duke University. Her research focuses on innovation in statistics pedagogy, with an emphasis on student-centered learning, computation, reproducible research, and open-source education.
Dr. Diez is a Quantitative Analyst at Google/Youtube where he works with massive data sets and performs statistical analyses in areas such as user behavior and forecasting.
Dr. Diez is a Quantitative Analyst at Google/Youtube where he works with massive data sets and performs statistical analyses in areas such as user behavior and forecasting.