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**: 4,151

**Type**: Textbook

**Publisher**:
CreateSpace Independent Publishing Platform

**License**:
Creative Commons Attribution-ShareAlike 3.0 Unported

**Post time**: 14 Dec 2016 08:00:00

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.

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From the Book Description:

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.

More Resources:

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.

More Resources:

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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.

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.

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