# Introductory Statistics with Randomization and Simulation First Edition

David Diez, Google/YouTube

Christopher Barr, Varadero Capital

Mine Ã‡etinkaya-Rundel, Duke University

Pub Date: 2014

ISBN 13:

Publisher: OpenIntro

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## Conditions of Use

Attribution-NonCommercial-ShareAlike

CC BY-NC-SA

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## Table of Contents

1. Introduction to data.

2. Foundations for inference.

3. Inference for categorical data.

4. Inference for numerical data.

5. Introduction to linear regression.

6. Multiple and logistic regression.

Appendix A. Probability.

## About the Book

We hope readers will take away three ideas from this book in addition to forming a foundation of statistical thinking and methods.

(1) Statistics is an applied field with a wide range of practical applications.

(2) You don’t have to be a math guru to learn from interesting, real data.

(3) Data are messy, and statistical tools are imperfect. However, when you understand the strengths and weaknesses of these tools, you can use them to learn interesting things about the world.

**Textbook overview**

The chapters of this book are as follows:

**1. Introduction to data. **Data structures, variables, summaries, graphics, and basic data collection techniques.

**2. Foundations for inference.** Case studies are used to introduce the ideas of statistical inference with randomization and simulations. The content leads into the standard parametric framework, with techniques reinforced in the subsequent chapters.1

It is also possible to begin with this chapter and introduce tools from Chapter 1 as they

are needed.

**3. Inference for categorical data. **Inference for proportions using the normal and chi-square distributions, as well as simulation and randomization techniques.

**4. Inference for numerical data.** Inference for one or two sample means using the t distribution, and also comparisons of many means using ANOVA. A special section for bootstrapping is provided at the end of the chapter.

**5. Introduction to linear regression. **An introduction to regression with two variables. Most of this chapter could be covered immediately after Chapter 1.

**6. Multiple and logistic regression.** An introduction to multiple regression and logistic regression for an accelerated course.

**Appendix A. Probability.** An introduction to probability is provided as an optional reference. Exercises and additional probability content may be found in Chapter 2 of OpenIntro Statistics at openintro.org. Instructor feedback suggests that probability, if discussed, is best introduced at the very start or very end of the course.

## About the Contributors

### Author(s)

**David Diez** is a Senior Quantitative Analyst at Google/YouTube.

**Christopher Barr** is an Investment Analyst at Varadero Capital.

**Dr. Mine Çetinkaya-Rundel **is the Director of Undergraduate Studies and an Associate Professor of the Practice in the Department of Statistical Science at Duke University. She received her Ph.D. in Statistics from the University of California, Los Angeles, and a B.S. in Actuarial Science from New York University’s Stern School of Business. Her work focuses on innovation in statistics pedagogy, with an emphasis on student-centered learning, computation, reproducible research, and open-source education.