Introductory Statistics with Randomization and Simulation First Edition
David Diez, Google/YouTube
Christopher Barr, Varadero Capital
Mine Çetinkaya-Rundel, Duke University
Pub Date: 2014
Read This Book
Conditions of Use
The text covers all areas and ideas of the subject appropriately, read more
The text covers all areas and ideas of the subject appropriately,
The textbook is accurate, error free and unbiased
The text is written and/or arranged in such a way that necessary update easy and straightforward to implement.
The material is presented in a clear and concise manner.
Yes. The text is internally consistent in terms of terminology and framework.
The text is easily and readily divided into smaller reading sections that can be assigned at different points within the course.
The topics in the text are presented in a logical, clear fashion. However the first chapter covers part of Descriptive statistic, I think that should not be the case. Descriptive Statistic should have their own chapter.
The text is free of significant interference issues. It is on a PDF format and can be printed easily
No grammatical errors.
The textbook is not culturally insensitive or offensive in any way.
Overall the textbook is a good one. The auteurs covered in details how to interpret computer output for the regression and what which coefficient represents. They covered how to interpret the slope and the y-intercept in the context of the problem at hand.
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.
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
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
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.