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    Read more about OpenIntro Statistics - Fourth Edition

    OpenIntro Statistics - Fourth Edition

    (21 reviews)

    David M. Diez, Harvard School of Public Health

    Christopher D. Barr, Harvard School of Public Health

    Mine Cetinkaya-Rundel, Duke University

    Copyright Year:

    Last Update: 2019

    Publisher: OpenIntro

    Language: English

    Formats Available

    Conditions of Use

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    CC BY-SA


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    Reviewed by Njesa Totty, Assistant Professor of Statistics, Framingham State University on 5/24/24

    The book does cover data collection and visualization, random variables, distributions, and inference up through logistic regression. This is great for an introductory statistics course. read more

    Reviewed by Sanduni Palliyage, Lecturer, James Madison University on 9/5/23

    This book includes introduction to data, summarizing data (numerical and graphical), probability, distributions of random variables, inference for categorical and numeric data, linear regression, multiple linear regression and logistic regression.... read more

    Reviewed by  Hamdy Mahmoud, Collegiate Assistant Professor, Virginia Tech on 5/16/22

    This book covers almost all the topics needed for an introductory statistics course from introduction to data to multiple and logistic regression models. One of the good topics is the random sampling methods, such as simple sample, stratified,... read more

    Reviewed by Leanne Merrill, Assistant Professor, Western Oregon University on 6/14/21

    This book has both the standard selection of topics from an introductory statistics course along with several in-depth case studies and some extended topics. In particular, the malaria case study and stokes case study add depth and real-world... read more

    Reviewed by Denise Wilkinson, Professor of Mathematics, Virginia Wesleyan University on 4/20/21

    This text book covers most topics that fit well with an introduction statistics course and in a manageable format. The chapter summaries are easy to follow and the order of the chapters begin with "Introduction to Data," which includes treatment... read more

    Reviewed by Monte Cheney, Associate Professor, Central Oregon Community College on 1/15/21

    Unless I missed something, the following topics do not seem to be covered: stem-and-leaf plots, outlier analysis, methods for finding percentiles, quartiles, Coefficient of Variation, inclusion of calculator or other software, combinatorics,... read more

    Reviewed by Kendall Rosales, Instructor and Service Level Coordinator, Western Oregon University on 8/20/20

    There is more than enough material for any introductory statistics course. There are a lot of topics covered. The topics are not covered in great depth; however, as an introductory text, it is appropriate. My biggest complaint is that... read more

    Reviewed by Alice Brawley Newlin, Assistant Professor, Gettysburg College on 3/31/20

    I found the book to be very comprehensive for an undergraduate introduction to statistics - I would likely skip several of the more advanced sections (a few of these I mention below in my comments on its relevance) for this level, but I was glad... read more

    Reviewed by Darin Brezeale, Senior Lecturer, University of Texas at Arlington on 1/21/20

    This book covers the standard topics for an introductory statistics courses: basic terminology, a one-chapter introduction to probability, a one-chapter introduction to distributions, inference for numerical and categorical data, and a one-chapter... read more

    Reviewed by Elizabeth Ward, Assistant Professor , James Madison University on 3/11/19

    Covers all of the topics usually found in introductory statistics as well as some extra topics (notably: log transforming data, randomization tests, power calculation, multiple regression, logistic regression, and map data). Similar to most intro... read more

    Reviewed by Lily Huang, Adjunct Math Instructor , Bethel University on 11/13/18

    The text covers all the core topics of statistics—data, probability and statistical theories and tools. According to the authors, the text is to help students “forming a foundation of statistical thinking and methods,” unfortunately, some basic... read more

    Reviewed by Barbara Kraemer, Part-time faculty, De Paul University School of Public Service on 6/20/17

    The texts includes basic topics for an introductory course in descriptive and inferential statistics. The approach is mathematical with some applications. More extensive coverage of contingency tables and bivariate measures of association would... read more

    Reviewed by Gregg Stall, Associate Professor, Nicholls State University on 2/8/17

    The text covers the foundations of data, distributions, probability, regression principles and inferential principles with a very broad net. It is certainly a fitting means of introducing all of these concepts to fledgling research students. At... read more

    Reviewed by Greg McAvoy, Professor, University of North Carolina at Greensboro on 12/5/16

    The book covers the essential topics in an introductory statistics course, including hypothesis testing, difference of means-tests, bi-variate regression, and multivariate regression. The authors make effective use of graphs both to illustrate the... read more

    Reviewed by Casey Jelsema, Assistant Professor, West Virginia University on 12/5/16

    There is one section that is under-developed (general concepts about continuous probability distributions), but aside from this, I think the book provides a good coverage of topics appropriate for an introductory statistics course. read more

    Reviewed by Emiliano Vega, Mathematics Instructor, Portland Community College on 12/5/16

    For a Statistics I course at most community colleges and some four year universities, this text thoroughly covers all necessary topics. For example, types of data, data collection, probability, normal model, confidence intervals and inference for... read more

    Reviewed by Robin Thomas, Professor, Miami University, Ohio on 8/21/16

    The coverage of this text conforms to a solid standard (very classical) semester long introductory statistics course that begins with descriptive statistics, basic probability, and moves through the topics in frequentist inference including basic... read more

    Reviewed by Monte Cheney, Associate Professor of Mathematics, Central Oregon Community College on 8/21/16

    More depth in graphs: histograms especially. Percentiles? Also, non-parametric alternatives would be nice, especially Monte Carlo/bootstrapping methods. read more

    Reviewed by Bo Hu, Assistant Professor, University of Minnesota on 7/15/14

    This book covers topics in a traditional curriculum of an introductory statistics course: probabilities, distributions, sampling distribution, hypothesis tests for means and proportions, linear regression, multiple regression and logistic... read more

    Reviewed by Paul Murtaugh, Associate Professor, Oregon State University on 7/15/14

    The text has a thorough introduction to data exploration, probability, statistical distributions, and the foundations of inference, but less complete discussions of specific methods, including one- and two-sample inference, contingency tables, and... read more

    Reviewed by Paul Goren, Professor, University of Minnesota on 7/15/14

    This text provides decent coverage of probability, inference, descriptive statistics, bivariate statistics, as well as introductory coverage of the bivariate and multiple linear regression model and logistics regression. Although there are some... read more

    Table of Contents

    • 1. Introduction to data.
    • 2. Summarizing data. 
    • 3. Probability. 
    • 4. Distributions of random variables. 
    • 5. Foundations for inference. 
    • 6. Inference for categorical data.
    • 7. Inference for numerical data.
    • 8. Introduction to linear regression. 
    • 9. Multiple and logistic regression. 

    Ancillary Material

    • OpenIntro
    • About the Book

      OpenIntro Statistics covers a first course in statistics, providing a rigorous introduction to applied
      statistics that is clear, concise, and accessible. This book was written with the undergraduate level
      in mind, but it’s also popular in high schools and graduate courses.
      We hope readers will take away three ideas from this book in addition to forming a foundation
      of statistical thinking and methods.

      • Statistics is an applied field with a wide range of practical applications.
      • You don’t have to be a math guru to learn from real, interesting data.
      • Data are messy, and statistical tools are imperfect. But, when you understand the strengths
      and weaknesses of these tools, you can use them to learn about the world.

      About the Contributors


      David M. Diez is a Quantitative Analyst at Google where he works with massive data sets and performs statistical analyses in areas such as user behavior and forecasting.

      Christopher D. Barr is an Assistant Research Professor with the Texas Institute for Measurement, Evaluation, and Statistics at the University of Houston.

      Mine Cetinkaya-Rundel is the Director of Undergraduate Studies and Assistant Professor of the Practice in the Department of Statistical Science at Duke University.

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