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OpenIntro Statistics

(11 reviews)

David Diez, Harvard School of Public Health

Christopher Barr, Harvard School of Public Health

Mine Cetinkaya-Rundel, Duke University

Pub Date: 2015

Publisher: OpenIntro

Language: English

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Reviews

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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 6/21/17

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 2/9/17

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 Casey Jelsema, Assistant Professor, West Virginia University on 12/6/16

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 Greg McAvoy, Professor, University of North Carolina at Greensboro on 12/6/16

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 Emiliano Vega, Mathematics Instructor, Portland Community College on 12/6/16

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 Monte Cheney, Associate Professor of Mathematics, Central Oregon Community College on 8/22/16

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

 

Reviewed by Robin Thomas, Professor, Miami University, Ohio on 8/22/16

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 Bo Hu, Assistant Professor, University of Minnesota on 7/16/14

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 Goren, Professor, University of Minnesota on 7/16/14

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

 

Reviewed by Paul Murtaugh, Associate Professor, Oregon State University on 7/16/14

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,... read more

 

Table of Contents

Chapter 1: Introduction to data
Chapter 2: Probability (special topic)
Chapter 3: Distributions of random variables
Chapter 4: Foundations for inference
Chapter 5: Inference for numerical data
Chapter 6: Inference for categorical data
Chapter 7: Introduction to linear regression
Chapter 8: Multiple and logistic regression

About the Book

OpenIntro Statistics 3rd Edition strives to be a complete introductory textbook of the highest caliber. Its core derives from the classic notions of statistics education and is extended by recent innovations. The textbook meets high quality standards and has been used at Princeton, Vanderbilt, UMass Amherst, and many other schools. We look forward to expanding the reach of the project and working with teachers from all colleges and schools. The chapters of this book are as follows:

  • Introduction to data. Data structures, variables, summaries, graphics, and basicdata collection techniques.
  • Probability (special topic). The basic principles of probability. An understandingof this chapter is not required for the main content in Chapters 3-8.
  • Distributions of random variables. Introduction to the normal model and otherkey distributions.
  • Foundations for inference. General ideas for statistical inference in the context ofestimating the population mean.
  • Inference for numerical data. Inference for one or two sample means using the normal model and t distribution, and also comparisons of many means using ANOVA.
  • Inference for categorical data. Inference for proportions using the normal and chi-square distributions, as well as simulation and randomization techniques.
  • Introduction to linear regression. An introduction to regression with two variables.Most of this chapter could be covered after Chapter 1.
  • Multiple and logistic regression. An introduction to multiple regression and logistic regression for an accelerated course.

OpenIntro Statistics was written to allow exibility in choosing and ordering coursetopics. The material is divided into two pieces: main text and special topics. The maintext has been structured to bring statistical inference and modeling closer to the front of acourse. Special topics, labeled in the table of contents and in section titles, may be addedto a course as they arise naturally in the curriculum.

About the Contributors

Authors

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.