OpenIntro Statistics

(10 reviews)

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David Diez, Harvard School of Public Health
Christopher Barr, Harvard School of Public Health
Mine Cetinkaya-Rundel, Duke University

Pub Date: 2015

ISBN 13:

Publisher: Independent

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CC BY-SA

Reviews

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Reviewed by Barbara Kraemer, Part-time faculty, De Paul University School of Public Service, on 6/21/2017.

The texts includes basic topics for an introductory course in descriptive and inferential statistics. The approach is mathematical with some … read more

 

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Reviewed by Gregg Stall, Associate Professor, Nicholls State University, on 2/9/2017.

The text covers the foundations of data, distributions, probability, regression principles and inferential principles with a very broad net. It is … read more

 

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Reviewed by Greg McAvoy, Professor, University of North Carolina at Greensboro, on 12/6/2016.

The book covers the essential topics in an introductory statistics course, including hypothesis testing, difference of means-tests, bi-variate … read more

 

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Reviewed by Casey Jelsema, Assistant Professor, West Virginia University, on 12/6/2016.

There is one section that is under-developed (general concepts about continuous probability distributions), but aside from this, I think the book … read more

 

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Reviewed by Emiliano Vega, Mathematics Instructor, Portland Community College, on 12/6/2016.

For a Statistics I course at most community colleges and some four year universities, this text thoroughly covers all necessary topics. For example, … read more

 

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Reviewed by Robin Thomas, Professor, Miami University, Ohio, on 8/22/2016.

The coverage of this text conforms to a solid standard (very classical) semester long introductory statistics course that begins with descriptive … read more

 

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Reviewed by Monte Cheney, Associate Professor of Mathematics, Central Oregon Community College, on 8/22/2016.

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

 

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Reviewed by Paul Murtaugh, Associate Professor, Oregon State University, on 7/16/2014.

The text has a thorough introduction to data exploration, probability, statistical distributions, and the foundations of inference, but less … read more

 

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Reviewed by Paul Goren, Professor, University of Minnesota, on 7/16/2014.

This text provides decent coverage of probability, inference, descriptive statistics, bivariate statistics, as well as introductory coverage of … read more

 

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Reviewed by Bo Hu, Assistant Professor, University of Minnesota, on 7/16/2014.

This book covers topics in a traditional curriculum of an introductory statistics course: probabilities, distributions, sampling distribution, … 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 basic data collection techniques.
  • Probability (special topic). The basic principles of probability. An understanding of this chapter is not required for the main content in Chapters 3-8.
  • Distributions of random variables. Introduction to the normal model and other key distributions.
  • Foundations for inference. General ideas for statistical inference in the context of estimating 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 course topics. The material is divided into two pieces: main text and special topics. The main text has been structured to bring statistical inference and modeling closer to the front of a course. Special topics, labeled in the table of contents and in section titles, may be added to a course as they arise naturally in the curriculum.

About the Contributors

Author(s)

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.