Students can easily get confused and think the p-value is in favor of the alternative hypothesis. Students are able to follow the text on their own. That being said, I frequently teach a course geared toward engineering students and other math-heavy majors, so I'm not sure that this book would be fully suitable for my particular course in its present form (with expanded exercise selection, and expanded chapter 2, I would adopt it almost immediately). However, after reviewing the textbook at length, I did note that it did become easier to follow the text with the omission of colorful fonts and colors, which may also be noted as distraction for some readers. But, when you understand the strengthsand weaknesses of these tools, you can use them to learn about the world. The text is well-written and with interesting examples, many of which used real data. The book has relevant and easily understood scientific questions. The book includes examples from a variety of fields (psychology, biology, medicine, and economics to name a few). The text, however, is not engaging and can be dry. This diversity in discipline comes at the cost of specificity of techniques that appear in some fields such as the importance of measures of effect in psychology. OpenIntro Statistics supports flexibility in choosing and ordering topics. The format is consistent throughout the textbook. There aren't really any cultural references in the book. The textbook price was updated from $14.99 for the 3rd Edition to $20 for the 4th Edition, which we believe will be a sustainable price point that helps support OpenIntro as it scales into new subjects. The content is up-to-date. It is as if the authors ran out of gas after the first seven chapters and decided to use the final chapter as a catchall for some important, uncovered topics. The nicely designed website (https://www.openintro.org) contains abundant resources which are very valuable for both students and teachers, including the labs, videos, forums and extras. Other examples: "Each of the conclusions are based on some data" (p. 9); "You might already be familiar with many aspects of probability, however, formalization of the concepts is new for most" (p. 68); and "Sometimes two variables is one too many" (p. 21). The text covers the foundations of data, distributions, probability, regression principles and inferential principles with a very broad net. This book was written with the undergraduate level in mind, but it's also popular in high schools and graduate courses. For the most part, examples are limited to biological/medical studies or experiments, so they will last. It would be nice to have an e-book version (though maybe I missed how to access this on the website). The bookmarks of chapters are easy to locate. I do think a more easily navigable e-book would be ideal. You are on page 1 of 3. The best statistics OER I have seen yet. The examples and exercises seem to be USA-centric (though I did spot one or two UK-based examples), but I do not think that it was being insensitive to any group. All of the chapters contain a number of useful tips on best practices and common misunderstandings in statistical analysis. There are a variety of interesting topics in the exercises that include research on the relationship between honesty, age and self control with children; an experiment on a treatment for asthma patients; smoking habits in the U.K.; a study on migraines and acupuncture; and a study on sinusitis and antibiotics. There are also pictures in the book and they appear clear and in the proper place in the chapters. Perhaps an even stronger structure would see all the types of content mentioned above applied to each type of data collection. Statistical methods, statistical inference and data analysis techniques do change much over time; therefore, I suspect the book will be relevant for years to come. Overall, the text is well-written and explained along with real-world data examples. Corresponding textbook Intro Stats | 4th Edition ISBN-13: 9780321825278 ISBN: 0321825276 Authors: Richard D. De Veaux, Paul F Velleman, David E. Bock Rent | Buy Alternate ISBN: 9780134429021, 9780321826213, 9780321925565, 9780321932815 Solutions by chapter Chapter 1 Chapter 2 Chapter 3 Chapter 4 Chapter 5 Chapter 6 Chapter 7 Chapter 8 Chapter 9 The approach of introducing the inferences of proportions and the Chi-square test in the same chapter is novel. I am not necessarily in disagreement with the authors, but there is a clear voice. The flow of a chapter is especially good when the authors continue to use a certain example in developing related concepts. This will increase the appeal of the text. In addition all of the source code to build the book is available so it can be easily modified. We don't have content for this book yet. Search inside document . One of the strengths of this text is the use of motivated examples underlying each major technique. (e.g., U.S. presidential elections, data from California, data from U.S. colleges, etc.) The section on model selection, covering just backward elimination and forward selection, seems especially old-fashioned. Overall, I recommend this book for an introductory statistics course, however, it has some advanced topics. Ability to whitelist other teachers so they can immediately get full access to teacher resources on openintro.org. I find the content to be quite relevant. I value the unique organization of chapters, the format of the material, and the resources for instructors and students. Some more separation between sections, and between text vs. exercises would be appreciated. The book presents all the topics in an appropriate sequence. 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. Technical accuracy is a strength for this text especially with respect to underlying theory and impacts of assumptions. For example, types of data, data collection, probability, normal model, confidence intervals and inference for I did not find any grammatical errors that impeded meaning. The colors of the font and tables in the textbook are mostly black and white. This could make it easier for students or instructors alike to identify practice on particular concepts, but it may make it more difficult for students to grasp the larger picture from the text alone. OpenIntro Statistics offers a traditional introduction to statistics at the college level. The drawbacks of the textbook are: 1) it doesn't offer how to use of any computer software or graphing calculator to perform the calculations and analyses; 2) it didn't offer any real world data analysis examples. For example, it is claimed that the Poisson distribution is suitable only for rare events (p. 148); the unequal-variances form of the standard error of the difference between means is used in conjunction with the t-distribution, with no mention of the need for the Satterthwaite adjustment of the degrees of freedom (p. 231); and the degrees of freedom in the chi-square goodness-of-fit test are not adjusted for the number of estimated parameters (p. 282). The resources, such as labs, lecture notes, and videos are good resources for instructors and students as well. The authors limit their discussion on categorical data analysis to the chi square statistic, which centers on inference rather than on the substantive magnitude of the bivariate relationship. It is certainly a fitting means of introducing all of these concepts to fledgling research students. The book used plenty of examples and included a lot of tips to understand basic concepts such as probabilities, p-values and significant levels etc. There are no proofs that might appeal to the more mathematically inclined. This book is quite good and is ethically produced. It has scientific examples for the topics so they are always in context. The primary ways to navigate appear to be via the pdf and using the physical book. There are also short videos for 75% of the book sections that are easy to follow and a plus for students. While the examples did connect with the diversity within our country or i.e. My only complaint in this is that, unlike a number of "standard" introductory statistics textbooks I have seen, is that the exercises are organized in a page-wide format, instead of, say, in two columns. Having a free pdf version and a hard copy for a few dollars is great. Statistics and Probability Statistics and Probability solutions manuals OpenIntro Statistics 4th edition We have solutions for your book! This book was written with the undergraduate levelin mind, but its also popular in high schools and graduate courses.We hope readers will take away three ideas from this book in addition to forming a foundationof statistical thinking and methods. Similar to most intro stat books, it does not cover the Bayesian view at all. 3rd Edition files and information (2015, 436 pages) 2nd Edition files and information (2012, 426 pages) In fact, I particularly like that the authors occasionally point out means by which data or statistics can be presented in a method that can distort the truth. The subsequent chapters have all of the specifics about carrying out hypothesis tests and calculating intervals for different types of data. The prose is sometimes tortured and imprecise. The code and datasets are available to reproduce materials from the book. As a mathematician, I find this book most readable, but I imagine that undergraduates might become somewhat confused. 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. The book covers familiar topics in statistics and quantitative analysis and the presentation of the material is accurate and effective. It is easy to skip some topics with no lack of consistency or confusion. It does a more thorough job than most books of covering ideas about data, study design, summarizing data and displaying data. Additionally concepts related to flawed practices in data collection and analysis were presented to point out how inaccuracies could arise in research. This text book covers most topics that fit well with an introduction statistics course and in a manageable format. However, it would not suffice for our two-quarter statistics sequence that includes nonparametrics. It would be nice if the authors can start with the big picture of how people perform statistical analysis for a data set. My biggest complaint is that one-sided tests are basically ignored. The coverage of probability and statistics is, for the most part, sound. 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. For example: "Researchers perform an observational study when they collect data in a way that does not directly interfere with how the data arise" (p. 13). The sections on these advanced topics would make this a candidate for more advanced-level courses than the introductory undergraduate one I teach, and I think will help with longevity. My biggest complaint is that Also, the discussion on hypothesis testing could be more detailed and specific. differential equations 4th edition solutions and answers quizlet calculus 4th edition . OpenIntro Statistics offers a traditional introduction to statistics at the college level. I did not see any grammatical issues that distract form the content presented. None of the examples seemed alarming or offensive. "Standard error" is defined as the "standard deviation associated with an estimate" (p. 163), but it is often unclear whether population or sample-based quantities are being referred to. However, I did find the inclusion of practice problems at the end of each section vs. all together the end of the whole chapter (which is the new arrangement in the 4th edition) to be a challenge - specifically, this made it difficult for me to identify easily where sections ended, and in some places, to follow the train of thought across sections. There are a few color splashes of blue and red in diagrams or URL's. These graphs and tables help the readers to understand the materials well, especially most of the graphs are colored figures. Exercises: Yes: Solutions: Odd numbered problems: Solution Manual: Available to verified teachers: License: Creative Commons: Fourth edition (May 2019) Black and white paperback version from Amazon $20; Each chapter is separated into sections and subsections. Teachers might quibble with a particular omission here or there (e.g., it would be nice to have kernel densities in chapter 1 to complement the histogram graphics and some more probability distributions for continuous random variables such as the F distribution), but any missing material could be readily supplemented. Some of the more advanced topics are treated as 'special topics' within the sections (e.g., power and standard error derivations). This book is highly modular. This is a free textbook for a one-semester, undergraduate statistics course. One of the real strengths of the book is the many examples and datasets that it includes. The regression treatment of categorical predictors is limited to dummy coding (though not identified as such) with two levels in keeping with the introductory nature of the text. I found the content in the 4th edition is extremely up-to-date - both in terms of its examples, and in terms of keeping up with the "movements" in many disciplines to be more transparent and considered in hypothesis testing choices (e.g., all hypothesis tests are two-tailed [though the reasoning for this is explained, especially in Section 5.3.7 on one-tailed tests), they include Bayes' theorem, many less common distributions for the introductory level like Bernoulli and Poisson, and estimating statistical power/desired sample size). Ensure every student can access the course textbook. The text is easily reorganized and re-sequenced. 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. Part I makes key concepts in statistics readily clear. The text is accurate due to its rather straight forward approach to presenting material. The texts selection for notation with common elements such as p-hat, subscripts, compliments, standard error and standard deviation is very clear and consistent. In other words, breadth, yes; and depth, not so much. I read the physical book, which is easy to navigate through the many references. 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 I think that the book is fairly easy to read. However, the linear combination of random variables is too much math focused and may not be good for students at the introductory level. Also, as fewer people do manual computations, interpretation of computer software output becomes increasingly important. The simple mention of the subject "statistics" can strike fear in the minds of many students. As the trend of analysis, students will be confronted with the needs to use computer software or a graphing calculator to perform the analyses. read more. While to some degree the text is easily and readily divisible into smaller reading sections, I would not recommend that anyone alter the sequence of the content until after Chapters 1, 3, and 4 are completed. web study with quizlet and memorize flashcards containing terms like 1 1 migraine and . They draw examples from sources (e.g., The Daily Show, The Colbert Report) and daily living (e.g., Mario Kart video games) that college students will surely appreciate. The definitions and procedures are clear and presented in a framework that is easy to follow. 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 regression. web jul 16 2016 openintro statistics fourth edition the solutions are available online i would suggest this book to everyone who has no 167, 185, and 222) and the comparison of two proportions (pp. Chapter 4-6 cover the inferences for means and proportions and the Chi-square test. Introduction The chapters are bookmarked along the side of the pdf file (once downloaded). I reviewed a paperback B&W copy of the 4th edition of this book (published 2019), which came with a list describing the major changes/reorganization that was done between this and the 3rd edition. It would be nice to see more examples of how statistics can bring cultural/social/economic issues to light (without being heavy handed) would be very motivating to students. I did not find any issues with consistency in the text, though it would be nice to have an additional decimal place reported for the t-values in the t-table, so as to make the presentation of corresponding values between the z and t-tables easier to introduce to students (e.g., tail p of .05 corresponds to t of 1.65 - with rounding - in large samples; but the same tail p falls precisely halfway between z of 1.64 and z of 1.65). The book uses relevant topics throughout that could be quickly updated. The material was culturally relevant to the demographic most likely to use the text in the United State. Reads more like a 300-level text than 100/200-level. This is important since examples used authentic situations to connect to the readers. Select the Edition for OpenIntro Statistics Below: . Chapter 7 and 8 cover the linear , multiple and logistic regression. A thoughtful index is provided at the end of the text as well as a strong library of homework / practice questions at the end of each chapter. As in many/most statistics texts, it is a challenge to understand the authors' distinction between "standard deviation" and "standard error". 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. Tables and graphs are sensibly annotated and well organized. The order of the topics seemed appropriate and not unlike many alternatives, but there was the issue of the term highlight boxes terms mentioned above. This text covers more advanced graphical su Understanding Statistics and Experimental Design, Empirical Research in Statistics Education, Statistics and Analysis of Scientific Data. of Contents 1. by David Diez, Mine Cetinkaya-Rundel, Christopher Barr. The only issue I had in the layout was that at the end of many sections was a box high-lighting a term. The book provides an effective index. I did not view an material that I felt would be offensive. For example, I can imagine using pieces of Chapters 2 (Probability) and 3 (Distributions of random variables) to motivate methods that I discuss in service courses. The consistency of this text is quite good. I believe students, as well as, instructors would find these additions helpful. The text is written in lucid, accessible prose, and provides plenty of examples for students to understand the concepts and calculations. This text will be useful as a supplement in the graduate course in applied statistics for public service. The first chapter addresses treatments, control groups, data tables and experiments. Also, non-parametric alternatives would be nice, especially Monte Carlo/bootstrapping methods. There is more than enough material for any introductory statistics course. Fisher's exact test is not even mentioned. This is the most innovative and comprehensive statistics learning website I have ever seen. The OpenIntro project was founded in 2009 to improve the quality and availability of education by producing exceptional books and teaching tools that are free to use and easy to modify. Notation, language, and approach are maintained throughout the chapters. However, I think a greater effort could be made to include more culturally relevant examples in this book. I was sometimes confused by tables with missing data or, as was the case on page 11, when the table was sideways on the page. The examples flow nicely into the guided practice problems and back to another example, definition, set of procedural steps, or explanation. 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. 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