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). 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. One-way analysis of variance is introduced as a special topic, with no mention that it is a generalization of the equal-variances t-test to more than two groups. In particular, examples and datasets about county characteristics, elections, census data, etc, can become outdated fairly quickly. The text covers all the core topics of statisticsdata, probability and statistical theories and tools. The content of the book is accurate and unbiased. I realize this is how some prefer it, but I think introducing the t distribution sooner is more practical. Also, I had some issues finding terms in the index. There is more than enough material for any introductory statistics course. 100% 100% found this document not useful, Mark this document as not useful. There are exercises at the end of each chapter (and exercise solutions at the end of the text). There are sections that can be added and removed at the instructors discretion. The authors are sloppy in their use of hat notation when discussing regression models, expressing the fitted value as a function of the parameters, instead of the estimated parameters (pp. The lack of discussion/examples/inclusion of statistical software or calculator usage is disappointing, as is the inclusion of statistical inference using critical values. 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. Books; Study; Career; Life; . I do think there are some references that may become obsolete or lost somewhat quickly; however, I think a diligent editorial team could easily update data sets and questions to stay current. The most accurate open-source textbook in statistics I have found. The examples were up-to-date, for example, discussing the fact that Google conducts experiments in which different users are given search results in different ways to compare the effectiveness of the presentations. The book reads cleanly throughout. read more. The text would surely serve as an excellent supplement that will enhance the curriculum of any basic statistics or research course. I was concerned that it also might add to the difficulty of analyzing tables. The text provides enough examples, exercises and tips for the readers to understand the materials. OpenIntro Statistics covers a first course in statistics, providing a rigorous introduction to appliedstatistics that is clear, concise, and accessible. The topics are not covered in great depth; however, as an introductory text, it is appropriate. There are two drawbacks to the interface. This could be either a positive or a negative to individual instructors. My biggest complaint is that one-sided tests are basically ignored. The students can easily see the connections between the two types of tests. Quite clear. The text, however, is not engaging and can be dry. The 4th Edition was released on May 1st, 2019. Probability is optional, inference is key, and we feature real data whenever . But there are instances where similar topics are not arranged very well: 1) when introducing the sampling distribution in chapter 4, the authors should introduce both the sampling distribution of mean and the sampling distribution of proportion in the same chapter. read more. The examples for tree diagrams are very good, e.g., small pox in Boston, breast cancer. That is, do probability and inference topics for a SRS, then do probability and inference for a stratified sample and each time taking your probability and inference ideas further so that they are constantly being built upon, from day one! 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). All of the chapters contain a number of useful tips on best practices and common misunderstandings in statistical analysis. It is difficult for a topic that in inherently cumulative to excel at modularity in the manner that is usually understanding. Most contain glaring conceptual and pedagogical errors, and are painful to read (don't get me started on percentiles or confidence intervals). Each chapter begins with a summary and a URL link to resources like videos, slides, etc. I do not think that the exercises focus in on any discipline, nor do they exclude any discipline. 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, All of the notation and terms are standard for statistics and consistent throughout the book. There are also a number of exercises embedded in the text immediately after key ideas and concepts are presented. (e.g., U.S. presidential elections, data from California, data from U.S. colleges, etc.) OpenIntro Statistics offers a traditional introduction to statistics at the college level. 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. 325 and 357). The final chapter (8) gives superficial treatments of two huge topics, multiple linear regression and logistic regression, with insufficient detail to guide serious users of these methods. The best statistics OER I have seen yet. David M. Diez, Mine etinkaya-Rundel, Christopher D. Barr . OpenIntro Statistics offers a traditional introduction to statistics at the college level. The coverage of probability and statistics is, for the most part, sound. This problem has been solved: Problem 1E Chapter CH1 Problem 1E Step-by-step solution Step 1 of 5 Refer to the contingency table in problem 1.1 of the textbook to answer the questions. I have seen other texts begin with correlation and regression prior to tests of means, etc., and wonder which approach is best. I have used this book now to teach for 4 semesters and have found no errors. Percentiles? This text is an excellent choice for an introductory statistics course that has a broad group of students from multiple disciplines. The interface is fine. The way the chapters are broken up into sections and the sections are broken up into subsections makes it easy to select the topics that need to be covered in a course based on the number of weeks of the course. As a mathematician, I find this book most readable, but I imagine that undergraduates might become somewhat confused. This is a statistics text, and much of the content would be kept in this order. Overall the organization is good, so I'm still rating it high, but individual instructors may disagree with some of the order of presentation. The consistency of this text is quite good. It strikes me as jumping around a bit. To many texts that cover basic theory are organized as theorem/proof/example which impedes understanding of the beginner. read more. There is a Chinese proverb: one flaw cannot obscure the splendor of the jade. In my opinion, the text is like jade, and can be used as a standalone text with abundant supplements on its website (https://www.openintro.org). openintro statistics fourth edition open textbook library . The order of introducing independence and conditional probability should be switched. The text is up to date and the content / data used is able to be modified or updated over time to help with the longevity of the text. Statistics is not a subject that becomes out of date, but in the last couple decades, more emphasis has been given to usage of computer technology and relevant data. Given that this is an introductory textbook, it is clearly written and accessible to students with a variety of disciplinary backgrounds. The text is in PDF format; there are no problems of navigation. I do not detect a bias in the work. There are a few instances referencing specific technology (such as iPods) that makes the text feel a bit dated. This book is very clearly laid out for both students and faculty. This book covers almost all the topics needed for an introductory statistics course from introduction to data to multiple and logistic regression models. I found virtually no issues in the grammar or sentence structure of the text. This ICME-13 Topical Survey provides a review of recent research into statistics education, with a focus on empirical research published in established educational journals and on the proceedings of important conferences on statistics education. OpenIntro Statistics 4th Edition. The book covers familiar topics in statistics and quantitative analysis and the presentation of the material is accurate and effective. The book has relevant and easily understood scientific questions. 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. The writing in this book is very clear and straightforward. Sample Solutions for this Textbook We offer sample solutions for OPENINTRO:STATISTICS homework problems. 0% 0% found this document useful, Mark this document as useful. of Contents 1. 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). For faculty, everything is very easy to find on the OpenIntro website. More modern approaches to statistical methods, however, will need to include concepts of important to the current replicability crisis in research: measures of effect, extensive applications of power analyses, and Bayesian alternatives. I read the physical book, which is easy to navigate through the many references. The approach is mathematical with some applications. I was impressed by the scope of fields represented in the example problems - everything from estimating the length of possums' heads, to smoke inhalation in one's line of work, to child development, and so on. 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). The text is easily reorganized and re-sequenced. The terms and notation are consistent throughout the text. It would be nice to have an e-book version (though maybe I missed how to access this on the website). 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. While the examples did connect with the diversity within our country or i.e. Typos and errors were minimal (I could find none). As well, the authors define probability but this is not connected as directly as it could be to the 3 fundamental axioms that comprise the mathematical definition of probability. #. There are lots of graphs in the book and they are very readable. 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, simulation methods, bootstrap intervals, or CI's for variance, critical value method for testing, and nonparametric methods. Mine Cetinkaya-Rundel is the Director of Undergraduate Studies and Assistant Professor of the Practice in the Department of Statistical Science at Duke University. I think in general it is a good choice, because it makes the book more accessible to a broad audience. This easily allow for small sets of reading on a class to class basis or larger sets of reading over a weekend. Generation of Electrical Energy, 7th Edition Gupta B.R. The odd-numbered exercises also have answers in the book. More extensive coverage of contingency tables and bivariate measures of association would be helpful. Overall, I liked the book. Download now. The text is easily and readily divisible into subsections. Each section within a chapter build on the previous sections making it easy to align content. The narrative of the text is grounded in examples which I appreciate. "Data" is sometimes singular, sometimes plural in the authors' prose. There are a few color splashes of blue and red in diagrams or URL's. But, when you understand the strengthsand weaknesses of these tools, you can use them to learn about the world. 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