The organization is fine. I think it would work well for liberal arts/social science students, but not for economics/math/science students who would need more mathematical rigor. Although it covers almost all the basic topics for an introductory course, it has some advanced topics which make it a candidate for more advanced courses as well and I believe this will help with longevity. I do not think that the exercises focus in on any discipline, nor do they exclude any discipline. The best statistics OER I have seen yet. Merely said, the openintro statistics 4th edition solutions is universally compatible gone any devices to read. The examples and solutions represent the information with formulas and clear process. HS Statistics (2nd Ed) exercise solutions Available to Verified Teachers, click here to apply for access Intro Stat w/Rand & Sim exercise solutions Available to Verified Teachers, click here to apply for access Previous Editions Click below to explore the history of each textbook that is in its 2nd or later edition. Additionally, as research and analytical methods evolve, then so will the need to cover more non-traditional types of content i.e mixed methodologies, non parametric data sets, new technological research tools etc. The bookmarks of chapters are easy to locate. One topic I was surprised to see trimmed and placed online as extra content were the calculations for variance estimates in ANOVA, but these are of course available as supplements for the book. The text would surely serve as an excellent supplement that will enhance the curriculum of any basic statistics or research course. Navigation as a PDF document is simple since all chapters and subsection within the table of contents are hyperlinked to the respective section. There are no proofs that might appeal to the more mathematically inclined. There are sections that can be added and removed at the instructors discretion. Statistics and Probability Statistics and Probability solutions manuals OpenIntro Statistics 4th edition We have solutions for your book! 100% 100% found this document not useful, Mark this document as not useful. David M. Diez, Harvard School of Public Health, Christopher D. Barr, Harvard School of Public Health, 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. Black and white paperback edition. The code and datasets are available to reproduce materials from the book. Overall, the text is well-written and explained along with real-world data examples. I suspect these will prove quite helpful to students. Marginal notes for key concepts & formulae? read more. Although there are some materials on experimental and observational data, this is, first and foremost, a book on mathematical and applied statistics. Notation, language, and approach are maintained throughout the chapters. The text, though dense, is easy to read. But, when you understand the strengthsand weaknesses of these tools, you can use them to learn about the world. read more. This is a statistics text, and much of the content would be kept in this order. It would be nice to have an e-book version (though maybe I missed how to access this on the website). It appears smooth and seamless. The 4th Edition was released on May 1st, 2019. It can be considered comprehensive if you consider this an introductory text. And, the authors have provided Latex code for slides so that instructors can customize the slides to meet their own needs. 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. It is difficult for a topic that in inherently cumulative to excel at modularity in the manner that is usually understanding. Table. 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). These are not necessary knowledge for future sections, so it is easy to see which sections you might leave out if there isnt time or desire to complete the whole book. The narrative of the text is grounded in examples which I appreciate. Overall I like it a lot. This will increase the appeal of the text. My biggest complaint is that one-sided tests are basically ignored. This book does not contain anything culturally insensitive, certainly. The text book contains a detailed table of contents, odd answers in the back and an index. Having a free pdf version and a hard copy for a few dollars is great. Great job overall. Since this particular textbook relies heavily on the use of scenarios or case study type examples to introduce/teach concepts, the need to update this information on occasion is real. Each chapter begins with a summary and a URL link to resources like videos, slides, etc. For the most part I liked the flow of the book, though there were a few instances where I would have liked to see some different organization. Also, for how the authors seem to be focusing on practicalities, I was somewhat surprised about some of the organization of the inference sections. Christopher D. Barr is an Assistant Research Professor with the Texas Institute for Measurement, Evaluation, and Statistics at the University of Houston. 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. There is only a small section explaining why they do not use one sided tests and a brief explanation on how to perform a one sided test. Each chapter consists of 5-10 sections. For example, types of data, data collection, probability, normal model, confidence intervals and inference for single proportions. The later chapters on inferences and regression (chapters 4-8) are built upon the former chapters (chapters 1-3). The student-facind end, while not flashy or gamified in any way, is easy to navigate and clear. 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 Tables and graphs are sensibly annotated and well organized. 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. Also, I had some issues finding terms in the index. The most accurate open-source textbook in statistics I have found. No problems, but again, the text is a bit dense. Display of graphs and figures is good, as is the use of color. I did not view an material that I felt would be offensive. Share. 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. 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. Search inside document . Then, the basics of both hypothesis tests and confidence intervals are covered in one chapter. Comes in pdf, tablet friendly pdf, and printed (15 dollars from amazon as of March, 2019). The content that this book focuses on is relatively stable and so changes would be few and far between. None of the examples seemed alarming or offensive. . 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 In some instances, various groups of students may be directed to certain chapters, while others hone in on that material relevant to their topic. Overall, this is a well written book for introductory level statistics. The probability section uses a data set on smallpox to discuss inoculation, another relevant topic whose topic set could be easily updated. 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. read more. 191 and 268). Mine Cetinkaya-Rundel is the Director of Undergraduate Studies and Assistant Professor of the Practice in the Department of Statistical Science at Duke University. At The book covers familiar topics in statistics and quantitative analysis and the presentation of the material is accurate and effective. The book uses relevant topics throughout that could be quickly updated. The text is easily reorganized and re-sequenced. Examples of how statistics can address gender bias were appreciated. There are a few color splashes of blue and red in diagrams or URL's. The distinction and common ground between standard deviation and standard error needs to be clarified. The text is easily and readily divisible into subsections. I have seen other texts begin with correlation and regression prior to tests of means, etc., and wonder which approach is best. The book is divided into many subsections. Some examples of this include the discussion of anecdotal evidence, bias in data collection, flaws in thinking using probability and practical significance vs statistical significance. The graphs and tables in the text are well designed and accurate. It defines terms, explains without jargon, and doesnt skip over details. The organization for each chapter is also consistent. Well, this text provides a kinder and gentler introduction to data analysis and statistics. 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. 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). Overall it was not offensive to me, but I am a college-educated white guy. 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 The reader can jump to each chapter, exercise solutions, data sets within the text, and distribution tables very easily. On occasion, all of us in academia have experienced a text where the progression from one chapter to another was not very seamless. I didn't experience any problems. These graphs and tables help the readers to understand the materials well, especially most of the graphs are colored figures. Getting Started Amazon links on openintro.org or in products are affiliate links. The color graphics come through clearly and the embedded links work as they should. 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). 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 is a bit of coverage on logistic regression appropriate for categorical (specifically, dichotomous) outcome variables that usually is not part of a basic introduction. Reviewed by Monte Cheney, Associate Professor of Mathematics, Central Oregon Community College on 8/21/16, More depth in graphs: histograms especially. This is a free textbook for a one-semester, undergraduate statistics course. 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 introduction to linear regression. The material in the book is currently relevant and, given the topic, some of it will never be irrelevant. Calculations by hand are not realistic. If the main goal is to reach multiple regression (Chapter 9 ) as quickly as possible, then the following are the ideal prerequisites: Chapter 1 , Sections 2.1 , and Section 2.2 for a solid introduction to data structures and statis- tical summaries that are used . Introductory statistics courses prepare students to think statistically but cover relatively few statistical methods. The text needs real world data analysis examples from finance, business and economics which are more relevant to real life. OpenIntro Statistics - 4th Edition - Solutions and Answers | Quizlet Math Probability OpenIntro Statistics 4th Edition ISBN: 9781943450077 Christopher Barr, David Diez, Mine etinkaya-Rundel Sorry! The approach of introducing the inferences of proportions and the Chi-square test in the same chapter is novel. It is certainly a fitting means of introducing all of these concepts to fledgling research students. Overall the organization is good, so I'm still rating it high, but individual instructors may disagree with some of the order of presentation. I did not notice any culturally sensitive examples, and no controversial or offensive examples for the reader are presented. This was not necessarily the case with some of the tables in the text. The content is up-to-date. For instance, the text shows students how to calculate the variance and standard deviation of an observed variable's distribution, but does not give the actual formula. Most essential materials for an introductory probability and statistics course are covered. The examples are up-to-date. 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. I viewed the text as a PDF and was pleasantly surprised at the clarity the fluid navigation that is not the norm with many PDFs. The text is written in lucid, accessible prose, and provides plenty of examples for students to understand the concepts and calculations. These updates would serve to ensure the connection between the learner and the material that is conducive to learning. More extensive coverage of contingency tables and bivariate measures of association would be helpful. This is important since examples used authentic situations to connect to the readers. For examples, the distinction between descriptive statistics and inferential statistics, the measures of central tendency and dispersion. read more. 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. One of the strengths of this text is the use of motivated examples underlying each major technique. The first chapter addresses treatments, control groups, data tables and experiments. One of the good topics is the random sampling methods, such as simple sample, stratified, cluster, and multistage random sampling methods. The cons are that the depth is often very light, for example, it would be difficult to learn how to perform simple or multiple regression from this book. Probability is optional, inference is key, and we feature real data whenever . read more. Words like "clearly" appear more than are warranted (ie: ever). Also, non-parametric alternatives would be nice, especially Monte Carlo/bootstrapping methods. I did not see any inaccuracies in the book. The consistency of this text is quite good. The text covers the foundations of data, distributions, probability, regression principles and inferential principles with a very broad net. Most contain glaring conceptual and pedagogical errors, and are painful to read (don't get me started on percentiles or confidence intervals). This is a particular use of the text, and my students would benefit from and be interested in more social-political-economic examples. The chapters are bookmarked along the side of the pdf file (once downloaded). One of the real strengths of the book is that it is nicely separated into coherent chapters and instructors would will have no trouble picking and choosing among them. The texts includes basic topics for an introductory course in descriptive and inferential statistics. As an example, I suggest the text provides data analysis by using Binomial option pricing model and Black-Scholes option pricing model. Use of the t-distribution is motivated as a way to "resolve the problem of a poorly estimated standard error", when really it is a way to properly characterize the distribution of a test statistic having a sample-based standard error in the denominator. 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. I do wonder about accessibility (for blind or deaf/HoH students) in this book since I don't see it clearly addressed on the website. The structure and organization of this text corresponds to a very classic treatment of the topic. This topic is usually covered in the middle of a textbook. While it would seem that the data in a statistics textbook would remain relevant forever, there are a few factors that may impact such a textbook's relevance and longevity. 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