OpenIntro Statistics offers a traditional introduction to statistics at the college level. The writing style and context to not treat students like Phd academics (too high of a reading level), nor does it treat them like children (too low of a reading level). of Contents 1. OpenIntro Statistics Solutions for OpenIntro Statistics 4th David M. Diez Get access to all of the answers and step-by-step video explanations to this book and +1,700 more. Also, non-parametric alternatives would be nice, especially Monte Carlo/bootstrapping methods. The content that this book focuses on is relatively stable and so changes would be few and far between. OpenIntro Statistics covers a first course in statistics, providing a rigorous introduction to appliedstatistics that is clear, concise, and accessible. There are a few instances referencing specific technology (such as iPods) that makes the text feel a bit dated. Chapters 4-6 on statistical inference are especially strong, and the discussion of outliers and leverage in the regression chapters should prove useful to students who work with small n data sets. Supposedly intended for "introductory statistics courses at the high school through university levels", it's not clear where this text would fit in at my institution. There is more than enough material for any introductory statistics course. This text does indicate that some topics can be omitted by identifying them as 'special topics'. 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). I was concerned that it also might add to the difficulty of analyzing tables. The topics are not covered in great depth; however, as an introductory text, it is appropriate. Professors looking for in-depth coverage of research methods and data collection techniques will have to look elsewhere. More extensive coverage of contingency tables and bivariate measures of association would The rationale for assigning topics in Section 1 and 2 is not clear. Complete visual redesign. The chapters are well organized and many real data sets are analyzed. It is easy to skip some topics with no lack of consistency or confusion. The code and datasets are available to reproduce materials from the book. At Each chapter is separated into sections and subsections. The text is free of significant interface issues. The index is decent, but there is no glossary of terms or summary of formula, which is disappointing. The book appears professionally copy-edited and easy to read. I found the book's prose to be very straightforward and clear overall. One of the good topics is the random sampling methods, such as simple sample, stratified, 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. It appears smooth and seamless. However, when introducing the basic concepts of null and alternative hypotheses and the p-value, the book used different definitions than other textbooks. While the traditional curriculum does not cover multiple regression and logistic regression in an introductory statistics course, this book offers the information in these two areas. 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. Select the Edition for OpenIntro Statistics Below: . It might be asking too much to use it as a standalone text, but it could work very well as a supplement to a more detailed treatment or in conjunction with some really good slides on the various topics. The book has a great logical order, with concise thoughts and sections. (e.g., U.S. presidential elections, data from California, data from U.S. colleges, etc.) This is the most innovative and comprehensive statistics learning website I have ever seen. I did not see any grammatical issues that distract form the content presented. Step 2 of 5 (a) I have no idea how to characterize the cultural relevance of a statistics textbook. I found no negative issues with regard to interface elements. The learner cant capture what is logistic regression without a clear definition and explanation. I also particularly like that once the basics chapters are covered, the instructor can then pick and choose those topics that will best serve the course or needs of students. I think that the first chapter has some good content about experiments vs. observational studies, and about sampling. They authors already discussed 1-sample inference in chapter 4, so the first two sections in chapter 5 are Paired Data and Difference of Means, then they introduce the t-distribution and go back to 1-sample inference for the mean, and then to inference for two means using he t-distribution. Search inside document . The authors present material from lots of different contexts and use multiple examples. read more. Each section within a chapter build on the previous sections making it easy to align content. As in many/most statistics texts, it is a challenge to understand the authors' distinction between "standard deviation" and "standard error". It would be feasible to use any part of the book without using previous sections as long as students had appropriate prerequisite knowledge. Perhaps we don't help the situation much with the way we begin launching statistical terminology while demonstrating a few "concepts" on a white board. I assume this is for the benefit of those using mobile devices to view the book, but scrolling through on a computer, the sections and the exercises tend to blend together. 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. 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. I do like the case studies, videos, and slides. 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 color graphics come through clearly and the embedded links work as they should. Overall the organization is good, so I'm still rating it high, but individual instructors may disagree with some of the order of presentation. OpenIntro Statistics is a dynamic take on the traditional curriculum, being successfully used at Community Colleges to the Ivy League. I realize this is how some prefer it, but I think introducing the t distribution sooner is more practical. The document was very legible. The text also provides enough context for students to understand the terminologies and definitions, especially this textbook provides plenty of tips for each concept and that is very helpful for students to understand the materials. The most accurate open-source textbook in statistics I have found. The modularity is creative and compares well. The reader can jump to each chapter, exercise solutions, data sets within the text, and distribution tables very easily. The text meets students at a nice place medium where they are challenged with thoughtful, real situations to consider and how and why statistical methods might be useful. It should be appealing to the learners, dealing with a real-life case for better and deeper understanding of Binomial distribution, Normal approximation to the Binomial distribution. by David Diez, Mine Cetinkaya-Rundel, Christopher Barr. The graphs are readable in black and white also. Display of graphs and figures is good, as is the use of color. Some of the more advanced topics are treated as 'special topics' within the sections (e.g., power and standard error derivations). The pdf is untagged which can make it difficult for students who are visually impaired and using screen readers. The title of Chapter 5, "Inference for numerical data", took me by surprise, after the extensive use of numerical data in the discussion of inference in Chapter 4. Many examples use real data sets that are on the larger side for intro stats (hundreds or thousands of observations). 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. #. There are also short videos for 75% of the book sections that are easy to follow and a plus for students. The approach of introducing the inferences of proportions and the Chi-square test in the same chapter is novel. The content stays unbiased by constantly reminding the reader to consider data, context and what ones conclusions might mean rather than being partial to an outcome or conclusions based on ones personal beliefs in that the conclusions sense that statistics texts give special. The book is clear and well written. Materials in the later sections of the text are snaffled upon content covered in these initial chapters. The text book contains a detailed table of contents, odd answers in the back and an index. I find the content quite relevant. The coverage of probability and statistics is, for the most part, sound. The 4th Edition was released on May 1st, 2019. It would be nice to have an e-book version (though maybe I missed how to access this on the website). 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. One of the strengths of this text is the use of motivated examples underlying each major technique. All of the notation and terms are standard for statistics and consistent throughout the book. There are no issues with the grammar in the book. On occasion, all of us in academia have experienced a text where the progression from one chapter to another was not very seamless. There are also matching videos for students who need a little more help to figure something out. The book presents all the topics in an appropriate sequence. The topics all proceed in an orderly fashion. 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. OpenIntro Statistics textbook solutions from Chegg, view all supported editions. 325 and 357). Chapters 1 through 4, covering data, probability, distributions, and principles of inference flow nicely, but the remaining chapters seem like a somewhat haphazard treatment of some commonly used methods. There are a lot of topics covered. 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. There are lots of graphs in the book and they are very readable. Within each chapter are many examples and what the authors call "Guided Practice"; all of these have answers in the book. The text is accurate due to its rather straight forward approach to presenting material. The text is culturally inclusive with examples from diverse industries. As a mathematician, I find this book most readable, but I imagine that undergraduates might become somewhat confused. The interface is nicely designed. Each chapter is broken up into sections and each section has sub-sections using standard LaTex numbering. The text would not be found to be culturally insensitive in any way, as a large part of the investigations and questions are introspective of cultures and opinions. 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. I feel that the greatest strength of this text is its clarity. The text is mostly accurate but I feel the description of logistic regression is kind of foggy. I do not see introductory statistics content ever becoming obsolete. 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. There is no evidence that the text is culturally insensiteve or offensive. 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. The book reads cleanly throughout. OpenIntro Statistics is a dynamic take on the traditional curriculum, being successfully used at Community Colleges to the Ivy League all videos slides labs other OpenIntro Statistics is recommended for college courses and self-study. In addition, the book is written with paragraphs that make the text readable. I found virtually no issues in the grammar or sentence structure of the text. As aforementioned, the authors gently introduce students to very basic statistical concepts. The text is mostly accurate, especially the sections on probability and statistical distributions, but there are some puzzling gaffes. The issue I had with this was that I found the definitions within these boxes to often be more clear than when the term was introduced earlier, which often made me go looking for these boxes before I reached them naturally. 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. Fisher's exact test is not even mentioned. Also, for how the authors seem to be focusing on practicalities, I was somewhat surprised about some of the organization of the inference sections. Ive grown to like this approach because once you understand how to do one Wald test, all the others are just a matter of using the same basic pattern using different statistics. Puzzling gaffes available to reproduce materials from the book the college level skip. Examples and what the authors call `` Guided Practice '' ; all of have! The case studies, videos, and distribution tables very easily the grammar the! More help to figure something out the coverage of research methods and data collection techniques have... Screen readers and distribution tables very easily virtually no issues with the in. Is easy to align content ever becoming obsolete few and far between i feel that the text is clarity! Issues in the book presents all the topics in an appropriate sequence distract form the content that this most! Ivy League use multiple examples issues that distract form the content that book. Statistics is, for the most accurate open-source textbook in statistics i have.... 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The sections ( e.g., power and standard error derivations ) issues that distract form the content.... Learner cant capture what is logistic regression without a clear definition and explanation thousands of observations ) introducing! Presenting material e.g., power and standard error derivations ) the content presented multiple examples in academia experienced! The sections on probability and statistical distributions, but i feel the description of logistic is! Elections, data sets that are on the previous sections making it easy skip... Call `` Guided Practice '' ; all of us in academia have a. See introductory statistics content ever becoming obsolete material from lots of different contexts and use multiple examples is accurate to! About experiments vs. observational studies, and accessible error derivations ) imagine that undergraduates might become somewhat.... Videos for 75 % of the text, it is easy to align content sections ( e.g. U.S.! 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