As in many/most statistics texts, it is a challenge to understand the authors' distinction between "standard deviation" and "standard error". This book can work in a number of ways. The sections seem easily labeled and would make it easy to skip particular sections, etc. 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. The writing could be slightly more inviting, and concept could be more readily introduced via accessible examples more often. I often assign reading and homework before I discuss topics in lecture. 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. The overall organization of the text is logical. 191 and 268). No display issues with the devices that I have. Sample Solutions for this Textbook We offer sample solutions for OPENINTRO:STATISTICS homework problems. David M. Diez, Mine etinkaya-Rundel, Christopher D. Barr . Another example that would be easy to update and is unlikely to become non-relevant is email and amount of spam, used for numerous topics. The text begins with data collection, followed by probability and distributions of a random variable and then finishing (for a Statistics I course) with inference. The pdf is likely accessible for screen readers, though. It has scientific examples for the topics so they are always in context. 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. The chapters are bookmarked along the side of the pdf file (once downloaded). The book provides an effective index. The presentation is professional with plenty of good homework sets and relevant data sets and examples. There are a few color splashes of blue and red in diagrams or URL's. It is certainly a fitting means of introducing all of these concepts to fledgling research students. The text is free of significant interface issues. The material was culturally relevant to the demographic most likely to use the text in the United State. 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). My biggest complaint is that one-sided tests are basically ignored. Although there are some materials on experimental and observational data, this is, first and foremost, a book on mathematical and applied statistics. Another welcome topic that is not typical of introductory texts is logistic regression, which I have seen many references to in the currently hot topic of Data Science. The index and table of contents are clear and useful. Also, the discussion on hypothesis testing could be more detailed and specific. Reviewed by Monte Cheney, Associate Professor of Mathematics, Central Oregon Community College on 8/21/16, More depth in graphs: histograms especially. I see essentially no errors in this book. The book is broken into small sections for each topic. This textbook did not contain much real world application data sets which can be a draw back on its relevance to today's data science trend. 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. There do not appear to be grammatical errors. The text is organized into sections, and the numbering system within each chapter facilitates assigning sections of a chapter. It begins with the basics of descriptive statistics, probability, hypothesis test concepts, tests of numerical variables, categorical, and ends with 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. The text offered quite a lot of examples in the medical research field and that is probably related to the background of the authors. The supplementary material for this book is excellent, particularly if instructors are familiar with R and Latex. 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. There are also pictures in the book and they appear clear and in the proper place in the chapters. the U.K., they may not be the best examples that could be used to connect with those from non-western countries. by David Diez, Mine Cetinkaya-Rundel, Christopher Barr. The text is well-written and with interesting examples, many of which used real data. The text is quite consistent in terms of terminology and framework. The examples flow nicely into the guided practice problems and back to another example, definition, set of procedural steps, or explanation. 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. Also, for how the authors seem to be focusing on practicalities, I was somewhat surprised about some of the organization of the inference sections. Overall, I recommend this book for an introductory statistics course, however, it has some advanced topics. I also appreciated that the authors use examples from the hard sciences, life sciences, and social sciences. read more. The book is very consistent from what I can see. Navigation as a PDF document is simple since all chapters and subsection within the table of contents are hyperlinked to the respective section. This selection of topics and their respective data sets are layered throughout the book. 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. 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. 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. This easily allow for small sets of reading on a class to class basis or larger sets of reading over a weekend. The text is accurate due to its rather straight forward approach to presenting material. The content of the book is accurate and unbiased. 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). Appendix A contains solutions to the end of chapter exercises. The subsequent chapters have all of the specifics about carrying out hypothesis tests and calculating intervals for different types of data. Adv. The only visual issues occurs in some graphs, such as on page 40-41, which have maps of the U.S. using color to show intensity. Also, grouping confidence intervals and hypothesis testing in Ch.5 is odd, when Ch.7 covers hypothesis testing of numerical data. Black and white paperback edition. Print. OpenIntro Statistics. There are also a number of exercises embedded in the text immediately after key ideas and concepts are presented. I did not find any grammatical errors that impeded meaning. The examples are up-to-date. It appears to stick to more non-controversial examples, which is perhaps more effective for the subject matter for many populations. 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. 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. Then, the basics of both hypothesis tests and confidence intervals are covered in one chapter. The text provides enough examples, exercises and tips for the readers to understand the materials. 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). read more. 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. The later chapters on inferences and regression (chapters 4-8) are built upon the former chapters (chapters 1-3). As a mathematician, I find this book most readable, but I imagine that undergraduates might become somewhat confused. In fact, I could not differentiate a change in style or clarity in any sections of this text. At first when reviewing, I found it to be difficult for to quickly locate definitions and examples and often focus on the material. The text book contains a detailed table of contents, odd answers in the back and an index. I think it would be better to group all of the chapter's exercises until each section can have a greater number of exercises. This could be either a positive or a negative to individual instructors. This topic is usually covered in the middle of a textbook. For examples, the distinction between descriptive statistics and inferential statistics, the measures of central tendency and dispersion. It does a more thorough job than most books of covering ideas about data, study design, summarizing data and displaying data. For example, a goodness of fit test begins by having readers consider a situation of whether or not the ethnic representation of a jury is consistent with the ethnic representation of the area. 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. This text will be useful as a supplement in the graduate course in applied statistics for public service. Each topic builds on the one before it in any statistical methods course. 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. Errors that impeded meaning ) are built upon the former chapters ( chapters 4-8 ) are upon. Text offered quite a lot of examples in the medical research field that! 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