I did not notice any culturally sensitive examples, and no controversial or offensive examples for the reader are presented. This book is very readable. None. It covers all the standard topics fully. It definitely makes the students more comfortable with learning a new test because its just the same thing with different statistics. Overall, the book is heavy on using ordinary language and common sense illustrations to get across the main ideas. The Guided Practice problems allow students to try a problem with the solution in the footnote at the bottom. The authors do a terrific job in chapter 1 introducing key ideas about data collection, sampling, and rudimentary data analysis. This open access textbook provides the background needed to correctly use, interpret and understand statistics and statistical data in diverse settings. 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 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. Introduction It should be pointed out that logistic regression is using a logistic function to model a binary dependent variable. The book provides an effective index. The overall organization of the text is logical. 325 and 357). Most contain glaring conceptual and pedagogical errors, and are painful to read (don't get me started on percentiles or confidence intervals). Overall it was not offensive to me, but I am a college-educated white guy. The authors make effective use of graphs both to illustrate the subject matter and to teach students how to construct and interpret graphs in their own work. I wish they included measures of association for categorical data analysis that are used in sociology and political science, such as gamma, tau b and tau c, and Somers d. Finally, I think the book needs to add material on the desirable properties of statistical estimators (i.e., unbiasedness, efficiency, consistency). Join Free Today Chapters 1 Introduction to Data 4 sections 60 questions RK 2 Summarizing data 3 sections 26 questions RK 3 Probability 5 sections 47 questions These concepts are reinforced by authentic examples that allow students to connect to the material and see how it is applied in the real world. Things flow together so well that the book can be used as is. Most essential materials for an introductory probability and statistics course are covered. In particular, examples and datasets about county characteristics, elections, census data, etc, can become outdated fairly quickly. Print. I think that the book is fairly easy to read. The texts includes basic topics for an introductory course in descriptive and inferential statistics. 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. Well, this text provides a kinder and gentler introduction to data analysis and statistics. As aforementioned, the authors gently introduce students to very basic statistical concepts. The text is well-written and with interesting examples, many of which used real data. Table. Overall, the text is well-written and explained along with real-world data examples. 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. The examples are up-to-date, but general enough to be relevant in years to come or formatted appropriately so that, if necessary, they may be easily replaced. Each chapter contains short sections and each section contains small subsections. I did not see any inaccuracies in the book. Overall, this is a well written book for introductory level statistics. 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. Some examples in the text are traditional ones that are overused, i.e., throwing dice and drawing cards to teach probability. And, the authors have provided Latex code for slides so that instructors can customize the slides to meet their own needs. Some of the more advanced topics are treated as 'special topics' within the sections (e.g., power and standard error derivations). This book covers almost all the topics needed for an introductory statistics course from introduction to data to multiple and logistic regression models. 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. Some topics in descriptive statistics are presented without much explanation, such as dotplots and boxplots. No display issues with the devices that I have. One of the strengths of this text is the use of motivated examples underlying each major technique. OpenIntro Statistics covers a first course in statistics, providing a rigorous introduction to appliedstatistics that is clear, concise, and accessible. The writing in this book is above average. 2019, 422 pages. These blend well with the Exercises that contain the odd solutions at the end of the text. It recognizes the prevalence of technology in statistics and covers reading output from software. David M. Diez, Mine etinkaya-Rundel, Christopher D. Barr . Nothing was jarring in this aspect, and the sections/chapters were consistent. 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. The subsequent chapters have all of the specifics about carrying out hypothesis tests and calculating intervals for different types of data. 0% 0% found this document useful, Mark this document as useful. I believe students, as well as, instructors would find these additions helpful. read more. 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. 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. Normal approximations are presented as the tool of choice for working with binomial data, even though exact methods are efficiently implemented in modern computer packages. Although accurate, I believe statistics textbooks will increasingly need to incorporate non-parametric and computer-intensive methods to stay relevant to a field that is rapidly changing. The material was culturally relevant to the demographic most likely to use the text in the United State. The content is accurate in terms of calculations and conclusions and draws on information from many sources, including the U.S. Census Bureau to introduce topics and for homework sets. Reviewed by Elizabeth Ward, Assistant Professor , James Madison University on 3/11/19, 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). 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. 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. openintro statistics fourth edition open textbook library . The writing is clear, and numerous graphs and examples make concepts accessible to students. The formatting and interface are clear and effective. It is certainly a fitting means of introducing all of these concepts to fledgling research students. The book covers the essential topics in an introductory statistics course, including hypothesis testing, difference of means-tests, bi-variate regression, and multivariate regression. But, when you understand the strengthsand weaknesses of these tools, you can use them to learn about the world. There are two drawbacks to the interface. Overall, I would consider this a decent text for a one-quarter or one-semester introductory statistics textbook. The text covers all the core topics of statisticsdata, probability and statistical theories and tools. The content is up-to-date. I find the content to be quite relevant. This text does indicate that some topics can be omitted by identifying them as 'special 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. 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 I didn't experience any problems. There were some author opinions on such things as how to go about analyzing the data and how to determine when a test was appropriate, but those things seem appropriate to me and are welcome in providing guidance to people trying to understand when to choose a particular statistical test or how to interpret the results of one. OpenIntro Statistics supports flexibility in choosing and ordering topics. The authors use a method inclusive of examples (noted with a Blue Dot), guided practice (noted by a large empty bullet), and exercises (found at end of each chapter). Reviewed by Monte Cheney, Associate Professor of Mathematics, Central Oregon Community College on 8/21/16, More depth in graphs: histograms especially. The section on model selection, covering just backward elimination and forward selection, seems especially old-fashioned. The rationale for assigning topics in Section 1 and 2 is not clear. There are lots of great exercises at the end of each chapter that professors can use to reinforce the concepts and calculations appearing in the chapter. The discussion of data analysis is appropriately pitched for use in introductory quantitative analysis courses in a variety of disciplines in the social sciences . It is difficult for a topic that in inherently cumulative to excel at modularity in the manner that is usually understanding. Statistics and Probability Statistics and Probability solutions manuals OpenIntro Statistics 4th edition We have solutions for your book! The examples and solutions represent the information with formulas and clear process. 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). We don't have content for this book yet. The examples are general and do not deal with racial or cultural matters. 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. I value the unique organization of chapters, the format of the material, and the resources for instructors and students. Errors are not found as of yet. This may allow the reader to process statistical terminology and procedures prior to learning about regression. Each topic builds on the one before it in any statistical methods course. Examples from a variety of disciplines are used to illustrate the material. This book was written with the undergraduate level in mind, but it's also popular in high schools and graduate courses. More color, diagrams, etc.? Save Save Solutions to Openintro Statistics For Later. The learner cant capture what is logistic regression without a clear definition and explanation. It is clear that the largest audience is assumed to be from the United States as most examples draw from regions in the U.S. Chapter 2 covers the knowledge of probabilities including the definition of probability, Law of Large Numbers, probability rules, conditional probability and independence and linear combinations of random variables. Appendix A contains solutions to the end of chapter exercises. Within each appears an adequate discussion of underlying assumptions and a representative array of applications. The presentation is professional with plenty of good homework sets and relevant data sets and examples. I found the book to be very comprehensive for an undergraduate introduction to statistics - I would likely skip several of the more advanced sections (a few of these I mention below in my comments on its relevance) for this level, but I was glad to see them included. There are also short videos for 75% of the book sections that are easy to follow and a plus for students. read more. The real data sets examples cover different topics, such as politics, medicine, etc. One of the real strengths of the book is the many examples and datasets that it includes. 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. Single proportion, two proportions, goodness of fit, test for independence and small sample hypothesis test for proportions. The examples for tree diagrams are very good, e.g., small pox in Boston, breast cancer. Given that this is an introductory textbook, it is clearly written and accessible to students with a variety of disciplinary backgrounds. 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. To many texts that cover basic theory are organized as theorem/proof/example which impedes understanding of the beginner. I found the overall structure to be standard of an introductory statistics course, with the exception of introducing inference with proportions first (as opposed to introducing this with means first instead). It is certainly a fitting means of introducing all of these concepts to fledgling research students. 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. Reviewed by Paul Murtaugh, Associate Professor, Oregon State University on 7/15/14, The text has a thorough introduction to data exploration, probability, statistical distributions, and the foundations of inference, but less complete discussions of specific methods, including one- and two-sample inference, contingency tables, and For example, income variations in two cities, ethnic distribution across the country, or synthesis of data from Africa. In addition all of the source code to build the book is available so it can be easily modified. My biggest complaint is that one-sided tests are basically ignored. Access even-numbered exercise solutions. 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. The pdf and tablet pdf have links to videos and slides. Characteristics, elections, census data, etc, can become outdated fairly quickly reader! Are traditional ones that are overused, i.e., throwing dice and drawing cards to teach.! Introductory quantitative analysis courses in a variety openintro statistics 4th edition solutions quizlet disciplinary backgrounds in statistics, providing a rigorous introduction to data multiple. The writing is clear, concise, and no controversial or offensive examples for the reader presented... Cant capture what is logistic regression is using a logistic function to a. Output from software this document useful, Mark this document useful, Mark this document useful, Mark document. Easy to read disciplinary backgrounds instructors can customize the slides to meet their own needs text the. The jade in introductory quantitative analysis courses in a variety of disciplinary.... ' within the sections ( e.g., power and standard error derivations ) there are also short videos for %... Each topic builds on the one before it in any statistical methods course that instructors can customize the to. 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