Lots of good graphics and referenced data sets, but not much discussion or inclusion of prevailing software such as R, SPSS, Minitab, or free online packages. 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. The chapters are well organized and many real data sets are analyzed. This is similar to many other textbooks, but since there are generally fewer section exercises, they are easy to miss when scrolling through, and provide less selection for instructors. This selection of topics and their respective data sets are layered throughout the book. the U.K., they may not be the best examples that could be used to connect with those from non-western countries. The content of the book is accurate and unbiased. The definitions and procedures are clear and presented in a framework that is easy to follow. 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. There are exercises at the end of each chapter (and exercise solutions at the end of the text). Reminder: the 4th Edition is the newest edition. Any significant rearranging of those sections would be incredibly detrimental to the reader, but that is true of any statistics textbook, especially at the introductory level: Earlier concepts provide the basis for later concepts. Navigation as a PDF document is simple since all chapters and subsection within the table of contents are hyperlinked to the respective section. The chapter summaries are easy to follow and the order of the chapters begin with "Introduction to Data," which includes treatment I was able to read the entire book in about a month by knocking out a couple of subsections per day. Also, I had some issues finding terms in the index. The supplementary material for this book is excellent, particularly if instructors are familiar with R and Latex. However, there are a few instances where he/she are used to refer to a "theoretical person" rather than using they/them, Reviewed by Alice Brawley Newlin, Assistant Professor, Gettysburg College on 3/31/20, 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 I realize this is how some prefer it, but I think introducing the t distribution sooner is more practical. Reviewed by Denise Wilkinson, Professor of Mathematics, Virginia Wesleyan University on 4/20/21, This text book covers most topics that fit well with an introduction statistics course and in a manageable format. This is important since examples used authentic situations to connect to the readers. I read the physical book, which is easy to navigate through the many references. These concepts should be clarified at the first chapter. In fact, I could not differentiate a change in style or clarity in any sections of this text. The colors of the font and tables in the textbook are mostly black and white. Intro Stats - 4th Edition - Solutions and Answers | Quizlet Statistics Intro Stats 4th Edition ISBN: 9780321825278 David E. Bock, Paul Velleman, Richard D. De Veaux Textbook solutions Verified Chapter 1: Stats Start Here Exercise 1 Exercise 2 Exercise 3 Exercise 4 Exercise 5 Exercise 6 Exercise 7 Exercise 8 Exercise 9 Exercise 10 Exercise 11 Embed. The authors also make GREAT use of statistical graphics in all the chapters. No grammatical errors have been found as of yet. Similar to most intro stat books, it does not cover the Bayesian view at all. The pdf is untagged which can make it difficult for students who are visually impaired and using screen readers. The book is written as though one will use tables to calculate, but there is an online supplement for TI-83 and TI-84 calculator. It has scientific examples for the topics so they are always in context. 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. No issues with consistency in that text are found. The examples are general and do not deal with racial or cultural matters. The writing could be slightly more inviting, and concept could be more readily introduced via accessible examples more often. My biggest complaint is that The text covers all the core topics of statisticsdata, probability and statistical theories and tools. Our inaugural effort is OpenIntro Statistics. 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. 2017 Generation of Electrical Energy is written primarily for the undergraduate students of electrical engineering while also covering the syllabus of AMIE and act as a The lack of discussion/examples/inclusion of statistical software or calculator usage is disappointing, as is the inclusion of statistical inference using critical values. This text book covers most topics that fit well with an introduction statistics course and in a manageable format. 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. The graphs are readable in black and white also. This book is easy to follow and the roadmap at the front for the instructor adds additional ease. Tables and graphs are sensibly annotated and well organized. Perhaps an even stronger structure would see all the types of content mentioned above applied to each type of data collection. 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 authors use the Z distribution to work through much of the 1-sample inference. There are also pictures in the book and they appear clear and in the proper place in the chapters. This will increase the appeal of the text. The section on model selection, covering just backward elimination and forward selection, seems especially old-fashioned. read more. 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 The index and table of contents are clear and useful. There are chapters and sections that are optional. However, to meet the needs of this audience, the book should include more discussion of the measurement key concepts, construction of hypotheses, and research design (experiments and quasi-experiments). The writing in this book is very clear and straightforward. I also appreciated that the authors use examples from the hard sciences, life sciences, and social sciences. I think that the first chapter has some good content about experiments vs. observational studies, and about sampling. This textbook is nicely parsed. The structure and organization of this text corresponds to a very classic treatment of the topic. read more. Everything appeared to be accurate. None of the examples seemed alarming or offensive. Building on the basic statistical thinking emphasized in an introductory course, a second course in statistics at the undergraduate level can explore a large number of statistical methods. Though I might define p-values and interpret confidence intervals slightly differently. Search inside document . However, it would not suffice for our two-quarter statistics sequence that includes nonparametrics. This can be particularly confusing to "beginners.". 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). 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. The book has relevant and easily understood scientific questions. 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 was fairly consistent in its use of terminology. The approach is mathematical with some applications. The text is written in lucid, accessible prose, and provides plenty of examples for students to understand the concepts and calculations. As the trend of analysis, students will be confronted with the needs to use computer software or a graphing calculator to perform the analyses. The learner cant capture what is logistic regression without a clear definition and explanation. 191 and 268). The organization is fine. As an example, I suggest the text provides data analysis by using Binomial option pricing model and Black-Scholes option pricing model. 2019, 422 pages. More color, diagrams, photos? There are some things that should probably be included in subsequent revisions. It also offered enough graphs and tables to facilatate the reading. An interesting note is that they introduce inference with proportions before inference with means. It should be pointed out that logistic regression is using a logistic function to model a binary dependent variable. 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. Examples stay away from cultural topics. read more. Extra Content. In particular, the malaria case study and stokes case study add depth and real-world meaning to the topics covered, and there is a thorough coverage of distributions. Save Save Solutions to Openintro Statistics For Later. The authors bold important terms, and frequently put boxes around important formulas or definitions. 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 the best open-source statistics text I have reviewed. This open access textbook provides the background needed to correctly use, interpret and understand statistics and statistical data in diverse settings. The formatting and interface are clear and effective. 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, I often assign reading and homework before I discuss topics in lecture. (Unlike many modern books that seem to have random sentences scattered in between bullet points and boxes.). This textbook is widely used at the college level and offers an exceptional and accessible introduction for students from community colleges to the Ivy League. Appendix A contains solutions to the end of chapter exercises. For example, the Central Limit Theorem is introduced and used early in the inference section, and then later examined in more detail. And why dump Ch.6 in between with hypothesis testing of categorical data between them? After much searching, I particularly like the scope and sequence of this textbook. Reviewed by Darin Brezeale, Senior Lecturer, University of Texas at Arlington on 1/21/20, 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 The resources, such as labs, lecture notes, and videos are good resources for instructors and students as well. The book has a great logical order, with concise thoughts and sections. There are labs and instructions for using SAS and R as well. read more. It includes too much theory for our undergraduate service courses, but not enough practical details for our graduate-level service courses. There are a few instances referencing specific technology (such as iPods) that makes the text feel a bit dated. The material was culturally relevant to the demographic most likely to use the text in the United State. A teacher can sample the germane chapters and incorporate them without difficulty in any research methods class. Many examples use real data sets that are on the larger side for intro stats (hundreds or thousands of observations). 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). There are a lot of topics covered. Then, the basics of both hypothesis tests and confidence intervals are covered in one chapter. The content that this book focuses on is relatively stable and so changes would be few and far between. This defect is not present here: this text embraces an 'embodied' view of learning which prioritizes example applications first and then explanation of technique. OpenIntro Statistics - 4th Edition - Solutions and Answers | Quizlet Math Probability OpenIntro Statistics 4th Edition ISBN: 9781943450077 Christopher Barr, David Diez, Mine etinkaya-Rundel Sorry! 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. 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). Display of graphs and figures is good, as is the use of color. It is accurate. Ensure every student can access the course textbook. This is especially true when there are multiple authors. 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. OpenIntro Statistics 4th Edition by David Diez, Christopher Barr, Mine etinkaya-Rundel: 250: Join Chegg Study and get: Guided textbook solutions created by . Share. The text covers the foundations of data, distributions, probability, regression principles and inferential principles with a very broad net. At While section are concise they are not limited in rigor or depth (as exemplified by a great section on the "power" of a hypothesis test) and numerous case studies to introduce topics. The book started with several examples and case study to introduce types of variables, sampling designs and experimental designs (chapter 1). The topics are not covered in great depth; however, as an introductory text, it is appropriate. It's very fitting for my use with teachers whose primary focus is on data analysis rather than post-graduate research. Nothing was jarring in this aspect, and the sections/chapters were consistent. I do not see introductory statistics content ever becoming obsolete. The chapters are bookmarked along the side of the pdf file (once downloaded). 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. However, classical measures of effect such as confidence intervals and R squared appear when appropriate though they are not explicitly identified as measures of effect. Within each chapter are many examples and what the authors call "Guided Practice"; all of these have answers in the book. Many OERs (and published textbooks) are difficult to convert from a typical 15-week semester to a 10-week term, but not this one! 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. All of the notation and terms are standard for statistics and consistent throughout the book. It would be feasible to use any part of the book without using previous sections as long as students had appropriate prerequisite knowledge. Better than most of the introductory book that I have used thus far (granted, my books were more geared towards engineers). #. These blend well with the Exercises that contain the odd solutions at the end of the text. The rationale for assigning topics in Section 1 and 2 is not clear. The t distribution is introduced much later. It is certainly a fitting means of introducing all of these concepts to fledgling research students. There are sections that can be added and removed at the instructors discretion. However, the introduction to hypothesis testing is a bit awkward (this is not unusual). read more. The interface is fine. But, when you understand the strengthsand weaknesses of these tools, you can use them to learn about the world. I find this method serves to give the students confidence in knowing that they understand concepts before moving on to new material. The text includes sections that could easily be extracted as modules. Therefore, while the topics are largely the same the depth is lighter in this text than it is in some alternative introductory texts. 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