OpenIntro Statistics. If you are looking for deep mathematical comprehensiveness of exercises, this may not be the right book, but for most introductory statistics students who are not pursuing deeper options in math/stat, this is very comprehensive. 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. It's very fitting for my use with teachers whose primary focus is on data analysis rather than post-graduate research. The learner cant capture what is logistic regression without a clear definition and explanation. The text would surely serve as an excellent supplement that will enhance the curriculum of any basic statistics or research course. 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. There is also a list of known errors that shows that errors are fixed in a timely manner. One of the real strengths of the book is the many examples and datasets that it includes. The consistency of this text is quite good. There are a lot of topics covered. Also, for how the authors seem to be focusing on practicalities, I was somewhat surprised about some of the organization of the inference sections. It definitely makes the students more comfortable with learning a new test because its just the same thing with different statistics. Reviewed by Monte Cheney, Associate Professor of Mathematics, Central Oregon Community College on 8/21/16, More depth in graphs: histograms especially. The examples for tree diagrams are very good, e.g., small pox in Boston, breast cancer. 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. 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 The text is quite consistent in terms of terminology and framework. Print. read more. read more. I found no problems with the book itself. One of the good topics is the random sampling methods, such as simple sample, stratified, cluster, and multistage random sampling methods. My interest in this text is for a graduate course in applied statistics in the field of public service. This text does indicate that some topics can be omitted by identifying them as 'special topics'. 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). Probability is optional, inference is key, and we feature real data whenever . It should be pointed out that logistic regression is using a logistic function to model a binary dependent variable. Nothing was jarring in this aspect, and the sections/chapters were consistent. Words like "clearly" appear more than are warranted (ie: ever). This book has both the standard selection of topics from an introductory statistics course along with several in-depth case studies and some extended topics. The graphs and tables in the text are well designed and accurate. The narrative of the text is grounded in examples which I appreciate. Calculations by hand are not realistic. The code and datasets are available to reproduce materials from the book. There are no issues with the grammar in the book. Archive. Typos that are identified and reported appear to be fixed within a few days which is great. Each chapter begins with a summary and a URL link to resources like videos, slides, etc. The content is up-to-date. The bookmarks of chapters are easy to locate. I teach at an institution with 10-week terms and I found it relatively easy to subdivide the material in this book into a digestible 10 weeks (I am not covering the entire book!). I did not see any problems in regards to the book's notation or terminology. Examples from a variety of disciplines are used to illustrate the material. The texts includes basic topics for an introductory course in descriptive and inferential statistics. 100% 100% found this document not useful, Mark this document as not useful. Reviewed by Kendall Rosales, Instructor and Service Level Coordinator, Western Oregon University on 8/20/20, There is more than enough material for any introductory statistics course. Especially, this book covers Bayesian probabilities, false negative and false positive calculations. Ensure every student can access the course textbook. The book provides an effective index. Given that this is an introductory textbook, it is clearly written and accessible to students with a variety of disciplinary backgrounds. In general I was satisfied. The text provides enough examples, exercises and tips for the readers to understand the materials. Share. I do not see introductory statistics content ever becoming obsolete. Marginal notes for key concepts & formulae? 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 sections that can be added and removed at the instructors discretion. I found the book's prose to be very straightforward and clear overall. 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. The index and table of contents are clear and useful. The order of introducing independence and conditional probability should be switched. There are a few color splashes of blue and red in diagrams or URL's. The textbook has been thoroughly vetted with an estimated 20,000 students using it annually. The text is accurate due to its rather straight forward approach to presenting material. Like most statistics books, each topic builds on ones that have come before and readers will have no trouble following the terminology as they progress through the book. 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. It includes too much theory for our undergraduate service courses, but not enough practical details for our graduate-level service courses. Each section within a chapter build on the previous sections making it easy to align content. The examples are general and do not deal with racial or cultural matters. 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 linear and logistic regression. 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 content of the book is accurate and unbiased. For examples, the distinction between descriptive statistics and inferential statistics, the measures of central tendency and dispersion. Also, grouping confidence intervals and hypothesis testing in Ch.5 is odd, when Ch.7 covers hypothesis testing of numerical data. The pdf and tablet pdf have links to videos and slides. The texts selection for notation with common elements such as p-hat, subscripts, compliments, standard error and standard deviation is very clear and consistent. This is the third edition and benefits from feedback from prior versions. However, even with this change, I found the presentation to overall be clear and logical. This is the most innovative and comprehensive statistics learning website I have ever seen. 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. 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). This is a particular use of the text, and my students would benefit from and be interested in more social-political-economic examples. This book was written with the undergraduate level in mind, but it's also popular in high schools and graduate courses. Jargon is introduced adequately, though. The authors bold important terms, and frequently put boxes around important formulas or definitions. The B&W textbook did not seem to pose any problems for me in terms of distortion, understanding images/charts, etc., in print. The organization/structure provides a smooth way for the contents to gradually progress in depth and breadth. OpenIntro Statistics 4th Edition. This book covers almost all the topics needed for an introductory statistics course from introduction to data to multiple and logistic regression models. This text book covers most topics that fit well with an introduction statistics course and in a manageable format. 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). In other cases I found the omissions curious. I did not notice any culturally sensitive examples, and no controversial or offensive examples for the reader are presented. I think that the first chapter has some good content about experiments vs. observational studies, and about sampling. Complete visual redesign. The text covers the foundations of data, distributions, probability, regression principles and inferential principles with a very broad net. 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, The authors use the Z distribution to work through much of the 1-sample inference. read more. Similar to most intro The topics are not covered in great depth; however, as an introductory text, it is appropriate. OpenIntro Statistics supports flexibility in choosing and ordering topics. The section on model selection, covering just backward elimination and forward selection, seems especially old-fashioned. The students can easily see the connections between the two types of tests. I found no negative issues with regard to interface elements. The topics are not covered in great depth; however, as an introductory text, it is appropriate. The overall length of the book is 436 pages, which is about half the length of some introductory statistics books. These examples and techniques are very carefully described with quality graphical and visual aids to support learning. In particular, I like that the probability chapter (which comes early in the text) is not necessary for the chapters on inference. 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 However, when introducing the basic concepts of null and alternative hypotheses and the p-value, the book used different definitions than other textbooks. The statistical terms, definitions, and equation notations are consistent throughout the text. Some of these will continue to be useful over time, but others may be may have a shorter shelf life. No issues with consistency in that text are found. 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. The pros are that it's small enough that a person can work their way through it much faster than would be possible with many of the alternatives. Chapter 7 and 8 cover the linear , multiple and logistic regression. The wording "at least as favorable to the alternative hypothesis as our current data" is misleading. It also offered enough graphs and tables to facilatate the reading. The book is clear and well written. If the volunteer sample is covered also that would be great because it is very common nowadays. I often assign reading and homework before I discuss topics in lecture. 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 have not noted any inconsistencies, inaccuracies, or biases. At The authors used a consistent method of presenting new information and the terminology used throughout the text remained consistent. In addition, some topics are marked as special topics. Some examples in the text are traditional ones that are overused, i.e., throwing dice and drawing cards to teach probability. read more. I was sometimes confused by tables with missing data or, as was the case on page 11, when the table was sideways on the page. However, I did find the inclusion of practice problems at the end of each section vs. all together the end of the whole chapter (which is the new arrangement in the 4th edition) to be a challenge - specifically, this made it difficult for me to identify easily where sections ended, and in some places, to follow the train of thought across sections. Although there are some materials on experimental and observational data, this is, first and foremost, a book on mathematical and applied statistics. 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. Create a clear way to explain this multi-faceted topic and the world will beat a path to your door. For example, the inference for categorical data chapter is broken in five main section. For example, types of data, data collection, probability, normal model, confidence intervals and inference for The reading of the book will challenge students but at the same time not leave them behind. Professors looking for in-depth coverage of research methods and data collection techniques will have to look elsewhere. For the most part, examples are limited to biological/medical studies or experiments, so they will last. The text, though dense, is easy to read. The student-facind end, while not flashy or gamified in any way, is easy to navigate and clear. This introductory material then serves as the foundation for later chapter where students are introduced to inferential statistical practices. The best statistics OER I have seen yet. Typos and errors were minimal (I could find none). 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. read more. You are on page 1 of 3. The book is very consistent from what I can see. Download now. 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. Well, this text provides a kinder and gentler introduction to data analysis and statistics. Labs are available in many modern software: R, Stata, SAS, and others. The fourth edition is a definite improvement over previous editions, but still not the best choice for our curriculum. Ideas about unusual results are seeded throughout the early chapters. 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. 4th edition solutions and quizlet . It is certainly a fitting means of introducing all of these concepts to fledgling research students. One of the good topics is the random sampling methods, such as simple sample, stratified, 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. Part I makes key concepts in statistics readily clear. An interesting note is that they introduce inference with proportions before inference with means. No display issues with the devices that I have. OpenIntro Statistics - 4th Edition - Solutions and Answers | Quizlet Math Probability OpenIntro Statistics 4th Edition ISBN: 9781943450077 Christopher Barr, David Diez, Mine etinkaya-Rundel Sorry! 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. It has scientific examples for the topics so they are always in context. Most of the examples are general and not culturally related. read more. The modularity is creative and compares well. Teachers might quibble with a particular omission here or there (e.g., it would be nice to have kernel densities in chapter 1 to complement the histogram graphics and some more probability distributions for continuous random variables such as the F distribution), but any missing material could be readily supplemented. There are lots of graphs in the book and they are very readable. 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. Many OERs (and published textbooks) are difficult to convert from a typical 15-week semester to a 10-week term, but not this one! However, it would not suffice for our two-quarter statistics sequence that includes nonparametrics. As a mathematician, I find this book most readable, but I imagine that undergraduates might become somewhat confused. After much searching, I particularly like the scope and sequence of this textbook. 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 book is easy to follow and the roadmap at the front for the instructor adds additional ease. Some more separation between sections, and between text vs. exercises would be appreciated. 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. The organization in chapter 5 also seems a bit convoluted to me. The graphs and diagrams were also clear and provided information in a way that aided in understanding concepts. Chapter 3 covers random variables and distributions including normal, geometry and binomial distributions. I was concerned that it also might add to the difficulty of analyzing tables. The interface of the book appears to be fine for me, but more attractive colors would make it better. Most contain glaring conceptual and pedagogical errors, and are painful to read (don't get me started on percentiles or confidence intervals). 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. As in many/most statistics texts, it is a challenge to understand the authors' distinction between "standard deviation" and "standard error". This open access textbook provides the background needed to correctly use, interpret and understand statistics and statistical data in diverse settings. The discussion of data analysis is appropriately pitched for use in introductory quantitative analysis courses in a variety of disciplines in the social sciences . . The nicely designed website (https://www.openintro.org) contains abundant resources which are very valuable for both students and teachers, including the labs, videos, forums and extras. 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. 325 and 357). Jump to Page . Journalism, Media Studies & Communications. Exercises: Yes: Solutions: Odd numbered problems: Solution Manual: Available to verified teachers: License: Creative Commons: Fourth edition (May 2019) Black and white paperback version from Amazon $20; The examples are up-to-date. This topic is usually covered in the middle of a textbook. All of the chapters contain a number of useful tips on best practices and common misunderstandings in statistical analysis. 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. I did not find any grammatical errors that impeded meaning. Each chapter is separated into sections and subsections. This text is an excellent choice for an introductory statistics course that has a broad group of students from multiple disciplines. In addition, the book is written with paragraphs that make the text readable. At the same time, the material is covered in such a matter as to provide future research practitioners with a means of understanding the possibilities when considering research that may prove to be of value in their respective fields. The book has relevant and easily understood scientific questions. Ability to whitelist other teachers so they can immediately get full access to teacher resources on openintro.org. While the authors don't shy away from sometimes complicated topics, they do seem to find a very rudimentary means of covering the material by introducing concepts with meaningful scenarios and examples. Each chapter consists of 5-10 sections. Parts I and II give an overview of the most common tests (t-test, ANOVA, correlations) and work out their statistical principles. 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. Everything appeared to be accurate. There are also pictures in the book and they appear clear and in the proper place in the chapters. In addition all of the source code to build the book is available so it can be easily modified. The book started with several examples and case study to introduce types of variables, sampling designs and experimental designs (chapter 1). The authors also make GREAT use of statistical graphics in all the chapters. There are chapters and sections that are optional. Overall, this is the best open-source statistics text I have reviewed. It recognizes the prevalence of technology in statistics and covers reading output from software. 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. The book was fairly consistent in its use of terminology. The topics all proceed in an orderly fashion. The language seems to be free of bias. 3rd Edition files and information (2015, 436 pages) 2nd Edition files and information (2012, 426 pages) The later chapters (chapter 4-8) are self-contained and can be re-ordered. The examples will likely become dated, but that is always the case with statistics textbooks; for now, they all seem very current (in one example, we solve for the % of cat videos out of all the videos on Youtube). The overall organization of the text is logical. 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 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. I find this method serves to give the students confidence in knowing that they understand concepts before moving on to new material. There are distracting grammatical errors. 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. 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. The second is that examples and exercises are numbered in a similar manner and students frequently confuse them early in the class. Extra Content. Display of graphs and figures is good, as is the use of color. The presentation is professional with plenty of good homework sets and relevant data sets and examples. The resources on the website also are well organized and easy to access and download. 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. 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 . This book has both the standard selection of topics from an introductory statistics course along with several in-depth case studies and some extended topics. More extensive coverage of contingency tables and bivariate measures of association would be helpful. 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. Percentiles? The color graphics come through clearly and the embedded links work as they should. The basic theory is well covered and motivated by diverse examples from different fields. Also, non-parametric alternatives would be nice, especially Monte Carlo/bootstrapping methods. #. The examples flow nicely into the guided practice problems and back to another example, definition, set of procedural steps, or explanation. 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. The authors introduce a definition or concept by first introducing an example and then reference back to that example to show how that object arises in practice. This book does not contain anything culturally insensitive, certainly. 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). It would be feasible to use any part of the book without using previous sections as long as students had appropriate prerequisite knowledge. Statistics and Probability Statistics and Probability solutions manuals OpenIntro Statistics 4th edition We have solutions for your book! The material in the book is currently relevant and, given the topic, some of it will never be irrelevant. read more. The drawback of this book is that it does not cover how to use any computer software or even a graphing calculator to perform the calculations for inferences. Chapter 4-6 cover the inferences for means and proportions and the Chi-square test. 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. I also appreciated that the authors use examples from the hard sciences, life sciences, and social sciences. The organization for each chapter is also consistent. While the text could be used in both undergraduate and graduate courses, it is best suited for the social sciences. The authors are sloppy in their use of hat notation when discussing regression models, expressing the fitted value as a function of the parameters, instead of the estimated parameters (pp. The coverage of probability and statistics is, for the most part, sound. There aren't really any cultural references in the book. The most accurate open-source textbook in statistics I have found. With several in-depth case studies and some extended topics they should I did not notice any sensitive! 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