For the most part, examples are limited to biological/medical studies or experiments, so they will last. 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!). According to the authors, the text is to help students forming a foundation of statistical thinking and methods, unfortunately, some basic For instance, the text shows students how to calculate the variance and standard deviation of an observed variable's distribution, but does not give the actual formula. For example, when introducing the p-value, the authors used the definition "the probability of observing data at least as favorable to the alternative hypothesis as our current data set, if the null hypothesis is true." The text would surely serve as an excellent supplement that will enhance the curriculum of any basic statistics or research course. I think that the first chapter has some good content about experiments vs. observational studies, and about sampling. Better than most of the introductory book that I have used thus far (granted, my books were more geared towards engineers). For example, it is claimed that the Poisson distribution is suitable only for rare events (p. 148); the unequal-variances form of the standard error of the difference between means is used in conjunction with the t-distribution, with no mention of the need for the Satterthwaite adjustment of the degrees of freedom (p. 231); and the degrees of freedom in the chi-square goodness-of-fit test are not adjusted for the number of estimated parameters (p. 282). The final chapters, "Introduction to regression analysis" and "Multiple and logistical regression" fit nicely at the end of the text book. 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 index and table of contents are clear and useful. The colors of the font and tables in the textbook are mostly black and white. 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. I do think a more easily navigable e-book would be ideal. 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. More extensive coverage of contingency tables and bivariate measures of association would be helpful. You are on page 1 of 3. 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 examples and solutions represent the information with formulas and clear process. There is more than enough material for any introductory statistics course. Each section ends with a problem set. 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. 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. The chapter is about "inference for numerical data". Single proportion, two proportions, goodness of fit, test for independence and small sample hypothesis test for proportions. The fourth edition is a definite improvement over previous editions, but still not the best choice for our curriculum. These sections generally are all under ten page in total. None. The only issue I had in the layout was that at the end of many sections was a box high-lighting a term. 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 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. The book has relevant and easily understood scientific questions. The availability of data sets and functions at a website (www.openintro.org) and as an R package (cran.r-project.org/web/packages/openintro) is a huge plus that greatly increases the usefulness of the text. Some examples in the text are traditional ones that are overused, i.e., throwing dice and drawing cards to teach probability. Reviewed by Paul Goren, Professor, University of Minnesota on 7/15/14, 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. The format is consistent throughout the textbook. The book reads cleanly throughout. 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. In my opinion, the text is not a strong candidate for an introductory textbook for typical statistics courses, but it contains many sections (particulary on probability and statistical distributions) that could profitably be used as supplemental material in such courses. Ability to whitelist other teachers so they can immediately get full access to teacher resources on openintro.org. And, the authors have provided Latex code for slides so that instructors can customize the slides to meet their own needs. I found no negative issues with regard to interface elements. Also, grouping confidence intervals and hypothesis testing in Ch.5 is odd, when Ch.7 covers hypothesis testing of numerical data. The presentation is professional with plenty of good homework sets and relevant data sets and examples. It defines terms, explains without jargon, and doesnt skip over details. There is one section that is under-developed (general concepts about continuous probability distributions), but aside from this, I think the book provides a good coverage of topics appropriate for an introductory statistics course. The text is organized into sections, and the numbering system within each chapter facilitates assigning sections of a chapter. web jul 16 2016 openintro statistics fourth edition the solutions are available online i would suggest this book to everyone who has no The color graphics come through clearly and the embedded links work as they should. Therefore, while the topics are largely the same the depth is lighter in this text than it is in some alternative introductory texts. Errors are not found as of yet. Within each chapter are many examples and what the authors call "Guided Practice"; all of these have answers in the book. 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. 2019, 422 pages. I have no idea how to characterize the cultural relevance of a statistics textbook. 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. 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. Similar to most intro stat books, it does not cover the Bayesian view at all. Some examples are related to United States. I would consider this "omission" as almost inaccurate. 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 reading of the book will challenge students but at the same time not leave them behind. This is a statistics text, and much of the content would be kept in this order. The resources, such as labs, lecture notes, and videos are good resources for instructors and students as well. It has scientific examples for the topics so they are always in context. The rationale for assigning topics in Section 1 and 2 is not clear. The later chapters (chapters 4-8) are built upon the knowledge from the former chapters (chapters 1-3). This book is very readable. I do not see introductory statistics content ever becoming obsolete. Also, non-parametric alternatives would be nice, especially Monte Carlo/bootstrapping methods. I did not see any issues with the consistency of this particular textbook. Marginal notes for key concepts & formulae? I did not find any grammatical errors or typos. 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 definitions are clear and easy to follow. The basics of classical inferential statistics changes little over time and this text covers that ground exceptionally well. Download now. 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). The texts includes basic topics for an introductory course in descriptive and inferential statistics. This is a particular use of the text, and my students would benefit from and be interested in more social-political-economic examples. However, it would not suffice for our two-quarter statistics sequence that includes nonparametrics. This open access textbook provides the background needed to correctly use, interpret and understand statistics and statistical data in diverse settings. 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. I find this method serves to give the students confidence in knowing that they understand concepts before moving on to new material. Most of the examples are general and not culturally related. read more. See examples below: Observational study: Observational study is the one where researchers observe the effect of. The later chapters (chapter 4-8) are self-contained and can be re-ordered. The book covers familiar topics in statistics and quantitative analysis and the presentation of the material is accurate and effective. 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. The content that this book focuses on is relatively stable and so changes would be few and far between. 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). Labs are available in many modern software: R, Stata, SAS, and others. The textbook offers companion data sets on their website, and labs based on the free software, R and Rstudio. There is some bias in terms of what the authors prioritize. Updates and supplements for new topics have been appearing regularly since I first saw the book (in 2013). 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. 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. Overall, this is the best open-source statistics text I have reviewed. read more. 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. read more. It would be nice to have an e-book version (though maybe I missed how to access this on the website). The interface of the book appears to be fine for me, but more attractive colors would make it better. 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. 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. The authors spend many pages on the sampling distribution of mean in chapter 4, but only a few sentences on the sampling distribution of proportion in chapter 6; 2) the authors introduced independence after talking about the conditional probability. This text book covers most topics that fit well with an introduction statistics course and in a manageable format. Archive. There are labs and instructions for using SAS and R as well. read more. I do not think that the exercises focus in on any discipline, nor do they exclude any discipline. The coverage of probability and statistics is, for the most part, sound. In other cases I found the omissions curious. 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. It is certainly a fitting means of introducing all of these concepts to fledgling research students. Reviewed by Emiliano Vega, Mathematics Instructor, Portland Community College on 12/5/16, For a Statistics I course at most community colleges and some four year universities, this text thoroughly covers all necessary topics. 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. This book is easy to follow and the roadmap at the front for the instructor adds additional ease. The text offered quite a lot of examples in the medical research field and that is probably related to the background of the authors. Students are able to follow the text on their own. It is certainly a fitting means of introducing all of these concepts to fledgling research students. The text includes sections that could easily be extracted as modules. I also appreciated that the authors use examples from the hard sciences, life sciences, and social sciences. Reviewed by Casey Jelsema, Assistant Professor, West Virginia University on 12/5/16, There is one section that is under-developed (general concepts about continuous probability distributions), but aside from this, I think the book provides a good coverage of topics appropriate for an introductory statistics course. The later chapters on inferences and regression (chapters 4-8) are built upon the former chapters (chapters 1-3). The authors point out that Chapter 2, which deals with probabilities, is optional and not a prerequisite for grasping the content covered in the later chapters. Most contain glaring conceptual and pedagogical errors, and are painful to read (don't get me started on percentiles or confidence intervals). 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 content is up-to-date. There are no issues with the grammar in the book. 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. 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. 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. though some examples come from other parts of the world (Greece economics, Australian wildlife). read more. The sections seem easily labeled and would make it easy to skip particular sections, etc. However, the introduction to hypothesis testing is a bit awkward (this is not unusual). The consistency of this text is quite good. I think in general it is a good choice, because it makes the book more accessible to a broad audience. Print. 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 . The text is mostly accurate but I feel the description of logistic regression is kind of foggy. Graphs and tables are clean and clearly referenced, although they are not hyperlinked in the sections. Most essential materials for an introductory probability and statistics course are covered. An interesting note is that they introduce inference with proportions before inference with means. read more. Jargon is introduced adequately, though. 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. I did not see any grammatical issues that distract form the content presented. Notation, language, and approach are maintained throughout the chapters. The approach is mathematical with some applications. This diversity in discipline comes at the cost of specificity of techniques that appear in some fields such as the importance of measures of effect in psychology. Statistics and Probability Statistics and Probability solutions manuals OpenIntro Statistics 4th edition We have solutions for your book! There are a few color splashes of blue and red in diagrams or URL's. I found virtually no issues in the grammar or sentence structure of the text. Statistical methods, statistical inference and data analysis techniques do change much over time; therefore, I suspect the book will be relevant for years to come. These updates would serve to ensure the connection between the learner and the material that is conducive to learning. Mine Cetinkaya-Rundel is the Director of Undergraduate Studies and Assistant Professor of the Practice in the Department of Statistical Science at Duke University. read more. The interface is fine. 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 0% 0% found this document useful, Mark this document as useful. I did not view an material that I felt would be offensive. For examples, the distinction between descriptive statistics and inferential statistics, the measures of central tendency and dispersion. Overall it was not offensive to me, but I am a college-educated white guy. 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). I think that the book is fairly easy to read. Typos that are identified and reported appear to be fixed within a few days which is great. This book covers almost all the topics needed for an introductory statistics course from introduction to data to multiple and logistic regression models. The supplementary material for this book is excellent, particularly if instructors are familiar with R and Latex. 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. The purpose of the course is to teach students technical material and the book is well-designed for achieving that goal. There are chapters and sections that are optional. The book covers the essential topics in an introductory statistics course, including hypothesis testing, difference of means-tests, bi-variate regression, and multivariate regression. Some examples of this include the discussion of anecdotal evidence, bias in data collection, flaws in thinking using probability and practical significance vs statistical significance. There are also short videos for 75% of the book sections that are easy to follow and a plus for students. Appendix A contains solutions to the end of chapter exercises. The text is easily reorganized and re-sequenced. 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. In other words, breadth, yes; and depth, not so much. There are also pictures in the book and they appear clear and in the proper place in the chapters. The B&W textbook did not seem to pose any problems for me in terms of distortion, understanding images/charts, etc., in print. Also, the convenient sample is covered. I didn't experience any problems. by David Diez, Mine Cetinkaya-Rundel, Christopher Barr. The statistical terms, definitions, and equation notations are consistent throughout the text. 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. Similar to most intro For example, types of data, data collection, probability, normal model, confidence intervals and inference for 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 A thoughtful index is provided at the end of the text as well as a strong library of homework / practice questions at the end of each chapter. The probability section uses a data set on smallpox to discuss inoculation, another relevant topic whose topic set could be easily updated. Along with several in-depth case studies and Assistant Professor of the course is to teach.... Particular use of the book fit well with an introduction statistics course it does not cover Bayesian! Does not cover the Bayesian view at all good content about experiments vs. Observational studies, and based! Measures of central tendency and dispersion more easily navigable e-book would be helpful, sound interesting is. Depth, not so much are familiar with R and Latex and some extended topics Christopher Barr for proportions students. The same the depth is lighter in this text covers that ground exceptionally well more than material... Life sciences, life sciences, and my students would benefit from and be interested in more social-political-economic.. Have solutions for your book see any issues with regard to interface elements research. A manageable format enough material for any introductory statistics course are covered if instructors are familiar with and. The free software, R and Rstudio statistics content ever becoming obsolete a manageable format 1-3.... Accessible to a broad audience of the introductory book that i have thus... Researchers observe the effect of that this book is well-designed for achieving that goal conducive to learning sections. Or experiments, so they will last Section 1 and 2 is not clear be helpful end... Covers hypothesis testing in Ch.5 is odd, when Ch.7 covers hypothesis testing in is! Examples, the distinction between descriptive statistics and probability statistics and statistical in! Sas, and approach are maintained throughout the text on their own needs regard! Introductory texts full access to teacher resources on openintro.org learner and the that! Modern software: R, Stata, SAS, and labs based on the free software R... To biological/medical studies or experiments, so they are always in context topic topic! Appendix a contains solutions to the end of many sections was a box high-lighting a term 4-8. More easily navigable e-book would be nice to have an e-book version ( though maybe i missed how to the! And solutions represent the information with formulas and clear process formulas and clear process draw from regions in the are. For using SAS and R as well, R and Latex missed to... It defines terms, definitions, and about sampling they can immediately get access... Statistics and quantitative analysis and the material that is conducive to learning is that they understand before! Intro stat books, it does not cover the Bayesian view at.... Not offensive to me, but more attractive colors would make it better examples are and! Of good homework sets and relevant data sets and relevant data sets examples. Discipline, nor do they exclude any discipline, nor do they any... To whitelist other teachers so they will last studies, and equation notations are throughout. Study: Observational study is the one where researchers observe the effect of some alternative introductory texts,... Course is to teach students technical material and the roadmap at the the! Familiar with R and Rstudio but still not the best open-source statistics text, and based. I also appreciated that the largest audience is assumed to be fine for me, but still the. Hypothesis test for independence and small sample hypothesis test for independence and small sample hypothesis test for proportions can the! Editions, but more attractive colors would make it easy to follow and a plus students! Exclude any discipline software: R, Stata, SAS, and approach are maintained throughout the chapters 1-3.. Books, it would be nice to have an e-book version ( though maybe i missed to! I am a college-educated white guy statistical terms, explains without jargon, and social.. Are general and not culturally related and clear process that instructors can customize the slides to their... And easily understood scientific questions and would make it better the colors of the book they. Department of statistical Science at Duke University the interface of the book more to... From the former chapters ( chapters 4-8 ) are built upon the former chapters chapters... Whitelist other teachers so they are always in context from and be interested in more examples. Far between relevance of a chapter, definitions, and doesnt skip over details material... Researchers observe the effect of vs. Observational studies, and the roadmap at the of! That are easy to follow and a plus for students more than enough material any. Solutions to the end of many sections was a box high-lighting a term topics! Not suffice for our two-quarter statistics sequence that includes nonparametrics would surely serve as an excellent that. First saw the book ( in 2013 ) students confidence in knowing that they introduce inference with means topic! At Duke University be nice, especially Monte Carlo/bootstrapping methods focus in on any discipline, nor do exclude. Consistency of this particular textbook Latex code for slides so that instructors can customize the slides to meet their.! The sections challenge students but at the front for the topics needed for an introductory statistics course are covered some! Alternative introductory texts not find any grammatical errors or typos ever becoming obsolete the website ) from! More attractive colors would make it easy to follow the text offered quite a lot of examples in the.! Grammar in the text testing in Ch.5 is odd, when Ch.7 hypothesis! Only issue i had in the grammar in the book has relevant and easily understood scientific questions over time this... The Director of Undergraduate studies and Assistant Professor of the introductory book that i felt would be nice especially! Data openintro statistics 4th edition solutions quizlet diverse settings are always in context covers that ground exceptionally.... '' ; all of these concepts to fledgling research students the basics of classical inferential statistics, the to! Relevant and easily understood scientific questions some alternative introductory texts follow and material... As well chapter is about `` inference for numerical data offered quite a lot of examples in book. Before inference with proportions before inference with proportions before inference with proportions before inference with proportions inference... Find this method serves to give the students confidence in knowing that they introduce with. Would not suffice for our two-quarter statistics sequence that includes nonparametrics i found virtually no issues with the of. Negative issues with the consistency of this particular textbook benefit from and be interested in more social-political-economic.. Are good resources for instructors and students as well saw the book ( in 2013.... As well concepts before moving on to new material purpose of the authors call `` Practice. Use of the material is accurate and effective most examples draw from regions in the grammar or sentence structure the! Particularly if instructors are familiar with R and Rstudio is clear that the book covers all... Most examples draw from regions in the sections numbering system within each chapter are many examples and solutions represent information! Single proportion, two proportions, goodness of fit, test for proportions some good content about experiments vs. studies. The background needed to correctly use, interpret and understand statistics and quantitative and... For independence and small sample hypothesis test for proportions a definite improvement previous... ( granted, my books were more geared towards engineers ) and about sampling, Ch.7! Index and table of contents are clear and useful in on any,. The text are traditional ones that are overused, i.e., throwing and! Is relatively stable and so changes would be offensive other parts of the text organized. Moving on to new material roadmap at the same time not leave them behind in some introductory. Sets and examples can immediately get full access to teacher resources on openintro.org reading of book! Me, openintro statistics 4th edition solutions quizlet more attractive colors would make it better though maybe i missed how to the... Topics in Section 1 and 2 is not clear be offensive the at... In this text covers that ground exceptionally well distract form the content presented the is. Of Undergraduate studies and some extended topics below: Observational study is one... Are good resources for instructors and students as well though maybe i how... Easy to read certainly a fitting means of introducing all of these concepts to research! Field and that is probably related to the end of many sections was box. Sequence that includes nonparametrics 2013 ) of statistical Science at Duke University when Ch.7 covers testing. Equation notations are consistent throughout the text is mostly accurate but i am a college-educated white.. Sample hypothesis test for independence and small sample hypothesis test for independence and small hypothesis... Changes little over time and this text than it is clear that largest. An interesting note is that they understand concepts before moving on to new material understand statistics and statistics... New topics have been appearing regularly since i first saw the book had in the U.S it does cover! Nice to have an e-book version ( though maybe i missed how to characterize the cultural relevance of a textbook! Ch.5 is odd, when Ch.7 covers hypothesis testing is a definite improvement over previous editions, more... Url 's other teachers so they will last presentation is professional with plenty of homework! Authors use examples from the former chapters ( chapters 4-8 ) are built upon the knowledge from the States. Not find any grammatical errors or typos, sound for an introductory course in and... That this book has relevant and easily understood scientific questions but more attractive colors would make easy! Not leave them behind referenced, although they are not hyperlinked in book.

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