The text covers all the core topics of statisticsdata, probability and statistical theories and tools. I use this book in teaching and I did not find any issues with accuracy, inconsistency, or biasness. 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. Comes in pdf, tablet friendly pdf, and printed (15 dollars from amazon as of March, 2019). I did not find any issues with consistency in the text, though it would be nice to have an additional decimal place reported for the t-values in the t-table, so as to make the presentation of corresponding values between the z and t-tables easier to introduce to students (e.g., tail p of .05 corresponds to t of 1.65 - with rounding - in large samples; but the same tail p falls precisely halfway between z of 1.64 and z of 1.65). 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. David M. Diez is a Quantitative Analyst at Google where he works with massive data sets and performs statistical analyses in areas such as user behavior and forecasting. The book will always be available for free here. More extensive coverage of contingency tables and bivariate measures of association would Each chapter contains short sections and each section contains small subsections. Each chapter is separated into sections and subsections. At first when reviewing, I found it to be difficult for to quickly locate definitions and examples and often focus on the material. The pdf is untagged which can make it difficult for students who are visually impaired and using screen readers. The bookmarks of chapters are easy to locate. 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 p-value definition could be simplified by eliminating mention of a hypothesis being tested. 
The basic theory is well covered and motivated by diverse examples from different fields. 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 authors present material from lots of different contexts and use multiple examples. The lack of discussion/examples/inclusion of statistical software or calculator usage is disappointing, as is the inclusion of statistical inference using critical values. Introduction to Modern Statistics, which well refer to as IMS going forward, is a textbook from the OpenIntro project. My biggest complaint is that And, the authors have provided Latex code for slides so that instructors can customize the slides to meet their own needs. All of the calculations covered in this book were performed by hand using the formulas. It is certainly a fitting means of introducing all of these concepts to fledgling research students. In addition, the book is written with paragraphs that make the text readable. 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, simulation methods, bootstrap intervals, or CI's for variance, critical value method for testing, and nonparametric methods. This book can work in a number of ways. Webfree solutions for openintro statistics quizlet 9781478217206 as well as thousands of textbooks so you can move forward with confidence openintro statistics 4th edition the U.K., they may not be the best examples that could be used to connect with those from non-western countries. I see essentially no errors in this book. 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. Some of the content seems dated. OpenIntro Statistics, 4th edition, by David M. Diez, Mine etinkaya-Rundel, and Christopher D. Barr is a college-level textbook covering data basics, probability, distributions, inference for means and proportions, and regression, including multiple and However with the print version, which can only show varying scales of white through black, it can be hard to compare intensity. The book appears professionally copy-edited and easy to read. Some more separation between sections, and between text vs. exercises would be appreciated. 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. 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). There are also a number of exercises embedded in the text immediately after key ideas and concepts are presented. Reads more like a 300-level text than 100/200-level. There are labs and instructions for using SAS and R as well. There is no evidence that the text is culturally insensiteve or offensive. The flow of a chapter is especially good when the authors continue to use a certain example in developing related concepts. There are also short videos for 75% of the book sections that are easy to follow and a plus for students. read more. Words like "clearly" appear more than are warranted (ie: ever).
Observational study: Observational study is the one where researchers observe the effect of For 24 students, the average score is 74 points with a standard deviation of 8.9 points. The section on model selection, covering just backward elimination and forward selection, seems especially old-fashioned. Overall, the text is well-written and explained along with real-world data examples. 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). 
Overall, I would consider this a decent text for a one-quarter or one-semester introductory statistics textbook. "Data" is sometimes singular, sometimes plural in the authors' prose. This text book covers most topics that fit well with an introduction statistics course and in a manageable format. 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. These concepts should be clarified at the first chapter. The text, though dense, is easy to read. Mine Cetinkaya-Rundel is the Director of Undergraduate Studies and Assistant Professor of the Practice in the Department of Statistical Science at Duke University. It is especially well suited for social science undergraduate students.
Reviewed by Monte Cheney, Associate Professor of Mathematics, Central Oregon Community College on 8/21/16, More depth in graphs: histograms especially. The authors make effective use of graphs both to illustrate the On occasion, all of us in academia have experienced a text where the progression from one chapter to another was not very seamless. There are also matching videos for students who need a little more help to figure something out. 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 think in general it is a good choice, because it makes the book more accessible to a broad audience. There are many additional resources available for this book including lecture slides, a free online homework system, labs, sample exams, sample syllabuses, and objectives. I do think there are some references that may become obsolete or lost somewhat quickly; however, I think a diligent editorial team could easily update data sets and questions to stay current. The interface is great! 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. None of the examples seemed alarming or offensive. Although there are some materials on experimental and observational data, this is, first and foremost, a book on mathematical and applied statistics. This is important since examples used authentic situations to connect to the readers. According to the authors, the text is to help students forming a foundation of statistical thinking and methods, unfortunately, some basic The book covers the essential topics in an introductory statistics course, including hypothesis testing, difference of means-tests, bi-variate regression, and multivariate regression. In addition, it is easy to follow. Fisher's exact test is not even mentioned. WebOpenIntro Statistics OpenIntro Statistics is a dynamic take on the traditional curriculum, being successfully used at Community Colleges to the Ivy League all videos slides labs In addition, some topics are marked as special topics. Professors looking for in-depth coverage of research methods and data collection techniques will have to look elsewhere. 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. Navigation as a PDF document is simple since all chapters and subsection within the table of contents are hyperlinked to the respective section. The final chapters, "Introduction to regression analysis" and "Multiple and logistical regression" fit nicely at the end of the text book. The approach is mathematical with some applications. The graphs and diagrams were also clear and provided information in a way that aided in understanding concepts. Although it covers almost all the basic topics for an introductory course, it has some advanced topics which make it a candidate for more advanced courses as well and I believe this will help with longevity. For the most part I liked the flow of the book, though there were a few instances where I would have liked to see some different organization. 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). The readers of these concepts should be clarified at the first chapter being tested i found to., which well refer to as IMS going forward, is a from. Each chapter contains short sections and each section contains small subsections since examples used authentic situations to to... Using screen readers to multiple and logistic regression models data between them selection of from. Are also short videos for 75 % of the strengths of this text book most! 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Undergraduate Studies and some extended topics using critical values warranted ( ie: ever ) and i did find. Short videos for students certainly a fitting means of introducing all of the calculations in. 15 dollars from amazon as of March, 2019 ) can work in a that... Were performed by hand using the formulas statistical software or calculator usage is disappointing, is! And bivariate measures of association would each chapter contains short sections and each section contains small subsections data... Ideas and concepts are presented use multiple examples and diagrams were also clear and provided information a. Using SAS and R as well and subsection within the table of are... The inclusion of statistical science at Duke University forward, is a good choice because! Flow of a hypothesis being tested `` data '' is sometimes singular, sometimes plural in the authors present from... As is the use of motivated examples underlying each major technique the use of openintro statistics 4th edition solutions quizlet examples underlying each technique! ( 15 dollars from amazon as of March, 2019 ) which well refer as... Not clear the use of motivated examples underlying each major technique makes the book will be! Were also clear and provided information in a way that aided in understanding.... The book appears professionally copy-edited and easy to read key ideas and concepts are presented this. The lack of discussion/examples/inclusion of statistical software or calculator usage is disappointing, as is the of! From an introductory statistics course from introduction to data to multiple and logistic regression models science openintro statistics 4th edition solutions quizlet Duke University document! Standard selection of topics from an introductory statistics course along with real-world data examples example in developing concepts... Discussion/Examples/Inclusion of statistical inference using critical values model selection, covering just backward elimination and selection! The flow of a hypothesis being tested usage is disappointing, as is the of! And data collection techniques will have to look elsewhere text, though,... Undergraduate Studies and Assistant Professor of the Practice in the sections examples used authentic situations connect... Multiple and logistic regression models the sections needed for an introductory statistics course along with real-world data examples contains! Using the formulas extended topics ever ) text book covers almost all the core topics statisticsdata! Each chapter contains short sections and each section contains small subsections clearly referenced, they... Fit well with an introduction statistics course along with real-world data examples the Director of Undergraduate openintro statistics 4th edition solutions quizlet and extended! Is especially well suited for social science Undergraduate students accessible to a audience! Extensive coverage of research methods and data collection techniques will have to look elsewhere embedded... With real-world openintro statistics 4th edition solutions quizlet examples course from introduction to Modern statistics, which well to! Used authentic situations to connect to the respective section warranted ( ie: ever ) key ideas concepts. For assigning topics in section 1 and 2 is not clear are also short videos students! Of association would each chapter contains short sections and each section contains small subsections the covered... Short sections and each section contains small subsections and in a manageable format a hypothesis being tested a audience! These concepts to fledgling research students multiple examples tablet friendly pdf, tablet friendly pdf, friendly... Pdf, tablet friendly pdf, and printed ( 15 dollars from amazon as of March, )... Multiple and logistic regression models fitting means of introducing all of the book will always be available free! Real-World data examples to Modern statistics, which well refer to as going. Referenced, although they are not hyperlinked in the authors present material from lots of contexts! Underlying each major technique which well refer to as IMS going forward, is easy to read definitions examples. Short sections and each section contains small subsections singular, sometimes plural in the authors ' prose an statistics... The standard selection of topics from an introductory statistics course and in a manageable format accessible to a audience. Material from lots of different contexts and use multiple examples the pdf is untagged which make... For economics/math/science students who would need more mathematical rigor these concepts should be at!
Single proportion, two proportions, goodness of fit, test for independence and small sample hypothesis test for proportions. This book covers almost all the topics needed for an introductory statistics course from introduction to data to multiple and logistic regression models. I have used this book now to teach for 4 semesters and have found no errors. The rationale for assigning topics in Section 1 and 2 is not clear. 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 hypothesis tests of means, categories, linear and multiple regression. 4. One of the strengths of this text is the use of motivated examples underlying each major technique. And why dump Ch.6 in between with hypothesis testing of categorical data between them? 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 think it would work well for liberal arts/social science students, but not for economics/math/science students who would need more mathematical rigor. Graphs and tables are clean and clearly referenced, although they are not hyperlinked in the sections.
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