The authors present material from lots of different contexts and use multiple examples. read more. I value the unique organization of chapters, the format of the material, and the resources for instructors and students. 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. A teacher can sample the germane chapters and incorporate them without difficulty in any research methods class. This was not necessarily the case with some of the tables in the text. 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. From what I can tell, the book is accurate in terms of what it covers. While the text could be used in both undergraduate and graduate courses, it is best suited for the social sciences. The students can easily see the connections between the two types of tests. The organization for each chapter is also consistent. I think that these features make the book well-suited to self-study. There are no proofs that might appeal to the more mathematically inclined. The real data sets examples cover different topics, such as politics, medicine, etc. Introducing independence using the definition of conditional probability P(A|B)=P(A) is more accurate and easier for students to understand. Things flow together so well that the book can be used as is. Most of the examples are general and not culturally related. Also, the convenient sample is covered. 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. 325 and 357). Having a free pdf version and a hard copy for a few dollars is great. The subsequent chapters have all of the specifics about carrying out hypothesis tests and calculating intervals for different types of data. I believe students, as well as, instructors would find these additions helpful. The topics are not covered in great depth; however, as an introductory text, it is appropriate. Notation is consistent and easy to follow throughout the text. I have seen other texts begin with correlation and regression prior to tests of means, etc., and wonder which approach is best. That being said, I frequently teach a course geared toward engineering students and other math-heavy majors, so I'm not sure that this book would be fully suitable for my particular course in its present form (with expanded exercise selection, and expanded chapter 2, I would adopt it almost immediately). The text is well-written and with interesting examples, many of which used real data. I didn't experience any problems. 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. In addition, the book is written with paragraphs that make the text readable. Some examples are related to United States. These examples and techniques are very carefully described with quality graphical and visual aids to support learning. Especially, this book covers Bayesian probabilities, false negative and false positive calculations. It can be considered comprehensive if you consider this an introductory text. The simple mention of the subject "statistics" can strike fear in the minds of many students. OpenIntro Statistics - 4th Edition - Solutions and Answers | Quizlet Math Probability OpenIntro Statistics 4th Edition ISBN: 9781943450077 Christopher Barr, David Diez, Mine etinkaya-Rundel Sorry! Step 2 of 5 (a) It includes too much theory for our undergraduate service courses, but not enough practical details for our graduate-level service courses. Generation of Electrical Energy, 7th Edition Gupta B.R. The text is free of significant interface issues. The fourth edition is a definite improvement over previous editions, but still not the best choice for our curriculum. The pdf is untagged which can make it difficult for students who are visually impaired and using screen readers. The text is easily reorganized and re-sequenced. Sample Solutions for this Textbook We offer sample solutions for OPENINTRO:STATISTICS homework problems. Distributions and definitions that are defined are consistently referenced throughout the text as well as they apply or hold in the situations used. Errors are not found as of yet. 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). 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. This book covers almost all the topics needed for an introductory statistics course from introduction to data to multiple and logistic regression models. The presentation is professional with plenty of good homework sets and relevant data sets and examples. It's very fitting for my use with teachers whose primary focus is on data analysis rather than post-graduate research. In particular, the malaria case study and stokes case study add depth and real-world The authors limit their discussion on categorical data analysis to the chi square statistic, which centers on inference rather than on the substantive magnitude of the bivariate relationship. The revised 2nd edition of this book provides the reader with a solid foundation in probability theory and statistics as applied to the physical sciences, engineering and related fields. This text is an excellent choice for an introductory statistics course that has a broad group of students from multiple disciplines. I do not see introductory statistics content ever becoming obsolete. Also, non-parametric alternatives would be nice, especially Monte Carlo/bootstrapping methods. It would be nice to have an e-book version (though maybe I missed how to access this on the website). 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. If the volunteer sample is covered also that would be great because it is very common nowadays. The topics are not covered in great depth; however, as an introductory text, it is appropriate. There are labs and instructions for using SAS and R as well. Then, the basics of both hypothesis tests and confidence intervals are covered in one chapter. However, even with this change, I found the presentation to overall be clear and logical. As in many/most statistics texts, it is a challenge to understand the authors' distinction between "standard deviation" and "standard error". The lack of discussion/examples/inclusion of statistical software or calculator usage is disappointing, as is the inclusion of statistical inference using critical values. Materials in the later sections of the text are snaffled upon content covered in these initial chapters. The text is organized into sections, and the numbering system within each chapter facilitates assigning sections of a chapter. Some examples in the text are traditional ones that are overused, i.e., throwing dice and drawing cards to teach probability. Reviewed by Lily Huang, Adjunct Math Instructor , Bethel University on 11/13/18, The text covers all the core topics of statisticsdata, probability and statistical theories and tools. The book has relevant and easily understood scientific questions. 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. #. It is certainly a fitting means of introducing all of these concepts to fledgling research students. For example, there is a strong emphasis on assessing the normality assumption, even though most of the covered methods work well for non-normal data with reasonable sample sizes. While the examples did connect with the diversity within our country or i.e. 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. 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. 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. 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. The basic theory is well covered and motivated by diverse examples from different fields. openintro statistics fourth edition open textbook library . (e.g., U.S. presidential elections, data from California, data from U.S. colleges, etc.) The chapters are well organized and many real data sets are analyzed. I have used this book now to teach for 4 semesters and have found no errors. The text covers the foundations of data, distributions, probability, regression principles and inferential principles with a very broad net. Chapter 3 covers random variables and distributions including normal, geometry and binomial distributions. 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. The second is that examples and exercises are numbered in a similar manner and students frequently confuse them early in the class. There were some author opinions on such things as how to go about analyzing the data and how to determine when a test was appropriate, but those things seem appropriate to me and are welcome in providing guidance to people trying to understand when to choose a particular statistical test or how to interpret the results of one. The interface of the book appears to be fine for me, but more attractive colors would make it better. This text does indicate that some topics can be omitted by identifying them as 'special topics'. After much searching, I particularly like the scope and sequence of this textbook. Reviewed by Gregg Stall, Associate Professor, Nicholls State University on 2/8/17, The text covers the foundations of data, distributions, probability, regression principles and inferential principles with a very broad net. #. 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. I did not notice any culturally sensitive examples, and no controversial or offensive examples for the reader are presented. 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 writing in this book is above average. Select the Edition for OpenIntro Statistics Below: . The approach of introducing the inferences of proportions and the Chi-square test in the same chapter is novel. Typos and errors were minimal (I could find none). These graphs and tables help the readers to understand the materials well, especially most of the graphs are colored figures. The authors use the Z distribution to work through much of the 1-sample inference. It would be feasible to use any part of the book without using previous sections as long as students had appropriate prerequisite knowledge. Students can check their answers to the odd questions in the back of the book. 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. The odd-numbered exercises also have answers in the book. 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. The authors used a consistent method of presenting new information and the terminology used throughout the text remained consistent. One of the good topics is the random sampling methods, such as simple sample, stratified, cluster, and multistage random sampling methods. 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 book uses relevant topics throughout that could be quickly updated. 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. The text covers all the core topics of statisticsdata, probability and statistical theories and tools. For example, income variations in two cities, ethnic distribution across the country, or synthesis of data from Africa. The only issue I had in the layout was that at the end of many sections was a box high-lighting a term. read more. It is easy to skip some topics with no lack of consistency or confusion. These blend well with the Exercises that contain the odd solutions at the end of the text. There is only a small section explaining why they do not use one sided tests and a brief explanation on how to perform a one sided test. In other words, breadth, yes; and depth, not so much. In particular, I like that the probability chapter (which comes early in the text) is not necessary for the chapters on inference. read more. It appears smooth and seamless. 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