My biggest complaint is that one-sided tests are basically ignored. There are separate chapters on bi-variate and multiple regression and they work well together. I didn't experience any problems. The topics are in a reasonable order. The organization for each chapter is also consistent. Download now. In particular, examples and datasets about county characteristics, elections, census data, etc, can become outdated fairly quickly. It covers all the standard topics fully. The chapters are bookmarked along the side of the pdf file (once downloaded). The text is mostly accurate but I feel the description of logistic regression is kind of foggy. There are no proofs that might appeal to the more mathematically inclined. 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. There is no evidence that the text is culturally insensiteve or offensive. This is especially true when there are multiple authors. 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. For one. The material in the book is currently relevant and, given the topic, some of it will never be irrelevant. There are labs and instructions for using SAS and R as well. One of the strengths of this text is the use of motivated examples underlying each major technique. I found the book's prose to be very straightforward and clear overall. The index is decent, but there is no glossary of terms or summary of formula, which is disappointing. The formatting and interface are clear and effective. There are sections that can be added and removed at the instructors discretion. 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. At first when reviewing, I found it to be difficult for to quickly locate definitions and examples and often focus on the material. The text needs real world data analysis examples from finance, business and economics which are more relevant to real life. 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. Chapter 2 covers the knowledge of probabilities including the definition of probability, Law of Large Numbers, probability rules, conditional probability and independence and linear combinations of random variables. It also offered enough graphs and tables to facilatate the reading. The t distribution is introduced much later. It can be considered comprehensive if you consider this an introductory text. The book has relevant and easily understood scientific questions. 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. I use this book in teaching and I did not find any issues with accuracy, inconsistency, or biasness. Almost every worked example and possible homework exercise in the book is couched in real-world situation, nearly all of which are culturally, politically, and socially relevant. In other cases I found the omissions curious. 2019, 422 pages. The authors used a consistent method of presenting new information and the terminology used throughout the text remained consistent. 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. Sample Solutions for this Textbook We offer sample solutions for OPENINTRO:STATISTICS homework problems. Though I might define p-values and interpret confidence intervals slightly differently. You are on page 1 of 3. It would be nice if the authors can start with the big picture of how people perform statistical analysis for a data set. The text covers all the core topics of statisticsdata, probability and statistical theories and tools. As in many/most statistics texts, it is a challenge to understand the authors' distinction between "standard deviation" and "standard error". 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 examples and exercises seem to be USA-centric (though I did spot one or two UK-based examples), but I do not think that it was being insensitive to any group. I have no idea how to characterize the cultural relevance of a statistics textbook. 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. It is a pdf download rather than strictly online so the format is more classical textbook as would be experienced in a print version. Overall it was not offensive to me, but I am a college-educated white guy. More extensive coverage of contingency tables and bivariate measures of association would be helpful. a first course in probability 9th edition solutions; umn resident health insurance; cartoon network invaded tv tropes. There are a lot of topics covered. More depth in graphs: histograms especially. The authors also make GREAT use of statistical graphics in all the chapters. The writing could be slightly more inviting, and concept could be more readily introduced via accessible examples more often. According to the authors, the text is to help students forming a foundation of statistical thinking and methods, unfortunately, some basic 4th edition solutions and quizlet . Share. 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. Each chapter contains short sections and each section contains small subsections. OpenIntro Statistics - 4th Edition - Solutions and Answers | Quizlet Math Probability OpenIntro Statistics 4th Edition ISBN: 9781943450077 Christopher Barr, David Diez, Mine etinkaya-Rundel Sorry! read more. Black and white paperback edition. Overall I like it a lot. Tables and graphs are sensibly annotated and well organized. It is certainly a fitting means of introducing all of these concepts to fledgling research students. The discussion of data analysis is appropriately pitched for use in introductory quantitative analysis courses in a variety of disciplines in the social sciences . I did not notice any culturally sensitive examples, and no controversial or offensive examples for the reader are presented. The book is clear and well written. However, the introduction to hypothesis testing is a bit awkward (this is not unusual). The sections on these advanced topics would make this a candidate for more advanced-level courses than the introductory undergraduate one I teach, and I think will help with longevity. The color graphics come through clearly and the embedded links work as they should. Percentiles? I do not detect a bias in the work. Chapters 4-6 on statistical inference are especially strong, and the discussion of outliers and leverage in the regression chapters should prove useful to students who work with small n data sets. The text is easily and readily divisible into subsections. One of the real strengths of the book is that it is nicely separated into coherent chapters and instructors would will have no trouble picking and choosing among them. OpenIntro Statistics offers a traditional introduction to statistics at the college level. Overall, this is a well written book for introductory level statistics. 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. There are a variety of interesting topics in the exercises that include research on the relationship between honesty, age and self control with children; an experiment on a treatment for asthma patients; smoking habits in the U.K.; a study on migraines and acupuncture; and a study on sinusitis and antibiotics. The final chapters, "Introduction to regression analysis" and "Multiple and logistical regression" fit nicely at the end of the text book. An interesting note is that they introduce inference with proportions before inference with means. I feel that the greatest strength of this text is its clarity. read more. This book covers almost all the topics needed for an introductory statistics course from introduction to data to multiple and logistic regression models. 325 and 357). Ideas about unusual results are seeded throughout the early chapters. These examples and techniques are very carefully described with quality graphical and visual aids to support learning. The examples were up-to-date, for example, discussing the fact that Google conducts experiments in which different users are given search results in different ways to compare the effectiveness of the presentations. 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. Intro Statistics with Randomization and Simulation Bringing a fresh approach to intro statistics, ISRS introduces inference faster using randomization and simulation techniques. read more. This is sometimes a problem in statistics as there are a variety of ways to express the similar statistical concepts. differential equations 4th edition solutions and answers quizlet calculus 4th edition . The final chapter (8) gives superficial treatments of two huge topics, multiple linear regression and logistic regression, with insufficient detail to guide serious users of these methods. read more. There are also short videos for 75% of the book sections that are easy to follow and a plus for students. The document was very legible. Overall, this is the best open-source statistics text I have reviewed. 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 students can easily see the connections between the two types of tests. No issues with consistency in that text are found. All of the calculations covered in this book were performed by hand using the formulas. Overall, the book is heavy on using ordinary language and common sense illustrations to get across the main ideas. 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 section on model selection, covering just backward elimination and forward selection, seems especially old-fashioned. My interest in this text is for a graduate course in applied statistics in the field of public service. 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. Reads more like a 300-level text than 100/200-level. In particular, the malaria case study and stokes case study add depth and real-world OpenIntro Statistics is a dynamic take on the traditional curriculum, being successfully used at Community Colleges to the Ivy League all videos slides labs other OpenIntro Statistics is recommended for college courses and self-study. The supplementary material for this book is excellent, particularly if instructors are familiar with R and Latex. 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. Having a free pdf version and a hard copy for a few dollars is great. 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 text covers the foundations of data, distributions, probability, regression principles and inferential principles with a very broad net. My only complaint in this is that, unlike a number of "standard" introductory statistics textbooks I have seen, is that the exercises are organized in a page-wide format, instead of, say, in two columns. Many examples use real data sets that are on the larger side for intro stats (hundreds or thousands of observations). I reviewed a paperback B&W copy of the 4th edition of this book (published 2019), which came with a list describing the major changes/reorganization that was done between this and the 3rd edition. No grammatical errors have been 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). 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. More color, diagrams, photos? 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). 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. Overall the organization is good, so I'm still rating it high, but individual instructors may disagree with some of the order of presentation. These are essential components of quantitative analysis courses in the social sciences. The authors present material from lots of different contexts and use multiple 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). While the text could be used in both undergraduate and graduate courses, it is best suited for the social sciences. However, I think a greater effort could be made to include more culturally relevant examples in this book. The student-facind end, while not flashy or gamified in any way, is easy to navigate and clear. It appears to stick to more non-controversial examples, which is perhaps more effective for the subject matter for many populations. The fourth edition is a definite improvement over previous editions, but still not the best choice for our curriculum. Then, the basics of both hypothesis tests and confidence intervals are covered in one chapter. The modularity is creative and compares well. Ability to whitelist other teachers so they can immediately get full access to teacher resources on openintro.org. 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; 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. read more. It defines terms, explains without jargon, and doesnt skip over details. The first chapter addresses treatments, control groups, data tables and experiments. Archive. The text is quite consistent in terms of terminology and framework. 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. So future sections will not rely on them. For example: "Researchers perform an observational study when they collect data in a way that does not directly interfere with how the data arise" (p. 13). 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 flow of a chapter is especially good when the authors continue to use a certain example in developing related concepts. (Unlike many modern books that seem to have random sentences scattered in between bullet points and boxes.). It has scientific examples for the topics so they are always in context. Additionally concepts related to flawed practices in data collection and analysis were presented to point out how inaccuracies could arise in research. There is more than enough material for any introductory statistics course. This topic is usually covered in the middle of a textbook. This book differs a bit in its treatment of inference. The key will be ensuring that the latest research trends/improvements/refinements are added to the book and that omitted materials are added into subsequent editions. However with the print version, which can only show varying scales of white through black, it can be hard to compare intensity. Elections, openintro statistics 4th edition solutions quizlet data, etc, can become outdated fairly quickly for the social.... Language and common sense illustrations to get across the main ideas bi-variate and multiple regression and they well! Thousands of observations ) hard to compare intensity also make GREAT use motivated... No evidence that the text remained consistent color graphics come through clearly the., distributions, probability and statistical theories and tools probability and statistical theories and.! Think a greater effort could be made to include more culturally relevant examples in text. 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