Nothing was jarring in this aspect, and the sections/chapters were consistent. Students can easily get confused and think the p-value is in favor of the alternative hypothesis. But there are instances where similar topics are not arranged very well: 1) when introducing the sampling distribution in chapter 4, the authors should introduce both the sampling distribution of mean and the sampling distribution of proportion in the same chapter. OpenIntro Statistics is a dynamic take on the traditional curriculum, being successfully used at Community Colleges to the Ivy League. 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. The section on model selection, covering just backward elimination and forward selection, seems especially old-fashioned. 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. The organization/structure provides a smooth way for the contents to gradually progress in depth and breadth. The definitions are clear and easy to follow. Our inaugural effort is OpenIntro Statistics. The book is broken into small sections for each topic. The content that this book focuses on is relatively stable and so changes would be few and far between. The simple mention of the subject "statistics" can strike fear in the minds of many students. The authors present material from lots of different contexts and use multiple examples. 4th edition solutions and quizlet . Embed. Two topics I found absent were the calculation of effect sizes, such as Cohen's d, and the coverage of interval and ratio scales of measurement (the authors provide a breakdown of numerical variables as only discrete and continuous). I believe students, as well as, instructors would find these additions helpful. The bookmarks of chapters are easy to locate. That is, do probability and inference topics for a SRS, then do probability and inference for a stratified sample and each time taking your probability and inference ideas further so that they are constantly being built upon, from day one! The second is that examples and exercises are numbered in a similar manner and students frequently confuse them early in the class. I found virtually no issues in the grammar or sentence structure of the text. There are also a number of exercises embedded in the text immediately after key ideas and concepts are presented. The basic theory is well covered and motivated by diverse examples from different fields. This can be particularly confusing to "beginners.". The code and datasets are available to reproduce materials from the 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 authors make effective use of graphs both to illustrate the In general I was satisfied. (Unlike many modern books that seem to have random sentences scattered in between bullet points and boxes.). The book provides an effective index. Calculations by hand are not realistic. Within each chapter are many examples and what the authors call "Guided Practice"; all of these have answers in the book. I value the unique organization of chapters, the format of the material, and the resources for instructors and students. The text covers all the core topics of statisticsdata, probability and statistical theories and tools. It is certainly a fitting means of introducing all of these concepts to fledgling research students. This problem has been solved: Problem 1E Chapter CH1 Problem 1E Step-by-step solution Step 1 of 5 Refer to the contingency table in problem 1.1 of the textbook to answer the questions. I did not see any inaccuracies in the book. The sections seem easily labeled and would make it easy to skip particular sections, etc. 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. 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 . 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 primary ways to navigate appear to be via the pdf and using the physical book. Words like "clearly" appear more than are warranted (ie: ever). For 24 students, the average score is 74 points with a standard deviation of 8.9 points. 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. 3rd Edition files and information (2015, 436 pages) 2nd Edition files and information (2012, 426 pages) 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. read more. However, the introduction to hypothesis testing is a bit awkward (this is not unusual). The topics are not covered in great depth; however, as an introductory text, it is appropriate. and get access to extra resources: Request a free desk copy of an OpenIntro textbook for a course (US only). The real data sets examples cover different topics, such as politics, medicine, etc. 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. I also particularly like that once the basics chapters are covered, the instructor can then pick and choose those topics that will best serve the course or needs of students. In addition all of the source code to build the book is available so it can be easily modified. I did not see any problems in regards to the book's notation or terminology. Other examples: "Each of the conclusions are based on some data" (p. 9); "You might already be familiar with many aspects of probability, however, formalization of the concepts is new for most" (p. 68); and "Sometimes two variables is one too many" (p. 21). However, there are some sections that are quite dense and difficult to follow. The text is easily reorganized and re-sequenced. It includes too much theory for our undergraduate service courses, but not enough practical details for our graduate-level service courses. The approach of introducing the inferences of proportions and the Chi-square test in the same chapter is novel. The terms and notation are consistent throughout the text. Then, the basics of both hypothesis tests and confidence intervals are covered in one chapter. 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. However, I think a greater effort could be made to include more culturally relevant examples in this book. 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. I suspect these will prove quite helpful to students. Therefore, while the topics are largely the same the depth is lighter in this text than it is in some alternative introductory texts. Lots of good graphics and referenced data sets, but not much discussion or inclusion of prevailing software such as R, SPSS, Minitab, or free online packages. Better than most of the introductory book that I have used thus far (granted, my books were more geared towards engineers). There do not appear to be grammatical errors. Each section ends with a problem set. This will increase the appeal of the text. It would be nice if the authors can start with the big picture of how people perform statistical analysis for a data set. Introductory statistics courses prepare students to think statistically but cover relatively few statistical methods. This is a statistics text, and much of the content would be kept in this order. Skip Navigation. The book is written as though one will use tables to calculate, but there is an online supplement for TI-83 and TI-84 calculator. Also, the convenient sample is covered. The book appears professionally copy-edited and easy to read. 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. The flow of a chapter is especially good when the authors continue to use a certain example in developing related concepts. 325 and 357). The students can easily see the connections between the two types of tests. Reviewed by Monte Cheney, Associate Professor of Mathematics, Central Oregon Community College on 8/21/16, More depth in graphs: histograms especially. Chapter4 (foundations of inference), chapter 5 (inference of numerical data) and chapter 6 (inference of categorical data) provide clear and fresh logic for understanding statistics. the U.K., they may not be the best examples that could be used to connect with those from non-western countries. Statistics and Probability Statistics and Probability solutions manuals OpenIntro Statistics 4th edition We have solutions for your book! This is a particular use of the text, and my students would benefit from and be interested in more social-political-economic examples. 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. This could be either a positive or a negative to individual instructors. The content is up-to-date. Step 2 of 5 (a) It also offered enough graphs and tables to facilatate the reading. Print. Especially, this book covers Bayesian probabilities, false negative and false positive calculations. Statistics is an applied field with a wide range of practical applications. You dont have to be a math guru to learn from real, interesting data. Data are messy, and statistical tools are imperfect. 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). I also found it very refreshing to see a wide variability of fields and topics represented in the practice problems. web jul 16 2016 openintro statistics fourth edition the solutions are available online i would suggest this book to everyone who has no 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. The text book contains a detailed table of contents, odd answers in the back and an index. 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 The book includes examples from a variety of fields (psychology, biology, medicine, and economics to name a few). 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. However, there are a few instances where he/she are used to refer to a "theoretical person" rather than using they/them, Reviewed by Alice Brawley Newlin, Assistant Professor, Gettysburg College on 3/31/20, I found the book to be very comprehensive for an undergraduate introduction to statistics - I would likely skip several of the more advanced sections (a few of these I mention below in my comments on its relevance) for this level, but I was glad The authors used a consistent method of presenting new information and the terminology used throughout the text remained consistent. There is also a list of known errors that shows that errors are fixed in a timely manner. 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. They authors already discussed 1-sample inference in chapter 4, so the first two sections in chapter 5 are Paired Data and Difference of Means, then they introduce the t-distribution and go back to 1-sample inference for the mean, and then to inference for two means using he t-distribution. 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. Each chapter contains short sections and each section contains small subsections. 100% 100% found this document not useful, Mark this document as not useful. There is no evidence that the text is culturally insensiteve or offensive. As aforementioned, the authors gently introduce students to very basic statistical concepts. 2019, 422 pages. Also, grouping confidence intervals and hypothesis testing in Ch.5 is odd, when Ch.7 covers hypothesis testing of numerical data. 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. Overall, I recommend this book for an introductory statistics course, however, it has some advanced topics. Search inside document . I use this book in teaching and I did not find any issues with accuracy, inconsistency, or biasness. read more. The definitions and procedures are clear and presented in a framework that is easy to follow. Examples from a variety of disciplines are used to illustrate the material. This is sometimes a problem in statistics as there are a variety of ways to express the similar statistical concepts. 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 text is free of significant interface issues. The later chapters (chapters 4-8) are built upon the knowledge from the former chapters (chapters 1-3). Some examples are related to United States. In addition, the book is written with paragraphs that make the text readable. 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. The interface is nicely designed. Find step-by-step expert solutions for your textbook or homework problem Overall it was not offensive to me, but I am a college-educated white guy. 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. Most contain glaring conceptual and pedagogical errors, and are painful to read (don't get me started on percentiles or confidence intervals). Although accurate, I believe statistics textbooks will increasingly need to incorporate non-parametric and computer-intensive methods to stay relevant to a field that is rapidly changing. read more. I did have a bit of trouble looking up topics in the index - the page numbers seemed to be off for some topics (e.g., effect size). The probability section uses a data set on smallpox to discuss inoculation, another relevant topic whose topic set could be easily updated. I would consider this "omission" as almost inaccurate. For example, the inference for categorical data chapter is broken in five main section. There are distracting grammatical errors. 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. The structure and organization of this text corresponds to a very classic treatment of the topic. It definitely makes the students more comfortable with learning a new test because its just the same thing with different statistics. This is the third edition and benefits from feedback from prior versions. Marginal notes for key concepts & formulae? The interface is fine. One of the good topics is the random sampling methods, such as simple sample, stratified, cluster, and multistage random sampling methods. There are also short videos for 75% of the book sections that are easy to follow and a plus for students. 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. a first course in probability 9th edition solutions; umn resident health insurance; cartoon network invaded tv tropes. In addition, some topics are marked as special topics. The resources, such as labs, lecture notes, and videos are good resources for instructors and students as well. Share. 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. I do not detect a bias in the work. Professors looking for in-depth coverage of research methods and data collection techniques will have to look elsewhere. The chapter on hypothesis testing is very clear and effectively used in subsequent chapters. The text is written in lucid, accessible prose, and provides plenty of examples for students to understand the concepts and calculations. This book was written with the undergraduate level in mind, but it's also popular in high schools and graduate courses. This open book is licensed under a Creative Commons License (CC BY-SA). I read the physical book, which is easy to navigate through the many references. I did not see any grammatical issues that distract form the content presented. 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, 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 index is decent, but there is no glossary of terms or summary of formula, which is disappointing. This keeps all inference for proportions close and concise helping the reader stay uninterrupted in the topic. There are separate chapters on bi-variate and multiple regression and they work well together. 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. Introducing independence using the definition of conditional probability P(A|B)=P(A) is more accurate and easier for students to understand. The book covers familiar topics in statistics and quantitative analysis and the presentation of the material is accurate and effective. This text book covers most topics that fit well with an introduction statistics course and in a manageable format. I find the content quite relevant. There is more than enough material for any introductory statistics course. Examples stay away from cultural topics. The book is well organized and structured. Reviewed by Bo Hu, Assistant Professor, University of Minnesota on 7/15/14, This book covers topics in a traditional curriculum of an introductory statistics course: probabilities, distributions, sampling distribution, hypothesis tests for means and proportions, linear regression, multiple regression and logistic According to the authors, the text is to help students forming a foundation of statistical thinking and methods, unfortunately, some basic The book does build from a good foundation in univariate statistics and graphical presentation to hypothesis testing and linear regression. There is more than enough material for any introductory statistics course. 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 book started with several examples and case study to introduce types of variables, sampling designs and experimental designs (chapter 1). Some more modern concepts, such as various effect size measures, are not covered well or at all (for example, eta squared in ANOVA). 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. read more. Complete visual redesign. The order of the topics seemed appropriate and not unlike many alternatives, but there was the issue of the term highlight boxes terms mentioned above. Great job overall. The topics are presented in a logical order with each major topics given a thorough treatment. Though I might define p-values and interpret confidence intervals slightly differently. There aren't really any cultural references in the book. The text covers all the core topics of statisticsdata, probability and statistical theories and tools. The purpose of the course is to teach students technical material and the book is well-designed for achieving that goal. 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 material in the book is currently relevant and, given the topic, some of it will never be irrelevant. We don't have content for this book yet. Are used to connect with those from non-western countries contexts and use multiple examples or offensive the... 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