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 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. I feel that the greatest strength of this text is its clarity. The book presents all the topics in an appropriate sequence. 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 often assign reading and homework before I discuss topics in lecture. The task of reworking statistical training in response to this crisis will be daunting for any text author not just this one. The book has relevant and easily understood scientific questions. 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. This text covers more advanced graphical su Understanding Statistics and Experimental Design, Empirical Research in Statistics Education, Statistics and Analysis of Scientific Data. Select the Edition for OpenIntro Statistics Below: . 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 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. I did not see any problems in regards to the book's notation or terminology. Mine Cetinkaya-Rundel is the Director of Undergraduate Studies and Assistant Professor of the Practice in the Department of Statistical Science at Duke University. For example, the authors have intentionally included a chapter on probability that some instructors may want to include, but others may choose to excludes without loss of continuity. 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. read more. 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 narrative of the text is grounded in examples which I appreciate. As a mathematician, I find this book most readable, but I imagine that undergraduates might become somewhat confused. 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. Therefore, while the topics are largely the same the depth is lighter in this text than it is in some alternative introductory texts. The bookmarks of chapters are easy to locate. The chapter is about "inference for numerical data". However, I think a greater effort could be made to include more culturally relevant examples in this book. However, after reviewing the textbook at length, I did note that it did become easier to follow the text with the omission of colorful fonts and colors, which may also be noted as distraction for some readers. Reviewed by Leanne Merrill, Assistant Professor, Western Oregon University on 6/14/21, 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. Chapter 7 and 8 cover the linear , multiple and logistic regression. 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 to see them included. 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. 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). It can be considered comprehensive if you consider this an introductory text. read more. The student-facind end, while not flashy or gamified in any way, is easy to navigate and clear. 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. This book is highly modular. OpenIntro Statistics offers a traditional introduction to statistics at the college level. Tables and graphs are sensibly annotated and well organized. This selection of topics and their respective data sets are layered throughout the book. Similar to most intro stat books, it does not cover the Bayesian view at all. The chapter summaries are easy to follow and the order of the chapters begin with "Introduction to Data," which includes treatment I see essentially no errors in this book. Introduction OpenIntro Statistics covers a first course in statistics, providing a rigorous introduction to appliedstatistics that is clear, concise, and accessible. The structure and organization of this text corresponds to a very classic treatment of the topic. The text is easily reorganized and re-sequenced. 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. The text begins with data collection, followed by probability and distributions of a random variable and then finishing (for a Statistics I course) with inference. Then, the basics of both hypothesis tests and confidence intervals are covered in one chapter. The content stays unbiased by constantly reminding the reader to consider data, context and what ones conclusions might mean rather than being partial to an outcome or conclusions based on ones personal beliefs in that the conclusions sense that statistics texts give special. 100% 100% found this document not useful, Mark this document as not useful. 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. I find this method serves to give the students confidence in knowing that they understand concepts before moving on to new material. There are two drawbacks to the interface. This book was written with the undergraduate level in mind, but it's also popular in high schools and graduate courses. 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. Appendix A contains solutions to the end of chapter exercises. I found the overall structure to be standard of an introductory statistics course, with the exception of introducing inference with proportions first (as opposed to introducing this with means first instead). Percentiles? The text is quite consistent in terms of terminology and framework. It is easy to skip some topics with no lack of consistency or confusion. The approach is mathematical with some applications. I found virtually no issues in the grammar or sentence structure of the text. There are lots of graphs in the book and they are very readable. There do not appear to be grammatical errors. of Contents 1. 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. 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 purpose of the course is to teach students technical material and the book is well-designed for achieving that goal. One of the good topics is the random sampling methods, such as simple sample, stratified, The statistical terms, definitions, and equation notations are consistent throughout the text. The pdf and tablet pdf have links to videos and slides. 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. Adv. I did not see any issues with the consistency of this particular textbook. 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. The examples and solutions represent the information with formulas and clear process. OpenIntro Statistics covers a first course in statistics, providing a rigorous introduction to applied statistics that is clear, concise, and accessible. 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). Having a free pdf version and a hard copy for a few dollars is great. No grammatical errors have been found as of yet. This is the third edition and benefits from feedback from prior versions. Comes in pdf, tablet friendly pdf, and printed (15 dollars from amazon as of March, 2019). This is a free textbook for a one-semester, undergraduate statistics course. Given that this is an introductory textbook, it is clearly written and accessible to students with a variety of disciplinary backgrounds. The authors bold important terms, and frequently put boxes around important formulas or definitions. 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. I was concerned that it also might add to the difficulty of analyzing tables. The texts includes basic topics for an introductory course in descriptive and inferential statistics. 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 regression. For examples, the distinction between descriptive statistics and inferential statistics, the measures of central tendency and dispersion. The content is up-to-date. 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. 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. One of the good topics is the random sampling methods, such as simple sample, stratified, cluster, and multistage random sampling methods. Skip Navigation. This keeps all inference for proportions close and concise helping the reader stay uninterrupted in the topic. Each section ends with a problem set. The book has a great logical order, with concise thoughts and sections. This easily allow for small sets of reading on a class to class basis or larger sets of reading over a weekend. Probability is optional, inference is key, and we feature real data whenever . I did not see any inaccuracies in the book. The authors also make GREAT use of statistical graphics in all the chapters. There are a lot of topics covered. More extensive coverage of contingency tables and bivariate measures of association would be helpful. Archive. A teacher can sample the germane chapters and incorporate them without difficulty in any research methods class. The consistency of this text is quite good. The text covers the foundations of data, distributions, probability, regression principles and inferential principles with a very broad net. I do not think that the exercises focus in on any discipline, nor do they exclude any discipline. Perhaps an even stronger structure would see all the types of content mentioned above applied to each type of data collection. Also, non-parametric alternatives would be nice, especially Monte Carlo/bootstrapping methods. My biggest complaint is that The text covers all the core topics of statisticsdata, probability and statistical theories and tools. 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. These blend well with the Exercises that contain the odd solutions at the end of the text. The cons are that the depth is often very light, for example, it would be difficult to learn how to perform simple or multiple regression from this book. One of the real strengths of the book is the many examples and datasets that it includes. This introductory material then serves as the foundation for later chapter where students are introduced to inferential statistical practices. And why dump Ch.6 in between with hypothesis testing of categorical data between them? Errors are not found as of yet. None. So future sections will not rely on them. The drawbacks of the textbook are: 1) it doesn't offer how to use of any computer software or graphing calculator to perform the calculations and analyses; 2) it didn't offer any real world data analysis examples. Think it would work well for liberal arts/social Science students, but there is a Chinese proverb: flaw. Online supplement for TI-83 and TI-84 calculator where students are introduced to inferential statistical practices been found of. Between with hypothesis testing of categorical data between them is about `` inference for numerical data.! 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