The only visual issues occurs in some graphs, such as on page 40-41, which have maps of the U.S. using color to show “intensity”. The task of reworking statistical training in response to this crisis will be daunting for any text author not just this one. Chapter 4-6 cover the inferences for means and proportions and the Chi-square test. It strikes me as jumping around a bit. The interface is fine. The regression treatment of categorical predictors is limited to dummy coding (though not identified as such) with two levels in keeping with the introductory nature of the text. 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). 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. Please note that a derivative work cannot be branded or titled in a way that suggests the derivative work is an OpenIntro product or is endorsed by OpenIntro. 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. openintro statistics 4th edition solutions pdf. Overall the organization is good, so I'm still rating it high, but individual instructors may disagree with some of the order of presentation. In general I was satisfied. The authors spend many pages on the sampling distribution of mean in chapter 4, but only a few sentences on the sampling distribution of proportion in chapter 6; 2) the authors introduced independence after talking about the conditional probability. 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 second is that “examples” and “exercises” are numbered in a similar manner and students frequently confuse them early in the class. If you would like for a derivative work to branded as an OpenIntro resource (e.g. They have done an excellent job choosing ones that are likely to be of interest to and understandable by students with diverse backgrounds. More at the License webpage. 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. It does a more thorough job than most books of covering ideas about data, study design, summarizing data and displaying data. 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. I would tend to group this in with sampling distributions. This text is an excellent choice for an introductory statistics course that has a broad group of students from multiple disciplines. I did not see much explanation on what it means to fail to reject Ho. The topics are presented in a logical order with each major topics given a thorough treatment. Some of these will continue to be useful over time, but others may be may have a shorter shelf life. The introduction of jargon is easy streamlined in after this example introduction. read more. It is certainly a fitting means of introducing all of these concepts to fledgling research students. There are two drawbacks to the interface. The content stays unbiased by constantly reminding the reader to consider data, context and what one’s conclusions might mean rather than being partial to an outcome or conclusions based on one’s personal beliefs in that the conclusions sense that statistics texts give special. The authors make effective use of graphs both to illustrate the... It is especially well suited for social science undergraduate students. This could make it easier for students or instructors alike to identify practice on particular concepts, but it may make it more difficult for students to grasp the larger picture from the text alone. None of the examples seemed alarming or offensive. Chapters 1 through 4, covering data, probability, distributions, and principles of inference flow nicely, but the remaining chapters seem like a somewhat haphazard treatment of some commonly used methods. The issue I had with this was that I found the definitions within these boxes to often be more clear than when the term was introduced earlier, which often made me go looking for these boxes before I reached them naturally. There are labs and instructions for using SAS and R as well. 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. Textbook Pedagogy . openintro-statistics.Rproj . "Data" is sometimes singular, sometimes plural in the authors' prose. While the text could be used in both undergraduate and graduate courses, it is best suited for the social sciences. I am not necessarily in disagreement with the authors, but there is a clear voice. This text will be useful as a supplement in the graduate course in applied statistics for public service.