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. Overall, I recommend this book for an introductory statistics course, however, it has some advanced topics. 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. 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. And why dump Ch.6 in between with hypothesis testing of categorical data between them? The authors bold important terms, and frequently put boxes around important formulas or definitions. 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). The index is decent, but there is no glossary of terms or summary of formula, which is disappointing. I would tend to group this in with sampling distributions. See examples below: Observational study: Observational study is the one where researchers observe the effect of. The language seems to be free of bias. Having a free pdf version and a hard copy for a few dollars is great. The pros are that it's small enough that a person can work their way through it much faster than would be possible with many of the alternatives. The book is clear and well written. There are also matching videos for students who need a little more help to figure something out. The format is consistent throughout the textbook. 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. Typos that are identified and reported appear to be fixed within a few days which is great. It's very fitting for my use with teachers whose primary focus is on data analysis rather than post-graduate research. Reviewed by Elizabeth Ward, Assistant Professor , James Madison University on 3/11/19, Covers all of the topics usually found in introductory statistics as well as some extra topics (notably: log transforming data, randomization tests, power calculation, multiple regression, logistic regression, and map data). It would be nice to have an e-book version (though maybe I missed how to access this on the website). There do not appear to be grammatical errors. I do think a more easily navigable e-book would be ideal. I found the content in the 4th edition is extremely up-to-date - both in terms of its examples, and in terms of keeping up with the "movements" in many disciplines to be more transparent and considered in hypothesis testing choices (e.g., all hypothesis tests are two-tailed [though the reasoning for this is explained, especially in Section 5.3.7 on one-tailed tests), they include Bayes' theorem, many less common distributions for the introductory level like Bernoulli and Poisson, and estimating statistical power/desired sample size). It is accurate. The book has relevant and easily understood scientific questions. I think that these features make the book well-suited to self-study. The final chapters, "Introduction to regression analysis" and "Multiple and logistical regression" fit nicely at the end of the text book. 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; Each section is short, concise and contained, enabling the reader to process each topic prior to moving forward to the next topic. There aren't really any cultural references in the book. 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. The colors of the font and tables in the textbook are mostly black and white. I think that the first chapter has some good content about experiments vs. observational studies, and about sampling. 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! Many examples use real data sets that are on the larger side for intro stats (hundreds or thousands of observations). I found virtually no issues in the grammar or sentence structure of the text. Better than most of the introductory book that I have used thus far (granted, my books were more geared towards engineers). The book does build from a good foundation in univariate statistics and graphical presentation to hypothesis testing and linear regression. Comes in pdf, tablet friendly pdf, and printed (15 dollars from amazon as of March, 2019). No grammatical errors have been found as of yet. I do like the case studies, videos, and slides. There are a few instances referencing specific technology (such as iPods) that makes the text feel a bit dated. "Standard error" is defined as the "standard deviation associated with an estimate" (p. 163), but it is often unclear whether population or sample-based quantities are being referred to. 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. Most essential materials for an introductory probability and statistics course are covered. The authors introduce a definition or concept by first introducing an example and then reference back to that example to show how that object arises in practice. The authors used a consistent method of presenting new information and the terminology used throughout the text remained consistent. Table. However, the introduction to hypothesis testing is a bit awkward (this is not unusual). 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 discussion of data analysis is appropriately pitched for use in introductory quantitative analysis courses in a variety of disciplines in the social sciences . 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, simulation methods, bootstrap intervals, or CI's for variance, critical value method for testing, and nonparametric methods. Create a clear way to explain this multi-faceted topic and the world will beat a path to your door. Select the Edition for OpenIntro Statistics Below: . It includes too much theory for our undergraduate service courses, but not enough practical details for our graduate-level service courses. This keeps all inference for proportions close and concise helping the reader stay uninterrupted in the topic. This can be particularly confusing to "beginners.". 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. The text is quite consistent in terms of terminology and framework. 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. It would be feasible to use any part of the book without using previous sections as long as students had appropriate prerequisite knowledge. Also, for how the authors seem to be focusing on practicalities, I was somewhat surprised about some of the organization of the inference sections. This text is an excellent choice for an introductory statistics course that has a broad group of students from multiple disciplines. 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. 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. It is as if the authors ran out of gas after the first seven chapters and decided to use the final chapter as a catchall for some important, uncovered topics. Embed. More extensive coverage of contingency tables and bivariate measures of association would be helpful. Ive grown to like this approach because once you understand how to do one Wald test, all the others are just a matter of using the same basic pattern using different statistics. The texts includes basic topics for an introductory course in descriptive and inferential statistics. 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. 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. I realize this is how some prefer it, but I think introducing the t distribution sooner is more practical. OpenIntro Statistics covers a first course in statistics, providing a rigorous introduction to appliedstatistics that is clear, concise, and accessible. read more. The content is well-organized. I find this method serves to give the students confidence in knowing that they understand concepts before moving on to new material. . In addition to the above item-specific comments: #. The section on model selection, covering just backward elimination and forward selection, seems especially old-fashioned. It is certainly a fitting means of introducing all of these concepts to fledgling research students. The students can easily see the connections between the two types of tests. The authors make effective use of graphs both to illustrate the This is especially true when there are multiple authors. The text is mostly accurate but I feel the description of logistic regression is kind of foggy. There are a lot of topics covered. Technical accuracy is a strength for this text especially with respect to underlying theory and impacts of assumptions. The interface of the book appears to be fine for me, but more attractive colors would make it better. The graphs and tables in the text are well designed and accurate. There are a lot of topics covered. 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. Some examples are related to United States. Especially, this book covers Bayesian probabilities, false negative and false positive calculations. A teacher can sample the germane chapters and incorporate them without difficulty in any research methods class. The examples are general and do not deal with racial or cultural matters. Choosing the population proportion rather than the population mean to be covered in the foundation for inference chapter is a good idea because it is easier for students to understand compared to the population mean. This defect is not present here: this text embraces an 'embodied' view of learning which prioritizes example applications first and then explanation of technique. This was not necessarily the case with some of the tables in the text. read more. This topic is usually covered in the middle of a textbook. For example, types of data, data collection, probability, normal model, confidence intervals and inference for I did not see much explanation on what it means to fail to reject Ho. The fourth edition is a definite improvement over previous editions, but still not the best choice for our curriculum. 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 coverage of probability and statistics is, for the most part, sound. Save Save Solutions to Openintro Statistics For Later. It recognizes the prevalence of technology in statistics and covers reading output from software. Statistical methods, statistical inference and data analysis techniques do change much over time; therefore, I suspect the book will be relevant for years to come. This easily allow for small sets of reading on a class to class basis or larger sets of reading over a weekend. The color graphics come through clearly and the embedded links work as they should. An interesting note is that they introduce inference with proportions before inference with means. The lack of discussion/examples/inclusion of statistical software or calculator usage is disappointing, as is the inclusion of statistical inference using critical values. These concepts are reinforced by authentic examples that allow students to connect to the material and see how it is applied in the real world. OpenIntro Statistics 4th Edition by David Diez, Christopher Barr, Mine etinkaya-Rundel: 250: Join Chegg Study and get: Guided textbook solutions created by . 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