The B&W textbook did not seem to pose any problems for me in terms of distortion, understanding images/charts, etc., in print. Many examples use real data sets that are on the larger side for intro stats (hundreds or thousands of observations). This topic is usually covered in the middle of a textbook. None of the examples seemed alarming or offensive. The text is free of significant interface issues. The bookmarks of chapters are easy to locate. The wording "at least as favorable to the alternative hypothesis as our current data" is misleading. 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. #. 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. 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 . Another welcome topic that is not typical of introductory texts is logistic regression, which I have seen many references to in the currently hot topic of Data Science. There is no evidence that the text is culturally insensiteve or offensive. I think that the book is fairly easy to read. the U.K., they may not be the best examples that could be used to connect with those from non-western countries. The reading of the book will challenge students but at the same time not leave them behind. This textbook is nicely parsed. Comes in pdf, tablet friendly pdf, and printed (15 dollars from amazon as of March, 2019). Well, this text provides a kinder and gentler introduction to data analysis and statistics. More color, diagrams, etc.? The text is well-written and with interesting examples, many of which used real data. I didn't experience any problems. 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. 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. read more. It is certainly a fitting means of introducing all of these concepts to fledgling research students. No issues with consistency in that text are found. Examples from a variety of disciplines are used to illustrate the material. The chapters are well organized and many real data sets are analyzed. I use this book in teaching and I did not find any issues with accuracy, inconsistency, or biasness. There are also pictures in the book and they appear clear and in the proper place in the chapters. Chapter 4-6 cover the inferences for means and proportions and the Chi-square test. Books; Study; Career; Life; . 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). I found no problems with the book itself. However, it would not suffice for our two-quarter statistics sequence that includes nonparametrics. The subsequent chapters have all of the specifics about carrying out hypothesis tests and calculating intervals for different types of data. Some of the sections have only a few exercises, and more exercises are provided at the end of chapters. The text provides enough examples, exercises and tips for the readers to understand the materials. The material in the book is currently relevant and, given the topic, some of it will never be irrelevant. The topics all proceed in an orderly fashion. For instance, the text shows students how to calculate the variance and standard deviation of an observed variable's distribution, but does not give the actual formula. Also, as fewer people do manual computations, interpretation of computer software output becomes increasingly important. Mine Cetinkaya-Rundel is the Director of Undergraduate Studies and Assistant Professor of the Practice in the Department of Statistical Science at Duke University. One topic I was surprised to see trimmed and placed online as extra content were the calculations for variance estimates in ANOVA, but these are of course available as supplements for the book. For example, the inference for categorical data chapter is broken in five main section. 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. 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. In other words, breadth, yes; and depth, not so much. There are also matching videos for students who need a little more help to figure something out. Jump to Page . a first course in probability 9th edition solutions; umn resident health insurance; cartoon network invaded tv tropes. I find this method serves to give the students confidence in knowing that they understand concepts before moving on to new material. For example, there is a strong emphasis on assessing the normality assumption, even though most of the covered methods work well for non-normal data with reasonable sample sizes. Also, the convenient sample is covered. This defect is not present here: this text embraces an 'embodied' view of learning which prioritizes example applications first and then explanation of technique. These concepts should be clarified at the first chapter. For example, income variations in two cities, ethnic distribution across the country, or synthesis of data from Africa. The only issue I had in the layout was that at the end of many sections was a box high-lighting a term. The book does build from a good foundation in univariate statistics and graphical presentation to hypothesis testing and linear regression. Things flow together so well that the book can be used as is. The chapter is about "inference for numerical data". One-way analysis of variance is introduced as a special topic, with no mention that it is a generalization of the equal-variances t-test to more than two groups. I see essentially no errors in this book. 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). The authors make effective use of graphs both to illustrate the I was able to read the entire book in about a month by knocking out a couple of subsections per day. This keeps all inference for proportions close and concise helping the reader stay uninterrupted in the topic. Teachers looking for a text that they can use to introduce students to probability and basic statistics should find this text helpful. Better than most of the introductory book that I have used thus far (granted, my books were more geared towards engineers). One of the real strengths of the book is the many examples and datasets that it includes. While the text could be used in both undergraduate and graduate courses, it is best suited for the social sciences. Though I might define p-values and interpret confidence intervals slightly differently. 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. 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. The textbook offers companion data sets on their website, and labs based on the free software, R and Rstudio. Fisher's exact test is not even mentioned. I have not noted any inconsistencies, inaccuracies, or biases. 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. For example, it is claimed that the Poisson distribution is suitable only for rare events (p. 148); the unequal-variances form of the standard error of the difference between means is used in conjunction with the t-distribution, with no mention of the need for the Satterthwaite adjustment of the degrees of freedom (p. 231); and the degrees of freedom in the chi-square goodness-of-fit test are not adjusted for the number of estimated parameters (p. 282). The text needs real world data analysis examples from finance, business and economics which are more relevant to real life. This book is very clearly laid out for both students and faculty. There are no issues with the grammar in the book. As an example, I suggest the text provides data analysis by using Binomial option pricing model and Black-Scholes option pricing model. 191 and 268). It appears to stick to more non-controversial examples, which is perhaps more effective for the subject matter for many populations. The distinction and common ground between standard deviation and standard error needs to be clarified. 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). Labs are available in many modern software: R, Stata, SAS, and others. Like most statistics books, each topic builds on ones that have come before and readers will have no trouble following the terminology as they progress through the book. I did not see much explanation on what it means to fail to reject Ho. In some instances, various groups of students may be directed to certain chapters, while others hone in on that material relevant to their topic. Getting Started Amazon links on openintro.org or in products are affiliate links. Notation is consistent and easy to follow throughout the text. Reviewed by Kendall Rosales, Instructor and Service Level Coordinator, Western Oregon University on 8/20/20, There is more than enough material for any introductory statistics course. I think that the first chapter has some good content about experiments vs. observational studies, and about sampling. 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). 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 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 content that this book focuses on is relatively stable and so changes would be few and far between. 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. 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. Some more separation between sections, and between text vs. exercises would be appreciated. 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. Some topics in descriptive statistics are presented without much explanation, such as dotplots and boxplots. The textbook has been thoroughly vetted with an estimated 20,000 students using it annually. read more. I find the content to be quite relevant. The terms and notation are consistent throughout the text. The revised 2nd edition of this book provides the reader with a solid foundation in probability theory and statistics as applied to the physical sciences, engineering and related fields. The texts selection for notation with common elements such as p-hat, subscripts, compliments, standard error and standard deviation is very clear and consistent. The probability section uses a data set on smallpox to discuss inoculation, another relevant topic whose topic set could be easily updated. The definitions are clear and easy to follow. In other cases I found the omissions curious. 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. Although there are some 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 resources, such as labs, lecture notes, and videos are good resources for instructors and students as well. There are a lot of topics covered. The writing style and context to not treat students like Phd academics (too high of a reading level), nor does it treat them like children (too low of a reading level). There are lots of graphs in the book and they are very readable. This is a statistics text, and much of the content would be kept in this order. There is also a list of known errors that shows that errors are fixed in a timely manner. 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. Extra Content. 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 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. 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 is some bias in terms of what the authors prioritize. 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