My interest in this text is for a graduate course in applied statistics in the field of public service. For faculty, everything is very easy to find on the OpenIntro website. 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. Appendix A contains solutions to the end of chapter exercises. 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. Some of the more advanced topics are treated as 'special topics' within the sections (e.g., power and standard error derivations). 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. I was impressed by the scope of fields represented in the example problems - everything from estimating the length of possums' heads, to smoke inhalation in one's line of work, to child development, and so on. I did not find any grammatical errors or typos. The section on model selection, covering just backward elimination and forward selection, seems especially old-fashioned. Then, the basics of both hypothesis tests and confidence intervals are covered in one chapter. The consistency of this text is quite good. I found the book's prose to be very straightforward and clear overall. Each topic builds on the one before it in any statistical methods course. The book is well organized and structured. The chapter summaries are easy to follow and the order of the chapters begin with "Introduction to Data," which includes treatment OpenIntro Statistics supports flexibility in choosing and ordering topics. There are also pictures in the book and they appear clear and in the proper place in the chapters. The language seems to be free of bias. 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 basic theory is well covered and motivated by diverse examples from different fields. The topics are not covered in great depth; however, as an introductory text, it is appropriate. These concepts should be clarified at the first chapter. In particular, examples and datasets about county characteristics, elections, census data, etc, can become outdated fairly quickly. The organization of the topics is unique, but logical. 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. I would tend to group this in with sampling distributions. The probability section uses a data set on smallpox to discuss inoculation, another relevant topic whose topic set could be easily updated. 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 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. The introduction of jargon is easy streamlined in after this example introduction. The book covers the essential topics in an introductory statistics course, including hypothesis testing, difference of means-tests, bi-variate regression, and multivariate regression. 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. 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 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. Students are able to follow the text on their own. 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. read more. Notation, language, and approach are maintained throughout the chapters. 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. This ICME-13 Topical Survey provides a review of recent research into statistics education, with a focus on empirical research published in established educational journals and on the proceedings of important conferences on statistics education. It is easy to skip some topics with no lack of consistency or confusion. I have seen other texts begin with correlation and regression prior to tests of means, etc., and wonder which approach is best. It is accurate. The bookmarks of chapters are easy to locate. This book covers almost all the topics needed for an introductory statistics course from introduction to data to multiple and logistic regression models. This is sometimes a problem in statistics as there are a variety of ways to express the similar statistical concepts. The p-value definition could be simplified by eliminating mention of a hypothesis being tested. Teachers looking for a text that they can use to introduce students to probability and basic statistics should find this text helpful. I also appreciated that the authors use examples from the hard sciences, life sciences, and social sciences. An interesting note is that they introduce inference with proportions before inference with means. Try Numerade free. I think that the first chapter has some good content about experiments vs. observational studies, and about sampling. Print. OpenIntro Statistics Solutions for OpenIntro Statistics 4th David M. Diez Get access to all of the answers and step-by-step video explanations to this book and +1,700 more. For example, the Central Limit Theorem is introduced and used early in the inference section, and then later examined in more detail. The approach is mathematical with some applications. This text covers more advanced graphical su Understanding Statistics and Experimental Design, Empirical Research in Statistics Education, Statistics and Analysis of Scientific Data. Embed. Examples stay away from cultural topics. The graphs and diagrams were also clear and provided information in a way that aided in understanding concepts. There are also a number of exercises embedded in the text immediately after key ideas and concepts are presented. While the examples did connect with the diversity within our country or i.e. The color graphics come through clearly and the embedded links work as they should. The text is well-written and with interesting examples, many of which used real data. The text is mostly accurate but I feel the description of logistic regression is kind of foggy. 4th edition solutions and quizlet . 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. read more. This book is quite good and is ethically produced. 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. The authors are sloppy in their use of hat notation when discussing regression models, expressing the fitted value as a function of the parameters, instead of the estimated parameters (pp. The text is easy to read without a lot of distracting clutter. It is certainly a fitting means of introducing all of these concepts to fledgling research students. 2017 Generation of Electrical Energy is written primarily for the undergraduate students of electrical engineering while also covering the syllabus of AMIE and act as a I did not find any issues with consistency in the text, though it would be nice to have an additional decimal place reported for the t-values in the t-table, so as to make the presentation of corresponding values between the z and t-tables easier to introduce to students (e.g., tail p of .05 corresponds to t of 1.65 - with rounding - in large samples; but the same tail p falls precisely halfway between z of 1.64 and z of 1.65). The text is written in lucid, accessible prose, and provides plenty of examples for students to understand the concepts and calculations. I believe students, as well as, instructors would find these additions helpful. 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. Most essential materials for an introductory probability and statistics course are covered. The authors make effective use of graphs both to illustrate the For a Statistics I course at most community colleges and some four year universities, this text thoroughly covers all necessary topics. The writing could be slightly more inviting, and concept could be more readily introduced via accessible examples more often. The book is very consistent from what I can see. Probability is an important topic that is included as a "special topic" in the course. 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. The chapter summaries are easy to follow and the order of the chapters begin with "Introduction to Data," which includes treatment and control groups, data tables and experiments. 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 have no idea how to characterize the cultural relevance of a statistics textbook. The organization/structure provides a smooth way for the contents to gradually progress in depth and breadth. Reviewed by Lily Huang, Adjunct Math Instructor , Bethel University on 11/13/18, The text covers all the core topics of statisticsdata, probability and statistical theories and tools. #. The text is quite consistent in terms of terminology and framework. read more. 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. 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. read more. I did not see any issues with accuracy, though I think the p-value definition could be simplified. Quite clear. Percentiles? The authors make effective use of graphs both to illustrate the 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 use a method inclusive of examples (noted with a Blue Dot), guided practice (noted by a large empty bullet), and exercises (found at end of each chapter). Overall, I liked the book. read more. Also, as fewer people do manual computations, interpretation of computer software output becomes increasingly important. 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. Also, I had some issues finding terms in the index. The book has a great logical order, with concise thoughts and sections. These are essential components of quantitative analysis courses in the social sciences. Black and white paperback edition. Parts I and II give an overview of the most common tests (t-test, ANOVA, correlations) and work out their statistical principles. This could be either a positive or a negative to individual instructors. The prose is sometimes tortured and imprecise. Each section ends with a problem set. One of the good topics is the random sampling methods, such as simple sample, stratified, cluster, and multistage random sampling methods. read more. The terms and notation are consistent throughout the text. Some topics in descriptive statistics are presented without much explanation, such as dotplots and boxplots. 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). 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. 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.
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