More color, diagrams, photos? Students can easily get confused and think the p-value is in favor of the alternative hypothesis. The only issue I had in the layout was that at the end of many sections was a box high-lighting a term. There are labs and instructions for using SAS and R as well. 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 approach of introducing the inferences of proportions and the Chi-square test in the same chapter is novel. I was concerned that it also might add to the difficulty of analyzing tables. There are a lot of topics covered. 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 The book provides an effective index. The coverage of this text conforms to a solid standard (very classical) semester long introductory statistics course that begins with descriptive statistics, basic probability, and moves through the topics in frequentist inference including basic hypothesis tests of means, categories, linear and multiple regression. Reviewed by Greg McAvoy, Professor, University of North Carolina at Greensboro on 12/5/16, The book covers the essential topics in an introductory statistics course, including hypothesis testing, difference of means-tests, bi-variate regression, and multivariate regression. The writing in this book is very clear and straightforward. Archive. Also, non-parametric alternatives would be nice, especially Monte Carlo/bootstrapping methods. I also appreciated that the authors use examples from the hard sciences, life sciences, and social sciences. The text offered quite a lot of examples in the medical research field and that is probably related to the background of the authors. The graphs are readable in black and white also. This open book is licensed under a Creative Commons License (CC BY-SA). For a Statistics I course at most community colleges and some four year universities, this text thoroughly covers all necessary topics. The text is culturally inclusive with examples from diverse industries. It's very fitting for my use with teachers whose primary focus is on data analysis rather than post-graduate research. "Data" is sometimes singular, sometimes plural in the authors' prose. There are two drawbacks to the interface. I am not necessarily in disagreement with the authors, but there is a clear voice. It is certainly a fitting means of introducing all of these concepts to fledgling research students. I do think a more easily navigable e-book would be ideal. 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. Graphs and tables are clean and clearly referenced, although they are not hyperlinked in the sections. Reviewed by Monte Cheney, Associate Professor, Central Oregon Community College on 1/15/21, 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, There are a few color splashes of blue and red in diagrams or URL's. Each section ends with a problem set. This problem has been solved: Problem 1E Chapter CH1 Problem 1E Step-by-step solution Step 1 of 5 Refer to the contingency table in problem 1.1 of the textbook to answer the questions. It strikes me as jumping around a bit. read more. This text does indicate that some topics can be omitted by identifying them as 'special topics'. 0% 0% found this document useful, Mark this document as useful. Most essential materials for an introductory probability and statistics course are covered. You are on page 1 of 3. The final chapters, "Introduction to regression analysis" and "Multiple and logistical regression" fit nicely at the end of the text book. 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. I do wonder about accessibility (for blind or deaf/HoH students) in this book since I don't see it clearly addressed on the website. 100% 100% found this document not useful, Mark this document as not useful. My biggest complaint is that one-sided tests are basically ignored. All of the chapters contain a number of useful tips on best practices and common misunderstandings in statistical analysis. The terms and notation are consistent throughout the text. Statistics and Probability Statistics and Probability solutions manuals OpenIntro Statistics 4th edition We have solutions for your book! OpenIntro Statistics supports flexibility in choosing and ordering topics. For example, types of data, data collection, probability, normal model, confidence intervals and inference for OpenIntro Statistics covers a first course in statistics, providing a rigorous introduction to appliedstatistics that is clear, concise, and accessible. The text is written in lucid, accessible prose, and provides plenty of examples for students to understand the concepts and calculations. 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). The language seems to be free of bias. 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. 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). OpenIntro Statistics offers a traditional introduction to statistics at the college level. I did not see any issues with accuracy, though I think the p-value definition could be simplified. Embed. So future sections will not rely on them. This is a good position to set up the thought process of students to think about how statisticians collect data. read more. The examples flow nicely into the guided practice problems and back to another example, definition, set of procedural steps, or explanation. This is a free textbook for a one-semester, undergraduate statistics course. One of the good topics is the random sampling methods, such as simple sample, stratified, cluster, and multistage random sampling methods. read more. read more. 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. Probability is an important topic that is included as a "special topic" in the course. #. The code and datasets are available to reproduce materials from the book. read more. The wording "at least as favorable to the alternative hypothesis as our current data" is misleading. 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. It defines terms, explains without jargon, and doesnt skip over details. However, it would not suffice for our two-quarter statistics sequence that includes nonparametrics. There are lots of great exercises at the end of each chapter that professors can use to reinforce the concepts and calculations appearing in the chapter. Part I makes key concepts in statistics readily clear. The task of reworking statistical training in response to this crisis will be daunting for any text author not just this one. I do like the case studies, videos, and slides. Download now. The approach is mathematical with some applications. The writing in this book is above average. For example, a goodness of fit test begins by having readers consider a situation of whether or not the ethnic representation of a jury is consistent with the ethnic representation of the area. I did not see much explanation on what it means to fail to reject Ho. The authors use the Z distribution to work through much of the 1-sample inference. read more. The later chapters (chapters 4-8) are built upon the knowledge from the former chapters (chapters 1-3). At first when reviewing, I found it to be difficult for to quickly locate definitions and examples and often focus on the material. The authors do a terrific job in chapter 1 introducing key ideas about data collection, sampling, and rudimentary data analysis. The material was culturally relevant to the demographic most likely to use the text in the United State. 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 first chapter addresses treatments, control groups, data tables and experiments. The textbook has been thoroughly vetted with an estimated 20,000 students using it annually. Especially, this book covers Bayesian probabilities, false negative and false positive calculations. web study with quizlet and memorize flashcards containing terms like 1 1 migraine and . Although accurate, I believe statistics textbooks will increasingly need to incorporate non-parametric and computer-intensive methods to stay relevant to a field that is rapidly changing. The odd-numbered exercises also have answers in the book. Almost every worked example and possible homework exercise in the book is couched in real-world situation, nearly all of which are culturally, politically, and socially relevant. There is an up-to-date errata maintained on the website. If anything, I would prefer the book to have slightly more mathematical notation. This could be either a positive or a negative to individual instructors. The text would surely serve as an excellent supplement that will enhance the curriculum of any basic statistics or research course. It can be considered comprehensive if you consider this an introductory text. 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. This may allow the reader to process statistical terminology and procedures prior to learning about regression. The consistency of this text is quite good. These blend well with the Exercises that contain the odd solutions at the end of the text. Also, grouping confidence intervals and hypothesis testing in Ch.5 is odd, when Ch.7 covers hypothesis testing of numerical data. OpenIntro Statistics is a dynamic take on the traditional curriculum, being successfully used at Community Colleges to the Ivy League. The text is easily and readily divisible into subsections. As a mathematician, I find this book most readable, but I imagine that undergraduates might become somewhat confused. The second is that examples and exercises are numbered in a similar manner and students frequently confuse them early in the class. The organization is fine. I did not see any issues with the consistency of this particular textbook. The Guided Practice problems allow students to try a problem with the solution in the footnote at the bottom. This book covers almost all the topics needed for an introductory statistics course from introduction to data to multiple and logistic regression models. The interface is nicely designed. read more. 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. 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. 4th edition solutions and quizlet . If the volunteer sample is covered also that would be great because it is very common nowadays. For example, the inference for categorical data chapter is broken in five main section. Each topic builds on the one before it in any statistical methods course. Our inaugural effort is OpenIntro Statistics. In particular, I like that the probability chapter (which comes early in the text) is not necessary for the chapters on inference. None of the examples seemed alarming or offensive. On occasion, all of us in academia have experienced a text where the progression from one chapter to another was not very seamless. I would consider this "omission" as almost inaccurate. There are a lot of topics covered. The structure and organization of this text corresponds to a very classic treatment of the topic. This is a particular use of the text, and my students would benefit from and be interested in more social-political-economic examples. In some instances, various groups of students may be directed to certain chapters, while others hone in on that material relevant to their topic. There are sections that can be added and removed at the instructors discretion. The content that this book focuses on is relatively stable and so changes would be few and far between. 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). 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 book covers the essential topics in an introductory statistics course, including hypothesis testing, difference of means-tests, bi-variate regression, and multivariate regression. 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). It is clear that the largest audience is assumed to be from the United States as most examples draw from regions in the U.S. My interest in this text is for a graduate course in applied statistics in the field of public service.
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