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. One of the strengths of this text is the use of motivated examples underlying each major technique. Most contain glaring conceptual and pedagogical errors, and are painful to read (don't get me started on percentiles or confidence intervals). The rationale for assigning topics in Section 1 and 2 is not clear. The learner cant capture what is logistic regression without a clear definition and explanation. Given that this is an introductory textbook, it is clearly written and accessible to students with a variety of disciplinary backgrounds. The textbook offers companion data sets on their website, and labs based on the free software, R and Rstudio. This is a free textbook for a one-semester, undergraduate statistics course. From what I can tell, the book is accurate in terms of what it covers. 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. According to the authors, the text is to help students forming a foundation of statistical thinking and methods, unfortunately, some basic Great job overall. 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. The textbook price was updated from $14.99 for the 3rd Edition to $20 for the 4th Edition, which we believe will be a sustainable price point that helps support OpenIntro as it scales into new subjects. There are a variety of exercises that do not represent insensitivity or offensive to the reader. 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. 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 examples are general and do not deal with racial or cultural matters. Create a clear way to explain this multi-faceted topic and the world will beat a path to your door. While to some degree the text is easily and readily divisible into smaller reading sections, I would not recommend that anyone alter the sequence of the content until after Chapters 1, 3, and 4 are completed. It is certainly a fitting means of introducing all of these concepts to fledgling research students. There are also short videos for 75% of the book sections that are easy to follow and a plus for students. Percentiles? OpenIntro Statistics 4th Edition by David Diez, Christopher Barr, Mine etinkaya-Rundel: 250: Join Chegg Study and get: Guided textbook solutions created by . The narrative of the text is grounded in examples which I appreciate. David M. Diez, Harvard School of Public Health, Christopher D. Barr, Harvard School of Public Health, Reviewed by Hamdy Mahmoud, Collegiate Assistant Professor, Virginia Tech on 5/16/22, This book covers almost all the topics needed for an introductory statistics course from introduction to data to multiple and logistic regression models. I think that the book is fairly easy to read. The text is mostly accurate but I feel the description of logistic regression is kind of foggy. The drawback of this book is that it does not cover how to use any computer software or even a graphing calculator to perform the calculations for inferences. This may allow the reader to process statistical terminology and procedures prior to learning about regression. Introductory statistics courses prepare students to think statistically but cover relatively few statistical methods. The availability of data sets and functions at a website (www.openintro.org) and as an R package (cran.r-project.org/web/packages/openintro) is a huge plus that greatly increases the usefulness of the text. The most accurate open-source textbook in statistics I have found. read more. For example, the inference for categorical data chapter is broken in five main section. For examples, the distinction between descriptive statistics and inferential statistics, the measures of central tendency and dispersion. read more. This book differs a bit in its treatment of inference. There are no issues with the grammar in the book. I did not see any issues with accuracy, though I think the p-value definition could be simplified. The basic theory is well covered and motivated by diverse examples from different fields. read more. Online supplements cover interactions and bootstrap confidence intervals. 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. If the volunteer sample is covered also that would be great because it is very common nowadays. This introductory material then serves as the foundation for later chapter where students are introduced to inferential statistical practices. read more. The real data sets examples cover different topics, such as politics, medicine, etc. The book has a great logical order, with concise thoughts and sections. The fourth edition is a definite improvement over previous editions, but still not the best choice for our curriculum. The subsequent chapters have all of the specifics about carrying out hypothesis tests and calculating intervals for different types of data. 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. I didn't experience any problems. 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). The basics of classical inferential statistics changes little over time and this text covers that ground exceptionally well. The book is divided into many subsections. Overall, this is the best open-source statistics text I have reviewed. The graphs and diagrams were also clear and provided information in a way that aided in understanding concepts. Some of the sections have only a few exercises, and more exercises are provided at the end of chapters. For the most part, examples are limited to biological/medical studies or experiments, so they will last. This open access textbook provides the background needed to correctly use, interpret and understand statistics and statistical data in diverse settings. There is no evidence that the text is culturally insensiteve or offensive. Additionally, as research and analytical methods evolve, then so will the need to cover more non-traditional types of content i.e mixed methodologies, non parametric data sets, new technological research tools etc. 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). I think that these features make the book well-suited to self-study. Then, the basics of both hypothesis tests and confidence intervals are covered in one chapter. There are also pictures in the book and they appear clear and in the proper place in the chapters. The text is easy to read without a lot of distracting clutter. The book is well organized and structured. OpenIntro Statistics. This book was written with the undergraduate level in mind, but it's also popular in high schools and graduate courses. This is the most innovative and comprehensive statistics learning website I have ever seen. The pdf is likely accessible for screen readers, though. The book provides an effective index. For 24 students, the average score is 74 points with a standard deviation of 8.9 points. The text is accurate due to its rather straight forward approach to presenting material. The content is up-to-date. 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 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. Examples stay away from cultural topics. This is the third edition and benefits from feedback from prior versions. In fact, I particularly like that the authors occasionally point out means by which data or statistics can be presented in a method that can distort the truth. Corresponding textbook Intro Stats | 4th Edition ISBN-13: 9780321825278 ISBN: 0321825276 Authors: Richard D. De Veaux, Paul F Velleman, David E. Bock Rent | Buy Alternate ISBN: 9780134429021, 9780321826213, 9780321925565, 9780321932815 Solutions by chapter Chapter 1 Chapter 2 Chapter 3 Chapter 4 Chapter 5 Chapter 6 Chapter 7 Chapter 8 Chapter 9 In addition, it is easy to follow. There are a lot of topics covered. The examples flow nicely into the guided practice problems and back to another example, definition, set of procedural steps, or explanation. While the text could be used in both undergraduate and graduate courses, it is best suited for the social sciences. Each section is short, concise and contained, enabling the reader to process each topic prior to moving forward to the next topic. There is more than enough material for any introductory statistics course. Jargon is introduced adequately, though. Percentiles? 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. 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). It should be appealing to the learners, dealing with a real-life case for better and deeper understanding of Binomial distribution, Normal approximation to the Binomial distribution. 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. The topics are presented in a logical order with each major topics given a thorough treatment. The terms and notation are consistent throughout the text. Generation of Electrical Energy, 7th Edition Gupta B.R. I often assign reading and homework before I discuss topics in lecture. In fact, I could not differentiate a change in style or clarity in any sections of this text. 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. For example, the Central Limit Theorem is introduced and used early in the inference section, and then later examined in more detail. 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. Most of the examples are general and not culturally related. The final chapters, "Introduction to regression analysis" and "Multiple and logistical regression" fit nicely at the end of the text 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. Mine Cetinkaya-Rundel is the Director of Undergraduate Studies and Assistant Professor of the Practice in the Department of Statistical Science at Duke University. There are distracting grammatical errors. Also, non-parametric alternatives would be nice, especially Monte Carlo/bootstrapping methods. The writing in this book is above average. 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 . Overall, I would consider this a decent text for a one-quarter or one-semester introductory statistics textbook. Complete visual redesign. The authors make effective use of graphs both to illustrate the subject matter and to teach students how to construct and interpret graphs in their own work. The textbook has been thoroughly vetted with an estimated 20,000 students using it annually. Each section ends with a problem set. 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. One of the good topics is the random sampling methods, such as simple sample, stratified, The material in the book is currently relevant and, given the topic, some of it will never be irrelevant. The book was fairly consistent in its use of terminology. The topics are not covered in great depth; however, as an introductory text, it is appropriate. Quite clear. This book does not contain anything culturally insensitive, certainly. But, when you understand the strengthsand weaknesses of these tools, you can use them to learn about the world. Also, the discussion on hypothesis testing could be more detailed and specific. Reviewed by Bo Hu, Assistant Professor, University of Minnesota on 7/15/14, 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 The nicely designed website (https://www.openintro.org) contains abundant resources which are very valuable for both students and teachers, including the labs, videos, forums and extras. Chapter 3 covers random variables and distributions including normal, geometry and binomial distributions. The book is clear and well written. 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. Some topics in descriptive statistics are presented without much explanation, such as dotplots and boxplots. 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 statistical terms, definitions, and equation notations are consistent throughout the text. The texts includes basic topics for an introductory course in descriptive and inferential statistics. Students are able to follow the text on their own. 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. While the traditional curriculum does not cover multiple regression and logistic regression in an introductory statistics course, this book offers the information in these two areas. I find the content to be quite relevant. OpenIntro Statistics 4th Edition. 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. I do like the case studies, videos, and slides. 2019, 422 pages. Some of these will continue to be useful over time, but others may be may have a shorter shelf life. The text is culturally inclusive with examples from diverse industries. Building on the basic statistical thinking emphasized in an introductory course, a second course in statistics at the undergraduate level can explore a large number of statistical methods. Ensure every student can access the course textbook. There are many additional resources available for this book including lecture slides, a free online homework system, labs, sample exams, sample syllabuses, and objectives. Step 2 of 5 (a) Examples of how statistics can address gender bias were appreciated. I do not see introductory statistics content ever becoming obsolete. No display issues with the devices that I have. For faculty, everything is very easy to find on the OpenIntro website. though some examples come from other parts of the world (Greece economics, Australian wildlife). Adv. The topics are not covered in great depth; however, as an introductory text, it is appropriate. 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. The organization is fine. This diversity in discipline comes at the cost of specificity of techniques that appear in some fields such as the importance of measures of effect in psychology. Try Numerade free. It appears smooth and seamless. Two topics I found absent were the calculation of effect sizes, such as Cohen's d, and the coverage of interval and ratio scales of measurement (the authors provide a breakdown of numerical variables as only discrete and continuous). The text is free of significant interface issues. In addition to the above item-specific comments: #. 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. Also, grouping confidence intervals and hypothesis testing in Ch.5 is odd, when Ch.7 covers hypothesis testing of numerical data. 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 examples are up-to-date. The authors use the Z distribution to work through much of the 1-sample inference. differential equations 4th edition solutions and answers quizlet calculus 4th edition . The only issue I had in the layout was that at the end of many sections was a box high-lighting a term. The topics are not covered in great depth; however, as an introductory text, it is appropriate. Overall I like it a lot. Within each chapter are many examples and what the authors call "Guided Practice"; all of these have answers in the book. The resources on the website also are well organized and easy to access and download. Well, this text provides a kinder and gentler introduction to data analysis and statistics. read more. I have not noted any inconsistencies, inaccuracies, or biases. The resources, such as labs, lecture notes, and videos are good resources for instructors and students as well. 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. In addition all of the source code to build the book is available so it can be easily modified. It includes too much theory for our undergraduate service courses, but not enough practical details for our graduate-level service courses. It can be considered comprehensive if you consider this an introductory text. I do think there are some references that may become obsolete or lost somewhat quickly; however, I think a diligent editorial team could easily update data sets and questions to stay current. 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. The discussion of data analysis is appropriately pitched for use in introductory quantitative analysis courses in a variety of disciplines in the social sciences . 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. Similar to most intro This book covers almost all the topics needed for an introductory statistics course from introduction to data to multiple and logistic regression models. 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). The content is well-organized. It covers all the standard topics fully. The second is that examples and exercises are numbered in a similar manner and students frequently confuse them early in the class. These concepts should be clarified at the first chapter. The topics all proceed in an orderly fashion. Intro Stats - 4th Edition - Solutions and Answers | Quizlet Statistics Intro Stats 4th Edition ISBN: 9780321825278 David E. Bock, Paul Velleman, Richard D. De Veaux Textbook solutions Verified Chapter 1: Stats Start Here Exercise 1 Exercise 2 Exercise 3 Exercise 4 Exercise 5 Exercise 6 Exercise 7 Exercise 8 Exercise 9 Exercise 10 Exercise 11 In particular, examples and datasets about county characteristics, elections, census data, etc, can become outdated fairly quickly. This keeps all inference for proportions close and concise helping the reader stay uninterrupted in the topic. The section on model selection, covering just backward elimination and forward selection, seems especially old-fashioned. The probability section uses a data set on smallpox to discuss inoculation, another relevant topic whose topic set could be easily updated. David M. Diez, Mine etinkaya-Rundel, Christopher D. Barr . read more. Table. There are some things that should probably be included in subsequent revisions. Select the Edition for OpenIntro Statistics Below: . The examples and solutions represent the information with formulas and clear process. And why dump Ch.6 in between with hypothesis testing of categorical data between them? The text book contains a detailed table of contents, odd answers in the back and an index. The book does build from a good foundation in univariate statistics and graphical presentation to hypothesis testing and linear regression. 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! 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. This text will be useful as a supplement in the graduate course in applied statistics for public service. In particular, I like that the probability chapter (which comes early in the text) is not necessary for the chapters on inference. The text covers all the core topics of statisticsdata, probability and statistical theories and tools. I have seen other texts begin with correlation and regression prior to tests of means, etc., and wonder which approach is best. The 4th Edition was released on May 1st, 2019. Better than most of the introductory book that I have used thus far (granted, my books were more geared towards engineers). The lack of discussion/examples/inclusion of statistical software or calculator usage is disappointing, as is the inclusion of statistical inference using critical values. My biggest complaint is that Typos and errors were minimal (I could find none). The text provides enough examples, exercises and tips for the readers to understand the materials.
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