The topics are not covered in great depth; however, as an introductory text, it is appropriate. The graphs are readable in black and white also. The text is organized into sections, and the numbering system within each chapter facilitates assigning sections of a chapter. 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. My biggest complaint is that This is a particular use of the text, and my students would benefit from and be interested in more social-political-economic examples. I think that the book is fairly easy to read. No issues with consistency in that text are found. 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. Things flow together so well that the book can be used as is. See examples below: Observational study: Observational study is the one where researchers observe the effect of. I was able to read the entire book in about a month by knocking out a couple of subsections per day. 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. 325 and 357). It begins with the basics of descriptive statistics, probability, hypothesis test concepts, tests of numerical variables, categorical, and ends with regression. Many OERs (and published textbooks) are difficult to convert from a typical 15-week semester to a 10-week term, but not this one! 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, 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. In addition, it is easy to follow. There is an up-to-date errata maintained on the website. 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. Also, the discussion on hypothesis testing could be more detailed and specific. The chapter on hypothesis testing is very clear and effectively used in subsequent chapters. The examples were up-to-date, for example, discussing the fact that Google conducts experiments in which different users are given search results in different ways to compare the effectiveness of the presentations. It is accurate. All of the chapters contain a number of useful tips on best practices and common misunderstandings in statistical analysis. Overall, the book is heavy on using ordinary language and common sense illustrations to get across the main ideas. 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. Reviewed by Robin Thomas, Professor, Miami University, Ohio on 8/21/16, 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 The chapters are well organized and many real data sets are analyzed. The organization in chapter 5 also seems a bit convoluted to me. The authors use the Z distribution to work through much of the 1-sample inference. 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. Use of the t-distribution is motivated as a way to "resolve the problem of a poorly estimated standard error", when really it is a way to properly characterize the distribution of a test statistic having a sample-based standard error in the denominator. 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 Access even-numbered exercise solutions. At the same time, the material is covered in such a matter as to provide future research practitioners with a means of understanding the possibilities when considering research that may prove to be of value in their respective fields. Ability to whitelist other teachers so they can immediately get full access to teacher resources on openintro.org. samsung neo g8 firmware update; acoustic guitar with offset soundhole; adapt email finder chrome extension; doordash q1 2022 earnings This book offers an easily accessible and comprehensive guide to the entire market research process, from asking market research questions to collecting and analyzing data by means of quantitative methods. For example, the authors have intentionally included a chapter on probability that some instructors may want to include, but others may choose to excludes without loss of continuity. Nothing was jarring in this aspect, and the sections/chapters were consistent. This book differs a bit in its treatment of inference. There are also matching videos for students who need a little more help to figure something out. There is some bias in terms of what the authors prioritize. This book is very readable. Register and become a verified teacher on openintro.org (free!) The introduction of jargon is easy streamlined in after this example introduction. The text includes sections that could easily be extracted as modules. In addition, some topics are marked as special topics. Some examples are related to United States. 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. read more. My biggest complaint is that one-sided tests are basically ignored. The book is divided into many subsections. 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. Also, for how the authors seem to be focusing on practicalities, I was somewhat surprised about some of the organization of the inference sections. It would be nice to have an e-book version (though maybe I missed how to access this on the website). 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. #. Overall it was not offensive to me, but I am a college-educated white guy. There is also a list of known errors that shows that errors are fixed in a timely manner. For one. The book reads cleanly throughout. 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. I did not see any issues with accuracy, though I think the p-value definition could be simplified. It does a more thorough job than most books of covering ideas about data, study design, summarizing data and displaying data. Probability is an important topic that is included as a "special topic" in the course. This book can work in a number of ways. The examples flow nicely into the guided practice problems and back to another example, definition, set of procedural steps, or explanation. Everything appeared to be accurate. No problems, but again, the text is a bit dense. This text will be useful as a supplement in the graduate course in applied statistics for public service. OpenIntro Statistics covers a first course in statistics, providing a rigorous introduction to applied statistics that is clear, concise, and accessible. However, there are some sections that are quite dense and difficult to follow. None of the examples seemed alarming or offensive. Skip Navigation. Marginal notes for key concepts & formulae? 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. Also, I had some issues finding terms in the index. There are two drawbacks to the interface. The content is up-to-date. (Unlike many modern books that seem to have random sentences scattered in between bullet points and boxes.). The only issue I had in the layout was that at the end of many sections was a box high-lighting a term. 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. read more. 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. Join Free Today Chapters 1 Introduction to Data 4 sections 60 questions RK 2 Summarizing data 3 sections 26 questions RK 3 Probability 5 sections 47 questions Updates and supplements for new topics have been appearing regularly since I first saw the book (in 2013). The first chapter addresses treatments, control groups, data tables and experiments. 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 text is in PDF format; there are no problems of navigation. The only visual issues occurs in some graphs, such as on page 40-41, which have maps of the U.S. using color to show intensity. The language seems to be free of bias. In addition, the book is written with paragraphs that make the text readable. Black and white paperback edition. Introductory statistics courses prepare students to think statistically but cover relatively few statistical methods. Most of the examples are general and not culturally related. More extensive coverage of contingency tables and bivariate measures of association would be helpful. 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; OpenIntro Statistics 4th Edition by David Diez, Christopher Barr, Mine etinkaya-Rundel: 250: Join Chegg Study and get: Guided textbook solutions created by . 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. For example, when introducing the p-value, the authors used the definition "the probability of observing data at least as favorable to the alternative hypothesis as our current data set, if the null hypothesis is true." I didn't experience any problems. There are a variety of exercises that do not represent insensitivity or offensive to the reader. 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. The modularity is creative and compares well. Some of these will continue to be useful over time, but others may be may have a shorter shelf life. This topic is usually covered in the middle of a textbook. I also found it very refreshing to see a wide variability of fields and topics represented in the practice problems. 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. In particular, examples and datasets about county characteristics, elections, census data, etc, can become outdated fairly quickly. The examples are up-to-date. The pdf is likely accessible for screen readers, though. Then, the basics of both hypothesis tests and confidence intervals are covered in one chapter. 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. I was concerned that it also might add to the difficulty of analyzing tables. I did not see any inaccuracies in the book. Chapter 7 and 8 cover the linear , multiple and logistic regression. 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. The book uses relevant topics throughout that could be quickly updated. This is sometimes a problem in statistics as there are a variety of ways to express the similar statistical concepts. Though I might define p-values and interpret confidence intervals slightly differently. 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 assume this is for the benefit of those using mobile devices to view the book, but scrolling through on a computer, the sections and the exercises tend to blend together. There are also short videos for 75% of the book sections that are easy to follow and a plus for students. Reviewed by Darin Brezeale, Senior Lecturer, University of Texas at Arlington on 1/21/20, 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 The regression treatment of categorical predictors is limited to dummy coding (though not identified as such) with two levels in keeping with the introductory nature of the text. One of the strengths of this text is the use of motivated examples underlying each major technique. 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. 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. The supplementary material for this book is excellent, particularly if instructors are familiar with R and Latex. The resources, such as labs, lecture notes, and videos are good resources for instructors and students as well. read more. OpenIntro Statistics supports flexibility in choosing and ordering topics. Reviewed by Barbara Kraemer, Part-time faculty, De Paul University School of Public Service on 6/20/17, The texts includes basic topics for an introductory course in descriptive and inferential statistics. Each section is short, concise and contained, enabling the reader to process each topic prior to moving forward to the next topic. This introductory material then serves as the foundation for later chapter where students are introduced to inferential statistical practices. Ensure every student can access the course textbook. There is more than enough material for any introductory statistics course. 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. This may allow the reader to process statistical terminology and procedures prior to learning about regression. 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. The topics are in a reasonable order. Reminder: the 4th Edition is the newest edition. The text covers the foundations of data, distributions, probability, regression principles and inferential principles with a very broad net. The content is well-organized. 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 chapters are bookmarked along the side of the pdf file (once downloaded). Jargon is introduced adequately, though. It is certainly a fitting means of introducing all of these concepts to fledgling research students. Overall, I would consider this a decent text for a one-quarter or one-semester introductory statistics textbook. I have used this book now to teach for 4 semesters and have found no errors. I wish they included measures of association for categorical data analysis that are used in sociology and political science, such as gamma, tau b and tau c, and Somers d. Finally, I think the book needs to add material on the desirable properties of statistical estimators (i.e., unbiasedness, efficiency, consistency). I find the content to be quite relevant. This was not necessarily the case with some of the tables in the text. The text covers the foundations of data, distributions, probability, regression principles and inferential principles with a very broad net. The text, however, is not engaging and can be dry. This could make it easier for students or instructors alike to identify practice on particular concepts, but it may make it more difficult for students to grasp the larger picture from the text alone. The authors used a consistent method of presenting new information and the terminology used throughout the text remained consistent. The authors limit their discussion on categorical data analysis to the chi square statistic, which centers on inference rather than on the substantive magnitude of the bivariate relationship. Some topics seem to be introduced repeatedly, e.g., the Central Limit Theorem (pp. The book presents all the topics in an appropriate sequence. This book is highly modular. The text needs real world data analysis examples from finance, business and economics which are more relevant to real life. Reviewed by Leanne Merrill, Assistant Professor, Western Oregon University on 6/14/21, 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. It includes too much theory for our undergraduate service courses, but not enough practical details for our graduate-level service courses. I did not view an material that I felt would be offensive. 100% 100% found this document not useful, Mark this document as not useful. From what I can tell, the book is accurate in terms of what it covers. The formatting and interface are clear and effective. The text covers all the core topics of statisticsdata, probability and statistical theories and tools. 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 examples will likely become dated, but that is always the case with statistics textbooks; for now, they all seem very current (in one example, we solve for the % of cat videos out of all the videos on Youtube). Given that this is an introductory textbook, it is clearly written and accessible to students with a variety of disciplinary backgrounds. The narrative of the text is grounded in examples which I appreciate. It appears to stick to more non-controversial examples, which is perhaps more effective for the subject matter for many populations. The approach of introducing the inferences of proportions and the Chi-square test in the same chapter is novel. This book is easy to follow and the roadmap at the front for the instructor adds additional ease. It is easy to skip some topics with no lack of consistency or confusion. There are a variety of interesting topics in the exercises that include research on the relationship between honesty, age and self control with children; an experiment on a treatment for asthma patients; smoking habits in the U.K.; a study on migraines and acupuncture; and a study on sinusitis and antibiotics. and get access to extra resources: Request a free desk copy of an OpenIntro textbook for a course (US only). I use this book in teaching and I did not find any issues with accuracy, inconsistency, or biasness. The book is well organized and structured. For a Statistics I course at most community colleges and some four year universities, this text thoroughly covers all necessary topics. The most accurate open-source textbook in statistics I have found. According to the authors, the text is to help students forming a foundation of statistical thinking and methods, unfortunately, some basic Fisher's exact test is not even mentioned. As aforementioned, the authors gently introduce students to very basic statistical concepts. The consistency of this text is quite good. The topics all proceed in an orderly fashion. The book covers familiar topics in statistics and quantitative analysis and the presentation of the material is accurate and effective. The document was very legible. There is no evidence that the text is culturally insensiteve or offensive. The index and table of contents are clear and useful. The text is mostly accurate, especially the sections on probability and statistical distributions, but there are some puzzling gaffes. read more. 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). Getting Started Amazon links on openintro.org or in products are affiliate links. There are many additional resources available for this book including lecture slides, a free online homework system, labs, sample exams, sample syllabuses, and objectives. read more. There do not appear to be grammatical errors. 2019, 422 pages. I was sometimes confused by tables with missing data or, as was the case on page 11, when the table was sideways on the page. There are no proofs that might appeal to the more mathematically inclined. Effectively used in subsequent chapters is perhaps more effective for the subject matter for many populations easy streamlined in this... Paragraphs that make the text is a good position to set up the thought process of students think! Jarring in this aspect, and the presentation of the book hypothesis tests and confidence intervals slightly differently think the! Along the side of the pdf is likely accessible for screen readers though! 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