Most of the examples are general and not culturally related. The authors do a terrific job in chapter 1 introducing key ideas about data collection, sampling, and rudimentary data analysis. The examples flow nicely into the guided practice problems and back to another example, definition, set of procedural steps, or explanation. The organization is fine. The definitions and procedures are clear and presented in a framework that is easy to follow. For one. Most contain glaring conceptual and pedagogical errors, and are painful to read (don't get me started on percentiles or confidence intervals). Fisher's exact test is not even mentioned. read more. and get access to extra resources: Request a free desk copy of an OpenIntro textbook for a course (US only). The topics are in a reasonable order. 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. Overall, I liked the book. read more. The book provides an effective index. Each chapter is broken up into sections and each section has sub-sections using standard LaTex numbering. The text is up to date and the content / data used is able to be modified or updated over time to help with the longevity of the text. The second is that examples and exercises are numbered in a similar manner and students frequently confuse them early in the class. Especially, this book covers Bayesian probabilities, false negative and false positive calculations. For example, types of data, data collection, probability, normal model, confidence intervals and inference for It should be pointed out that logistic regression is using a logistic function to model a binary dependent variable. 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. While the text could be used in both undergraduate and graduate courses, it is best suited for the social sciences. You are on page 1 of 3. Given that this is an introductory textbook, it is clearly written and accessible to students with a variety of disciplinary backgrounds. And, the authors have provided Latex code for slides so that instructors can customize the slides to meet their own needs. The examples for tree diagrams are very good, e.g., small pox in Boston, breast cancer. Many OERs (and published textbooks) are difficult to convert from a typical 15-week semester to a 10-week term, but not this one! This book covers almost all the topics needed for an introductory statistics course from introduction to data to multiple and logistic regression models. Embed. The lack of discussion/examples/inclusion of statistical software or calculator usage is disappointing, as is the inclusion of statistical inference using critical values. This book is very clearly laid out for both students and faculty. Access even-numbered exercise solutions. 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." Chapter 7 and 8 cover the linear , multiple and logistic regression. For example, the inference for categorical data chapter is broken in five main section. The examples are up-to-date, but general enough to be relevant in years to come or formatted appropriately so that, if necessary, they may be easily replaced. The issue I had with this was that I found the definitions within these boxes to often be more clear than when the term was introduced earlier, which often made me go looking for these boxes before I reached them naturally. I did not see any problems in regards to the book's notation or terminology. The resources, such as labs, lecture notes, and videos are good resources for instructors and students as well. The distinction and common ground between standard deviation and standard error needs to be clarified. 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. It would be nice to have an e-book version (though maybe I missed how to access this on the website). 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. One of the good topics is the random sampling methods, such as simple sample, stratified, The text book contains a detailed table of contents, odd answers in the back and an index. 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 examples and exercises seem to be USA-centric (though I did spot one or two UK-based examples), but I do not think that it was being insensitive to any group. Ability to whitelist other teachers so they can immediately get full access to teacher resources on openintro.org. I did not see any inaccuracies in the book. There is an up-to-date errata maintained on the website. 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. The Guided Practice problems allow students to try a problem with the solution in the footnote at the bottom. The text is organized into sections, and the numbering system within each chapter facilitates assigning sections of a chapter. One of the real strengths of the book is the many examples and datasets that it includes. Supposedly intended for "introductory statistics courses at the high school through university levels", it's not clear where this text would fit in at my institution. "Data" is sometimes singular, sometimes plural in the authors' prose. However, I think a greater effort could be made to include more culturally relevant examples in this book. I do not see introductory statistics content ever becoming obsolete. Our inaugural effort is OpenIntro Statistics. Also, I had some issues finding terms in the index. #. If the volunteer sample is covered also that would be great because it is very common nowadays. OpenIntro Statistics covers a first course in statistics, providing a rigorous introduction to appliedstatistics that is clear, concise, and accessible. While it would seem that the data in a statistics textbook would remain relevant forever, there are a few factors that may impact such a textbook's relevance and longevity. The code and datasets are available to reproduce materials from the book. The text is culturally inclusive with examples from diverse industries. 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. Also, grouping confidence intervals and hypothesis testing in Ch.5 is odd, when Ch.7 covers hypothesis testing of numerical data. I have used this book now to teach for 4 semesters and have found no errors. the U.K., they may not be the best examples that could be used to connect with those from non-western countries. Also, non-parametric alternatives would be nice, especially Monte Carlo/bootstrapping methods. The text would surely serve as an excellent supplement that will enhance the curriculum of any basic statistics or research course. 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. Also, for how the authors seem to be focusing on practicalities, I was somewhat surprised about some of the organization of the inference sections. The text covers all the core topics of statisticsdata, probability and statistical theories and tools. This book was written with the undergraduate level in mind, but it's also popular in high schools and graduate courses. However, even with this change, I found the presentation to overall be clear and logical. 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). 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. One of the good topics is the random sampling methods, such as simple sample, stratified, cluster, and multistage random sampling methods. The only issue I had in the layout was that at the end of many sections was a box high-lighting a term. The interface of the book appears to be fine for me, but more attractive colors would make it better. According to the authors, the text is to help students forming a foundation of statistical thinking and methods, unfortunately, some basic topics are missed for reaching the goal. The approach of introducing the inferences of proportions and the Chi-square test in the same chapter is novel. 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 . 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 topics are presented in a logical order with each major topics given a thorough treatment. There are lots of graphs in the book and they are very readable. 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. 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). There are some things that should probably be included in subsequent revisions. The approach is mathematical with some applications. Statistics is not a subject that becomes out of date, but in the last couple decades, more emphasis has been given to usage of computer technology and relevant data. 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. This is a statistics text, and much of the content would be kept in this order. The statistical terms, definitions, and equation notations are consistent throughout the text. though some examples come from other parts of the world (Greece economics, Australian wildlife). This introductory material then serves as the foundation for later chapter where students are introduced to inferential statistical practices. 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. Some topics seem to be introduced repeatedly, e.g., the Central Limit Theorem (pp. Marginal notes for key concepts & formulae? Examples stay away from cultural topics. It covers all the standard topics fully. read more. There are also a number of exercises embedded in the text immediately after key ideas and concepts are presented. There are distracting grammatical errors. Skip Navigation. This keeps all inference for proportions close and concise helping the reader stay uninterrupted in the topic. read more. Jargon is introduced adequately, though. The prose is sometimes tortured and imprecise. I viewed the text as a PDF and was pleasantly surprised at the clarity the fluid navigation that is not the norm with many PDFs. 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. This is a good position to set up the thought process of students to think about how statisticians collect data. Tables and graphs are sensibly annotated and well organized. 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. See examples below: Observational study: Observational study is the one where researchers observe the effect of. The book includes examples from a variety of fields (psychology, biology, medicine, and economics to name a few). OpenIntro Statistics. I did not see any grammatical issues that distract form the content presented. This easily allow for small sets of reading on a class to class basis or larger sets of reading over a weekend. I suspect these will prove quite helpful to students. This textbook is nicely parsed. The chapters are bookmarked along the side of the pdf file (once downloaded). 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. For 24 students, the average score is 74 points with a standard deviation of 8.9 points. This can be particularly confusing to "beginners.". All of the calculations covered in this book were performed by hand using the formulas. The graphs are readable in black and white also. For example, I can imagine using pieces of Chapters 2 (Probability) and 3 (Distributions of random variables) to motivate methods that I discuss in service courses. The drawbacks of the textbook are: 1) it doesn't offer how to use of any computer software or graphing calculator to perform the calculations and analyses; 2) it didn't offer any real world data analysis examples. The presentation is professional with plenty of good homework sets and relevant data sets and examples. This defect is not present here: this text embraces an 'embodied' view of learning which prioritizes example applications first and then explanation of technique. The odd-numbered exercises also have answers in the book. The structure and organization of this text corresponds to a very classic treatment of the topic. edition by chopra openintro statistics 4th edition textbook solutions bartleby early transcendentals rogawski 4th edition solution manual pdf solutions to introduction to electrodynamics 4e by d j. griffiths traffic and highway engineering I think that these features make the book well-suited to self-study. It does a more thorough job than most books of covering ideas about data, study design, summarizing data and displaying data. 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. Parts I and II give an overview of the most common tests (t-test, ANOVA, correlations) and work out their statistical principles. The authors bold important terms, and frequently put boxes around important formulas or definitions. 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