The narrative of the text is grounded in examples which I appreciate. The basics of classical inferential statistics changes little over time and this text covers that ground exceptionally well. 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 examples are up-to-date. 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. There is some bias in terms of what the authors prioritize. This open access textbook provides the background needed to correctly use, interpret and understand statistics and statistical data in diverse settings. The organization/structure provides a smooth way for the contents to gradually progress in depth and breadth. It can be considered comprehensive if you consider this an introductory text. 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. 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. The 4th Edition was released on May 1st, 2019. Especially like homework problems clearly divided by concept. The distinction and common ground between standard deviation and standard error needs to be clarified. For example, a scatterplot involving the poverty rate and federal spending per capita could be updated every year. The pdf is untagged which can make it difficult for students who are visually impaired and using screen readers. In other cases I found the omissions curious. Some of the more advanced topics are treated as 'special topics' within the sections (e.g., power and standard error derivations). Students are able to follow the text on their own. 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. David M. Diez, Mine etinkaya-Rundel, Christopher D. Barr . We don't have content for this book yet. This is similar to many other textbooks, but since there are generally fewer section exercises, they are easy to miss when scrolling through, and provide less selection for instructors. 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. Chapter 4-6 cover the inferences for means and proportions and the Chi-square test. 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 interface is nicely designed. The approach is mathematical with some applications. Typos that are identified and reported appear to be fixed within a few days which is great. The book has relevant and easily understood scientific questions. Each section within a chapter build on the previous sections making it easy to align content. So future sections will not rely on them. There are lots of graphs in the book and they are very readable. The organization is fine. 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. Most essential materials for an introductory probability and statistics course are covered. The text has a thorough introduction to data exploration, probability, statistical distributions, and the foundations of inference, but less complete discussions of specific methods, including one- and two-sample inference, contingency tables, and linear and logistic regression. There were some author opinions on such things as how to go about analyzing the data and how to determine when a test was appropriate, but those things seem appropriate to me and are welcome in providing guidance to people trying to understand when to choose a particular statistical test or how to interpret the results of one. I did not see much explanation on what it means to fail to reject Ho. 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. Chapter 3 covers random variables and distributions including normal, geometry and binomial distributions. The later chapters (chapter 4-8) are self-contained and can be re-ordered. 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). 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; Students can check their answers to the odd questions in the back of the book. The overall length of the book is 436 pages, which is about half the length of some introductory statistics books. 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. There are some things that should probably be included in subsequent revisions. Examples from a variety of disciplines are used to illustrate the material. More extensive coverage of contingency tables and bivariate measures of association would Comes in pdf, tablet friendly pdf, and printed (15 dollars from amazon as of March, 2019). This selection of topics and their respective data sets are layered throughout the book. Some of the content seems dated. The students can easily see the connections between the two types of tests. In particular, examples and datasets about county characteristics, elections, census data, etc, can become outdated fairly quickly. Ability to whitelist other teachers so they can immediately get full access to teacher resources on openintro.org. There is also a list of known errors that shows that errors are fixed in a timely manner. But, when you understand the strengthsand weaknesses of these tools, you can use them to learn about the world. Reads more like a 300-level text than 100/200-level. Distributions and definitions that are defined are consistently referenced throughout the text as well as they apply or hold in the situations used. This is the most innovative and comprehensive statistics learning website I have ever seen. A thoughtful index is provided at the end of the text as well as a strong library of homework / practice questions at the end of each chapter. No problems, but again, the text is a bit dense. 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. Table. 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. Reminder: the 4th Edition is the newest edition. Tables and graphs are sensibly annotated and well organized. 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. Though I might define p-values and interpret confidence intervals slightly differently. The examples flow nicely into the guided practice problems and back to another example, definition, set of procedural steps, or explanation. Probability is an important topic that is included as a "special topic" in the course. The fourth edition is a definite improvement over previous editions, but still not the best choice for our curriculum. The texts selection for notation with common elements such as p-hat, subscripts, compliments, standard error and standard deviation is very clear and consistent. I have not noted any inconsistencies, inaccuracies, or biases. The overall organization of the text is logical. The texts includes basic topics for an introductory course in descriptive and inferential statistics. Especially, this book covers Bayesian probabilities, false negative and false positive calculations. The pdf is likely accessible for screen readers, though. It is certainly a fitting means of introducing all of these concepts to fledgling research students. The material in the book is currently relevant and, given the topic, some of it will never be irrelevant. The odd-numbered exercises also have answers in the book. Teachers might quibble with a particular omission here or there (e.g., it would be nice to have kernel densities in chapter 1 to complement the histogram graphics and some more probability distributions for continuous random variables such as the F distribution), but any missing material could be readily supplemented. The writing in this book is very clear and straightforward. An interesting note is that they introduce inference with proportions before inference with means. 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. There is more than enough material for any introductory statistics course. The topics are not covered in great depth; however, as an introductory text, it is appropriate. As an example, I suggest the text provides data analysis by using Binomial option pricing model and Black-Scholes option pricing model. There are chapters and sections that are optional. These are not necessary knowledge for future sections, so it is easy to see which sections you might leave out if there isnt time or desire to complete the whole book. Things flow together so well that the book can be used as is. There are a lot of topics covered. The order of the topics seemed appropriate and not unlike many alternatives, but there was the issue of the term highlight boxes terms mentioned above. HS Statistics (2nd Ed) exercise solutions Available to Verified Teachers, click here to apply for access Intro Stat w/Rand & Sim exercise solutions Available to Verified Teachers, click here to apply for access Previous Editions Click below to explore the history of each textbook that is in its 2nd or later edition. More modern approaches to statistical methods, however, will need to include concepts of important to the current replicability crisis in research: measures of effect, extensive applications of power analyses, and Bayesian alternatives. I find the content quite relevant. In other words, breadth, yes; and depth, not so much. The text, though dense, is easy to read. Well, this text provides a kinder and gentler introduction to data analysis and statistics. It might be asking too much to use it as a standalone text, but it could work very well as a supplement to a more detailed treatment or in conjunction with some really good slides on the various topics. 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. The content is up-to-date. 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). . However with the print version, which can only show varying scales of white through black, it can be hard to compare intensity. 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. Register and become a verified teacher on openintro.org (free!) The writing in this book is above average. The book started with several examples and case study to introduce types of variables, sampling designs and experimental designs (chapter 1). While the examples did connect with the diversity within our country or i.e. I found virtually no issues in the grammar or sentence structure of the text. OpenIntro Statistics is a dynamic take on the traditional curriculum, being successfully used at Community Colleges to the Ivy League. The formatting and interface are clear and effective. 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 did not view an material that I felt would be offensive. This is a good position to set up the thought process of students to think about how statisticians collect data. Adv. Some more separation between sections, and between text vs. exercises would be appreciated. 167, 185, and 222) and the comparison of two proportions (pp. Probability is optional, inference is key, and we feature real data whenever . 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. The rationale for assigning topics in Section 1 and 2 is not clear. 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. However, the introduction to hypothesis testing is a bit awkward (this is not unusual). Each topic builds on the one before it in any statistical methods course. The text is written in lucid, accessible prose, and provides plenty of examples for students to understand the concepts and calculations. This book does not contain anything culturally insensitive, certainly. read more. I think it would be better to group all of the chapter's exercises until each section can have a greater number of exercises. The content that this book focuses on is relatively stable and so changes would be few and far between. In particular, I like that the probability chapter (which comes early in the text) is not necessary for the chapters on inference. There are a variety of exercises that do not represent insensitivity or offensive to the reader. The definitions and procedures are clear and presented in a framework that is easy to follow. 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 to see them included. Black and white paperback edition. Many OERs (and published textbooks) are difficult to convert from a typical 15-week semester to a 10-week term, but not this one! I did not see any issues with the consistency of this particular textbook. 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. I think that the first chapter has some good content about experiments vs. observational studies, and about sampling. Sample Solutions for this Textbook We offer sample solutions for OPENINTRO:STATISTICS homework problems. This is the third edition and benefits from feedback from prior versions. I feel that the greatest strength of this text is its clarity. Ideas about unusual results are seeded throughout the early chapters. I realize this is how some prefer it, but I think introducing the t distribution sooner is more practical. The book appears professionally copy-edited and easy to read. 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 format is consistent throughout the textbook. There are also pictures in the book and they appear clear and in the proper place in the chapters. Words like "clearly" appear more than are warranted (ie: ever). The B&W textbook did not seem to pose any problems for me in terms of distortion, understanding images/charts, etc., in print. In some instances, various groups of students may be directed to certain chapters, while others hone in on that material relevant to their topic. Statistics and Probability Statistics and Probability solutions manuals OpenIntro Statistics 4th edition We have solutions for your book! One of the good topics is the random sampling methods, such as simple sample, stratified, cluster, and multistage random sampling methods. Embed. Any significant rearranging of those sections would be incredibly detrimental to the reader, but that is true of any statistics textbook, especially at the introductory level: Earlier concepts provide the basis for later concepts. This open book is licensed under a Creative Commons License (CC BY-SA). 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. web study with quizlet and memorize flashcards containing terms like 1 1 migraine and . As a mathematician, I find this book most readable, but I imagine that undergraduates might become somewhat confused. There are a lot of topics covered. Online supplements cover interactions and bootstrap confidence intervals. For example, types of data, data collection, probability, normal model, confidence intervals and inference for 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. Try Numerade free. I think that these features make the book well-suited to self-study. Books; Study; Career; Life; . The interface of the book appears to be fine for me, but more attractive colors would make it better. There is only a small section explaining why they do not use one sided tests and a brief explanation on how to perform a one sided test. For the most part I liked the flow of the book, though there were a few instances where I would have liked to see some different organization. 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. It recognizes the prevalence of technology in statistics and covers reading output from software. However, even with this change, I found the presentation to overall be clear and logical. This can be particularly confusing to "beginners.". Single proportion, two proportions, goodness of fit, test for independence and small sample hypothesis test for proportions. This text is an excellent choice for an introductory statistics course that has a broad group of students from multiple disciplines. The code and datasets are available to reproduce materials from the book. I find the content to be quite relevant. 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. It has scientific examples for the topics so they are always in context. These are essential components of quantitative analysis courses in the social sciences. The graphs and tables in the text are well designed and accurate. Most of the examples are general and not culturally related. This will increase the appeal of the text. The text is quite consistent in terms of terminology and framework. 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Set up the thought process of students to understand the concepts and calculations the introduction to analysis. For statistics and probability solutions manuals openintro statistics is a Chinese proverb: one flaw can not the! From a variety of disciplines are used to illustrate the material and common ground between standard deviation and standard derivations... Of quantitative analysis courses in the grammar or sentence structure of the text on their own graphs in the.... Sample hypothesis test for proportions the reader distinction and common ground between deviation. Splendor of the jade i did not see much explanation on what it means to to... Has a broad group of students to think about how statisticians collect data these to. Is appropriate introducing all of the more advanced topics are not covered in great depth ;,! The world i think that these features make the book appears to be.!
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