For example, types of data, data collection, probability, normal model, confidence intervals and inference for The definitions are clear and easy to follow. The p-value definition could be simplified by eliminating mention of a hypothesis being tested. The t distribution is introduced much later. (Unlike many modern books that seem to have random sentences scattered in between bullet points and boxes.). It's very fitting for my use with teachers whose primary focus is on data analysis rather than post-graduate research. I often assign reading and homework before I discuss topics in lecture. The pros are that it's small enough that a person can work their way through it much faster than would be possible with many of the alternatives. 100% 100% found this document not useful, Mark this document as not useful. The text would surely serve as an excellent supplement that will enhance the curriculum of any basic statistics or research course. There are a few instances referencing specific technology (such as iPods) that makes the text feel a bit dated. The text is mostly accurate but I feel the description of logistic regression is kind of foggy. OpenIntro Statistics offers a traditional introduction to statistics at the college level. The resources on the website also are well organized and easy to access and download. read more. However, the linear combination of random variables is too much math focused and may not be good for students at the introductory level. 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 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. However, even with this change, I found the presentation to overall be clear and logical. There are no issues with the grammar in the book. 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. Lots of good graphics and referenced data sets, but not much discussion or inclusion of prevailing software such as R, SPSS, Minitab, or free online packages. This is important since examples used authentic situations to connect to the readers. Each chapter contains short sections and each section contains small subsections. 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). There is also a list of known errors that shows that errors are fixed in a timely manner. I do not see introductory statistics content ever becoming obsolete. OpenIntro Statistics - 4th Edition - Solutions and Answers | Quizlet Math Probability OpenIntro Statistics 4th Edition ISBN: 9781943450077 Christopher Barr, David Diez, Mine etinkaya-Rundel Sorry! Reviewed by Denise Wilkinson, Professor of Mathematics, Virginia Wesleyan University on 4/20/21, This text book covers most topics that fit well with an introduction statistics course and in a manageable format. OpenIntro Statistics textbook solutions from Chegg, view all supported editions. Also, a reminder for reviewers to save their work as they complete this review would be helpful. Single proportion, two proportions, goodness of fit, test for independence and small sample hypothesis test for proportions. The book is well organized and structured. The colors of the font and tables in the textbook are mostly black and white. However, the introduction to hypothesis testing is a bit awkward (this is not unusual). As a mathematician, I find this book most readable, but I imagine that undergraduates might become somewhat confused. Also, non-parametric alternatives would be nice, especially Monte Carlo/bootstrapping methods. Access even-numbered exercise solutions. Each chapter consists of 5-10 sections. The graphs and diagrams were also clear and provided information in a way that aided in understanding concepts. #. Try Numerade free. read more. Ability to whitelist other teachers so they can immediately get full access to teacher resources on openintro.org. The organization/structure provides a smooth way for the contents to gradually progress in depth and breadth. The supplementary material for this book is excellent, particularly if instructors are familiar with R and Latex. Online supplements cover interactions and bootstrap confidence intervals. Then, the basics of both hypothesis tests and confidence intervals are covered in one chapter. 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. 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. Intro Statistics with Randomization and Simulation Bringing a fresh approach to intro statistics, ISRS introduces inference faster using randomization and simulation techniques. There are a few color splashes of blue and red in diagrams or URL's. I found the overall structure to be standard of an introductory statistics course, with the exception of introducing inference with proportions first (as opposed to introducing this with means first instead). Examples stay away from cultural topics. Perhaps an even stronger structure would see all the types of content mentioned above applied to each type of data collection. Quite clear. The text is written in lucid, accessible prose, and provides plenty of examples for students to understand the concepts and calculations. However with the print version, which can only show varying scales of white through black, it can be hard to compare intensity. As the trend of analysis, students will be confronted with the needs to use computer software or a graphing calculator to perform the analyses. David M. Diez, Mine etinkaya-Rundel, Christopher D. Barr . Overall I like it a lot. The order of introducing independence and conditional probability should be switched. Nothing was jarring in this aspect, and the sections/chapters were consistent. Especially like homework problems clearly divided by concept. read more. The book reads cleanly throughout. Typos and errors were minimal (I could find none). In particular, examples and datasets about county characteristics, elections, census data, etc, can become outdated fairly quickly. Especially, this book covers Bayesian probabilities, false negative and false positive calculations. 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. 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. In particular, the malaria case study and stokes case study add depth and real-world Similar to most intro Some topics seem to be introduced repeatedly, e.g., the Central Limit Theorem (pp. Chapter 3 covers random variables and distributions including normal, geometry and binomial distributions. The authors are sloppy in their use of hat notation when discussing regression models, expressing the fitted value as a function of the parameters, instead of the estimated parameters (pp. The text also provides enough context for students to understand the terminologies and definitions, especially this textbook provides plenty of tips for each concept and that is very helpful for students to understand the materials. The topics are presented in a logical order with each major topics given a thorough treatment. 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. The text includes sections that could easily be extracted as modules. The topics are in a reasonable order. On occasion, all of us in academia have experienced a text where the progression from one chapter to another was not very seamless. 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. Save Save Solutions to Openintro Statistics For Later. These are essential components of quantitative analysis courses in the social sciences. The task of reworking statistical training in response to this crisis will be daunting for any text author not just this one. The introduction of jargon is easy streamlined in after this example introduction. More extensive coverage of contingency tables and bivariate measures of association would Teachers looking for a text that they can use to introduce students to probability and basic statistics should find this text helpful. The second is that examples and exercises are numbered in a similar manner and students frequently confuse them early in the class. 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. The chapter summaries are easy to follow and the order of the chapters begin with "Introduction to Data," which includes treatment There are separate chapters on bi-variate and multiple regression and they work well together. There are a variety of exercises that do not represent insensitivity or offensive to the reader. Navigation as a PDF document is simple since all chapters and subsection within the table of contents are hyperlinked to the respective section. The text is accurate due to its rather straight forward approach to presenting material. The subsequent chapters have all of the specifics about carrying out hypothesis tests and calculating intervals for different types of data. Overall, this is the best open-source statistics text I have reviewed. None. 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. 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). 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. They have done an excellent job choosing ones that are likely to be of interest to and understandable by students with diverse backgrounds. The text is organized into sections, and the numbering system within each chapter facilitates assigning sections of a chapter. The book includes examples from a variety of fields (psychology, biology, medicine, and economics to name a few). As aforementioned, the authors gently introduce students to very basic statistical concepts. Professors looking for in-depth coverage of research methods and data collection techniques will have to look elsewhere. The content of the book is accurate and unbiased. The text is culturally inclusive with examples from diverse industries. 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 Typos that are identified and reported appear to be fixed within a few days which is great. Overall, I recommend this book for an introductory statistics course, however, it has some advanced topics. I see essentially no errors in this book. The text is in PDF format; there are no problems of navigation. This book is very clearly laid out for both students and faculty. I do not think that the exercises focus in on any discipline, nor do they exclude any discipline. However, there are some sections that are quite dense and difficult to follow. There are a lot of topics covered. Of course, the content in Chapters 5-8 would surely be useful as supplementary materials/refreshers for students who have mastered the basics in previous statistical coursework. The authors bold important terms, and frequently put boxes around important formulas or definitions. I realize this is how some prefer it, but I think introducing the t distribution sooner is more practical. The pdf and tablet pdf have links to videos and slides. samsung neo g8 firmware update; acoustic guitar with offset soundhole; adapt email finder chrome extension; doordash q1 2022 earnings The wording "at least as favorable to the alternative hypothesis as our current data" is misleading. The text is easily and readily divisible into subsections. 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. Find step-by-step expert solutions for your textbook or homework problem Students are able to follow the text on their own. I have used this book now to teach for 4 semesters and have found no errors. 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 examples for tree diagrams are very good, e.g., small pox in Boston, breast cancer. 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. In other cases I found the omissions curious. and get access to extra resources: Request a free desk copy of an OpenIntro textbook for a course (US only). Also, I had some issues finding terms in the index. 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. The later chapters (chapters 4-8) are built upon the knowledge from the former chapters (chapters 1-3). I found the book's prose to be very straightforward and clear overall. The topics all proceed in an orderly fashion. read more. Introducing independence using the definition of conditional probability P(A|B)=P(A) is more accurate and easier for students to understand. 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 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 topics are not covered in great depth; however, as an introductory text, it is appropriate. Skip Navigation. 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. 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 . It is difficult for a topic that in inherently cumulative to excel at modularity in the manner that is usually understanding. The examples are general and do not deal with racial or cultural matters. Overall, I would consider this a decent text for a one-quarter or one-semester introductory statistics textbook. The book covers the essential topics in an introductory statistics course, including hypothesis testing, difference of means-tests, bi-variate regression, and multivariate regression. Overall the organization is good, so I'm still rating it high, but individual instructors may disagree with some of the order of presentation. The text meets students at a nice place medium where they are challenged with thoughtful, real situations to consider and how and why statistical methods might be useful. The probability section uses a data set on smallpox to discuss inoculation, another relevant topic whose topic set could be easily updated. The overall organization of the text is logical. I found virtually no issues in the grammar or sentence structure of the text. Part I makes key concepts in statistics readily clear. The approach is mathematical with some applications. The text is well-written and with interesting examples, many of which used real data. 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. 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. read more. Chapter 2 covers the knowledge of probabilities including the definition of probability, Law of Large Numbers, probability rules, conditional probability and independence and linear combinations of random variables. Some more modern concepts, such as various effect size measures, are not covered well or at all (for example, eta squared in ANOVA). Everything appeared to be accurate. In the PDF of the book, these references are links that take you to the appropriate section. I would tend to group this in with sampling distributions. The student-facind end, while not flashy or gamified in any way, is easy to navigate and clear. At first when reviewing, I found it to be difficult for to quickly locate definitions and examples and often focus on the material. I find this method serves to give the students confidence in knowing that they understand concepts before moving on to new material. 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 later chapters on inferences and regression (chapters 4-8) are built upon the former chapters (chapters 1-3). Each section within a chapter build on the previous sections making it easy to align content. In addition to the above item-specific comments: #. 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; Our inaugural effort is OpenIntro Statistics. Select the Edition for OpenIntro Statistics Below: . At The book presents all the topics in an appropriate sequence. Chapter 4-6 cover the inferences for means and proportions and the Chi-square test. There are a lot of topics covered. It is certainly a fitting means of introducing all of these concepts to fledgling research students. 3rd Edition files and information (2015, 436 pages) 2nd Edition files and information (2012, 426 pages) 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). The textbook offers companion data sets on their website, and labs based on the free software, R and Rstudio. It strikes me as jumping around a bit. They draw examples from sources (e.g., The Daily Show, The Colbert Report) and daily living (e.g., Mario Kart video games) that college students will surely appreciate. The flow of a chapter is especially good when the authors continue to use a certain example in developing related concepts. I did not see any grammatical issues that distract form the content presented. It can be considered comprehensive if you consider this an introductory text. It is accurate. I have not noted any inconsistencies, inaccuracies, or biases. No grammatical errors have been found as of yet. The approach is mathematical with some applications. 167, 185, and 222) and the comparison of two proportions (pp. I do like the case studies, videos, and slides. There is some bias in terms of what the authors prioritize. I have no idea how to characterize the cultural relevance of a statistics textbook. It is certainly a fitting means of introducing all of these concepts to fledgling research students. This text is an excellent choice for an introductory statistics course that has a broad group of students from multiple disciplines. This book is highly modular. For example, types of data, data collection, probability, normal model, confidence intervals and inference for single proportions. It appears to stick to more non-controversial examples, which is perhaps more effective for the subject matter for many populations. This introductory material then serves as the foundation for later chapter where students are introduced to inferential statistical practices. 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. This easily allow for small sets of reading on a class to class basis or larger sets of reading over a weekend. Technical accuracy is a strength for this text especially with respect to underlying theory and impacts of assumptions. Each topic builds on the one before it in any statistical methods course. It is easy to skip some topics with no lack of consistency or confusion. The content stays unbiased by constantly reminding the reader to consider data, context and what ones conclusions might mean rather than being partial to an outcome or conclusions based on ones personal beliefs in that the conclusions sense that statistics texts give special. The writing could be slightly more inviting, and concept could be more readily introduced via accessible examples more often. 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 Download now. There are also matching videos for students who need a little more help to figure something out. Other examples: "Each of the conclusions are based on some data" (p. 9); "You might already be familiar with many aspects of probability, however, formalization of the concepts is new for most" (p. 68); and "Sometimes two variables is one too many" (p. 21). Mine Cetinkaya-Rundel is the Director of Undergraduate Studies and Assistant Professor of the Practice in the Department of Statistical Science at Duke University. 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. Embed. For example, income variations in two cities, ethnic distribution across the country, or synthesis of data from Africa. Each chapter begins with a summary and a URL link to resources like videos, slides, etc. The book started with several examples and case study to introduce types of variables, sampling designs and experimental designs (chapter 1). 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. According to the authors, the text is to help students forming a foundation of statistical thinking and methods, unfortunately, some basic OpenIntro Statistics supports flexibility in choosing and ordering topics. 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. 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 texts selection for notation with common elements such as p-hat, subscripts, compliments, standard error and standard deviation is very clear and consistent. Some topics in descriptive statistics are presented without much explanation, such as dotplots and boxplots. However, it would not suffice for our two-quarter statistics sequence that includes nonparametrics. The authors do a terrific job in chapter 1 introducing key ideas about data collection, sampling, and rudimentary data analysis. 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. The text would not be found to be culturally insensitive in any way, as a large part of the investigations and questions are introspective of cultures and opinions. OpenIntro Statistics is a dynamic take on the traditional curriculum, being successfully used at Community Colleges to the Ivy League all videos slides labs other OpenIntro Statistics is recommended for college courses and self-study. read more. The reader can jump to each chapter, exercise solutions, data sets within the text, and distribution tables very easily. While the text could be used in both undergraduate and graduate courses, it is best suited for the social sciences. 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. This book can work in a number of ways. 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. However, when introducing the basic concepts of null and alternative hypotheses and the p-value, the book used different definitions than other textbooks. The text provides enough examples, exercises and tips for the readers to understand the materials. In fact, I could not differentiate a change in style or clarity in any sections of this text. Additionally concepts related to flawed practices in data collection and analysis were presented to point out how inaccuracies could arise in research. 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. I reviewed a paperback B&W copy of the 4th edition of this book (published 2019), which came with a list describing the major changes/reorganization that was done between this and the 3rd edition. The organization for each chapter is also consistent. Print. 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. And why dump Ch.6 in between with hypothesis testing of categorical data between them? I think that these features make the book well-suited to self-study. There are no proofs that might appeal to the more mathematically inclined. Ideas about unusual results are seeded throughout the early chapters. Reviewed by Casey Jelsema, Assistant Professor, West Virginia University on 12/5/16, 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. Search inside document . So future sections will not rely on them. 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. 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. Overall, the text is well-written and explained along with real-world data examples. There is more than enough material for any introductory statistics course. The simple mention of the subject "statistics" can strike fear in the minds of many students. The chapters are well organized and many real data sets are analyzed. Most contain glaring conceptual and pedagogical errors, and are painful to read (don't get me started on percentiles or confidence intervals). 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." It covers all the standard topics fully. Some of the more advanced topics are treated as 'special topics' within the sections (e.g., power and standard error derivations). Reviewed by Kendall Rosales, Instructor and Service Level Coordinator, Western Oregon University on 8/20/20, There is more than enough material for any introductory statistics course. 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). Many examples use real data sets that are on the larger side for intro stats (hundreds or thousands of observations). openintro statistics fourth edition open textbook library . This keeps all inference for proportions close and concise helping the reader stay uninterrupted in the topic. 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. 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 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. Display of graphs and figures is good, as is the use of color. 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). This is sometimes a problem in statistics as there are a variety of ways to express the similar statistical concepts. I was able to read the entire book in about a month by knocking out a couple of subsections per day. The text is quite consistent in terms of terminology and framework. NOW YOU CAN DOWNLOAD ANY SOLUTION MANUAL YOU WANT FOR FREE > > just visit: www.solutionmanual.net > > and click on the required section for solution manuals > > if the solution ma This text does indicate that some topics can be omitted by identifying them as 'special topics'. Reviewed by Gregg Stall, Associate Professor, Nicholls State University on 2/8/17, The text covers the foundations of data, distributions, probability, regression principles and inferential principles with a very broad net. One of the good topics is the random sampling methods, such as simple sample, stratified, cluster, and multistage random sampling methods. The text covers all the core topics of statisticsdata, probability and statistical theories and tools. 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. Examples from a variety of disciplines are used to illustrate the material. More color, diagrams, photos? 325 and 357). Perhaps we don't help the situation much with the way we begin launching statistical terminology while demonstrating a few "concepts" on a white board. It begins with the basics of descriptive statistics, probability, hypothesis test concepts, tests of numerical variables, categorical, and ends with regression. 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). 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. OpenIntro Statistics is a dynamic take on the traditional curriculum, being successfully used at Community Colleges to the Ivy League. 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. I find the content quite relevant. Appendix A contains solutions to the end of chapter exercises. Ensure every student can access the course textbook. Graphs and tables are clean and clearly referenced, although they are not hyperlinked in the sections. I read the physical book, which is easy to navigate through the many references. I find the content to be quite relevant. I do not detect a bias in the work. Register and become a verified teacher on openintro.org (free!) The later chapters (chapter 4-8) are self-contained and can be re-ordered. The organization of the topics is unique, but logical. read more. The text book contains a detailed table of contents, odd answers in the back and an index. the U.K., they may not be the best examples that could be used to connect with those from non-western countries. 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 a lot of topics covered. The first chapter addresses treatments, control groups, data tables and experiments. It would be nice if the authors can start with the big picture of how people perform statistical analysis for a data set. These graphs and tables help the readers to understand the materials well, especially most of the graphs are colored figures. . No problems, but again, the text is a bit dense. 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. Things flow together so well that the book can be used as is. It defines terms, explains without jargon, and doesnt skip over details. This defect is not present here: this text embraces an 'embodied' view of learning which prioritizes example applications first and then explanation of technique. OpenIntro Statistics covers a first course in statistics, providing a rigorous introduction to applied statistics that is clear, concise, and accessible. 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. 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. This will increase the appeal of the text. 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. The consistency of this text is quite good. Fisher's exact test is not even mentioned. The code and datasets are available to reproduce materials from the book. The text is free of significant interface issues. "Data" is sometimes singular, sometimes plural in the authors' prose. The text, though dense, is easy to read. Reviewed by Paul Murtaugh, Associate Professor, Oregon State University on 7/15/14, 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 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. This may allow the reader to process statistical terminology and procedures prior to learning about regression. 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. More extensive coverage of contingency tables and bivariate measures of association would be helpful. This book was written with the undergraduate level in mind, but it's also popular in high schools and graduate courses. 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. 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). 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). It has scientific examples for the topics so they are always in context. 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. Better than most of the introductory book that I have used thus far (granted, my books were more geared towards engineers). The format is consistent throughout the textbook. These blend well with the Exercises that contain the odd solutions at the end of the text. Also, the convenient sample is covered. Words like "clearly" appear more than are warranted (ie: ever). I was concerned that it also might add to the difficulty of analyzing tables. While section are concise they are not limited in rigor or depth (as exemplified by a great section on the "power" of a hypothesis test) and numerous case studies to introduce topics. For examples, the distinction between descriptive statistics and inferential statistics, the measures of central tendency and dispersion. The text covers the foundations of data, distributions, probability, regression principles and inferential principles with a very broad net. These examples and techniques are very carefully described with quality graphical and visual aids to support learning. The book is broken into small sections for each topic. 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. Share. There are also short videos for 75% of the book sections that are easy to follow and a plus for students. The book provides an effective index. 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. And, the authors have provided Latex code for slides so that instructors can customize the slides to meet their own needs. In addition, some topics are marked as special topics. 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 One of the real strengths of the book is the many examples and datasets that it includes. One of the strengths of this text is the use of motivated examples underlying each major technique. The basics of classical inferential statistics changes little over time and this text covers that ground exceptionally well. Reviewed by Monte Cheney, Associate Professor of Mathematics, Central Oregon Community College on 8/21/16, More depth in graphs: histograms especially. I did not see much explanation on what it means to fail to reject Ho. Students can easily get confused and think the p-value is in favor of the alternative hypothesis. There are many additional resources available for this book including lecture slides, a free online homework system, labs, sample exams, sample syllabuses, and objectives. 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. These sections generally are all under ten page in total. The authors point out that Chapter 2, which deals with probabilities, is optional and not a prerequisite for grasping the content covered in the later chapters. The writing in this book is above average. read more. Notation, language, and approach are maintained throughout the chapters. "Standard error" is defined as the "standard deviation associated with an estimate" (p. 163), but it is often unclear whether population or sample-based quantities are being referred to. For example, it is claimed that the Poisson distribution is suitable only for rare events (p. 148); the unequal-variances form of the standard error of the difference between means is used in conjunction with the t-distribution, with no mention of the need for the Satterthwaite adjustment of the degrees of freedom (p. 231); and the degrees of freedom in the chi-square goodness-of-fit test are not adjusted for the number of estimated parameters (p. 282). The pdf is untagged which can make it difficult for students who are visually impaired and using screen readers. Each section ends with a problem set. Updates and supplements for new topics have been appearing regularly since I first saw the book (in 2013). 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. I do think a more easily navigable e-book would be ideal. The sections seem easily labeled and would make it easy to skip particular sections, etc. Each chapter is separated into sections and subsections. This open book is licensed under a Creative Commons License (CC BY-SA). 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. The texts includes basic topics for an introductory course in descriptive and inferential statistics. 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. Probability is an important topic that is included as a "special topic" in the course. However, I think a greater effort could be made to include more culturally relevant examples in this book. Books; Study; Career; Life; . I would consider this "omission" as almost inaccurate. I found no problems with the book itself. The authors use the Z distribution to work through much of the 1-sample inference. 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. The odd-numbered exercises also have answers in the book. The final chapter (8) gives superficial treatments of two huge topics, multiple linear regression and logistic regression, with insufficient detail to guide serious users of these methods. Sample Solutions for this Textbook We offer sample solutions for OPENINTRO:STATISTICS homework problems. Well, this text provides a kinder and gentler introduction to data analysis and statistics. I use this book in teaching and I did not find any issues with accuracy, inconsistency, or biasness. The writing is clear, and numerous graphs and examples make concepts accessible to students. read more. It is a pdf download rather than strictly online so the format is more classical textbook as would be experienced in a print version. Though I might define p-values and interpret confidence intervals slightly differently. The overall length of the book is 436 pages, which is about half the length of some introductory statistics books. For one. The real data sets examples cover different topics, such as politics, medicine, etc. by David Diez, Mine Cetinkaya-Rundel, Christopher Barr. Tables and graphs are sensibly annotated and well organized. Therefore, while the topics are largely the same the depth is lighter in this text than it is in some alternative introductory texts. The interface is nicely designed. 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. The statistical terms, definitions, and equation notations are consistent throughout the text. I found no negative issues with regard to interface elements. The book covers familiar topics in statistics and quantitative analysis and the presentation of the material is accurate and effective. 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. The most accurate open-source textbook in statistics I have found. If the volunteer sample is covered also that would be great because it is very common nowadays. 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. Create a clear way to explain this multi-faceted topic and the world will beat a path to your door. Distributions and definitions that are defined are consistently referenced throughout the text as well as they apply or hold in the situations used. 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). The final chapters, "Introduction to regression analysis" and "Multiple and logistical regression" fit nicely at the end of the text book. There are also pictures in the book and they appear clear and in the proper place in the chapters. Overall, I liked the book. The 4th Edition was released on May 1st, 2019. The book is very consistent from what I can see. In addition, it is easy to follow. I think that the book is fairly easy to read. Labs are available in many modern software: R, Stata, SAS, and others. These concepts should be clarified at the first chapter. 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. I suspect these will prove quite helpful to students. 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. It is certainly a fitting means of introducing all of these concepts to fledgling research students. The texts includes basic topics for an introductory course in descriptive and inferential statistics. If you are looking for deep mathematical comprehensiveness of exercises, this may not be the right book, but for most introductory statistics students who are not pursuing deeper options in math/stat, this is very comprehensive. 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. For 24 students, the average score is 74 points with a standard deviation of 8.9 points. All of the calculations covered in this book were performed by hand using the formulas. There are chapters and sections that are optional. No issues with consistency in that text are found. 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. 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. OpenIntro Statistics covers a first course in statistics, providing a rigorous introduction to appliedstatistics that is clear, concise, and accessible. 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 regression. There are exercises at the end of each chapter (and exercise solutions at the end of the text). Given that this is an introductory textbook, it is clearly written and accessible to students with a variety of disciplinary backgrounds. This topic is usually covered in the middle of a textbook. A teacher can sample the germane chapters and incorporate them without difficulty in any research methods class. The only issue I had in the layout was that at the end of many sections was a box high-lighting a term. Chapter 7 and 8 cover the linear , multiple and logistic regression. The coverage of probability and statistics is, for the most part, sound. a first course in probability 9th edition solutions; umn resident health insurance; cartoon network invaded tv tropes. 191 and 268). 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. This book is easy to follow and the roadmap at the front for the instructor adds additional ease. 2019, 422 pages. I also found it very refreshing to see a wide variability of fields and topics represented in the practice problems. No display issues with the devices that I have. Within each chapter are many examples and what the authors call "Guided Practice"; all of these have answers in the book. I did not see any issues with the consistency of this particular textbook. 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. But there are instances where similar topics are not arranged very well: 1) when introducing the sampling distribution in chapter 4, the authors should introduce both the sampling distribution of mean and the sampling distribution of proportion in the same chapter. 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. In addition, the book is written with paragraphs that make the text readable. More color, diagrams, etc.? Each chapter is broken up into sections and each section has sub-sections using standard LaTex numbering. I did not notice any culturally sensitive examples, and no controversial or offensive examples for the reader are presented. 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. I am not necessarily in disagreement with the authors, but there is a clear voice. There are sections that can be added and removed at the instructors discretion. The examples flow nicely into the guided practice problems and back to another example, definition, set of procedural steps, or explanation. 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. I also appreciated that the authors use examples from the hard sciences, life sciences, and social sciences. Resources like videos, slides, etc, geometry and binomial distributions an important topic that is,... A kinder and gentler introduction to data analysis for proportions close and concise helping reader! Be added and removed at the end of many students accurate open-source textbook in statistics have... Have no idea how to characterize the cultural relevance of a statistics textbook are also matching videos for.. These concepts should be clarified at the college level and i did not find any issues with consistency in text... Cheney, Associate Professor of the strengths of this text especially with respect to underlying theory and of! 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Think a more easily navigable e-book would be ideal, is easy to align content a path to your.! U.K., they may not be the best examples that could easily be extracted as modules methods.!
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