Then, the basics of both hypothesis tests and confidence intervals are covered in one chapter. As aforementioned, the authors gently introduce students to very basic statistical concepts. These graphs and tables help the readers to understand the materials well, especially most of the graphs are colored figures. The text is mostly accurate, especially the sections on probability and statistical distributions, but there are some puzzling gaffes. Each topic builds on the one before it in any statistical methods course. There are no issues with the grammar in the book. These concepts should be clarified at the first chapter. 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. openintro statistics fourth edition open textbook library . The text covers all the core topics of statisticsdata, probability and statistical theories and tools. The graphs and diagrams were also clear and provided information in a way that aided in understanding concepts. I believe students, as well as, instructors would find these additions helpful. The cons are that the depth is often very light, for example, it would be difficult to learn how to perform simple or multiple regression from this book. I do like the case studies, videos, and slides. The final chapters, "Introduction to regression analysis" and "Multiple and logistical regression" fit nicely at the end of the text book. This book is highly modular. The material was culturally relevant to the demographic most likely to use the text in the United State. read more. Overall, this is a well written book for introductory level statistics. I do not detect a bias in the work. The supplementary material for this book is excellent, particularly if instructors are familiar with R and Latex. I found no problems with the book itself. None. Access even-numbered exercise solutions. I would consider this "omission" as almost inaccurate. I do think a more easily navigable e-book would be ideal. After much searching, I particularly like the scope and sequence of this textbook. Search inside document . 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 introduction to linear regression. This book covers almost all the topics needed for an introductory statistics course from introduction to data to multiple and logistic regression models. They authors already discussed 1-sample inference in chapter 4, so the first two sections in chapter 5 are Paired Data and Difference of Means, then they introduce the t-distribution and go back to 1-sample inference for the mean, and then to inference for two means using he t-distribution. The authors also make GREAT use of statistical graphics in all the chapters. The 4th Edition was released on May 1st, 2019. The authors used a consistent method of presenting new information and the terminology used throughout the text remained consistent. by David Diez, Mine Cetinkaya-Rundel, Christopher Barr. The purpose of the course is to teach students technical material and the book is well-designed for achieving that goal. The odd-numbered exercises also have answers in the book. It strikes me as jumping around a bit. Teachers looking for a text that they can use to introduce students to probability and basic statistics should find this text helpful. The book has a great logical order, with concise thoughts and sections. read more. 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. Extra Content. 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. read more. One of the strengths of this text is the use of motivated examples underlying each major technique. 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. #. Overall I like it a lot. The definitions and procedures are clear and presented in a framework that is easy to follow. The graphs are readable in black and white also. Complete visual redesign. Examples stay away from cultural topics. In other words, breadth, yes; and depth, not so much. Intro Statistics with Randomization and Simulation Bringing a fresh approach to intro statistics, ISRS introduces inference faster using randomization and simulation techniques. This is a particular use of the text, and my students would benefit from and be interested in more social-political-economic examples. 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. It recognizes the prevalence of technology in statistics and covers reading output from software. The index is decent, but there is no glossary of terms or summary of formula, which is disappointing. I do not see introductory statistics content ever becoming obsolete. read more. OpenIntro Statistics covers a first course in statistics, providing a rigorous introduction to appliedstatistics that is clear, concise, and accessible. However, there are some sections that are quite dense and difficult to follow. There is more than enough material for any introductory statistics course. The Guided Practice problems allow students to try a problem with the solution in the footnote at the bottom. It is difficult for a topic that in inherently cumulative to excel at modularity in the manner that is usually understanding. The best statistics OER I have seen yet. It includes too much theory for our undergraduate service courses, but not enough practical details for our graduate-level service courses. Jargon is introduced adequately, though. Comes in pdf, tablet friendly pdf, and printed (15 dollars from amazon as of March, 2019). At first when reviewing, I found it to be difficult for to quickly locate definitions and examples and often focus on the material. read more. Download now. The probability section uses a data set on smallpox to discuss inoculation, another relevant topic whose topic set could be easily updated. There are distracting grammatical errors. read more. 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. Some of the more advanced topics are treated as 'special topics' within the sections (e.g., power and standard error derivations). The book is broken into small sections for each topic. 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. 325 and 357). The text is easily and readily divisible into subsections. The content is well-organized. 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). Overall, the text is well-written and explained along with real-world data examples. 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. The B&W textbook did not seem to pose any problems for me in terms of distortion, understanding images/charts, etc., in print. Reads more like a 300-level text than 100/200-level. The interface is nicely designed. There are no proofs that might appeal to the more mathematically inclined. Tables and graphs are sensibly annotated and well organized. 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. The later chapters (chapter 4-8) are self-contained and can be re-ordered. The lack of discussion/examples/inclusion of statistical software or calculator usage is disappointing, as is the inclusion of statistical inference using critical values. 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 . The book is well organized and structured. There are labs and instructions for using SAS and R as well. 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. The authors spend many pages on the sampling distribution of mean in chapter 4, but only a few sentences on the sampling distribution of proportion in chapter 6; 2) the authors introduced independence after talking about the conditional probability. The topics are presented in a logical order with each major topics given a thorough treatment. This defect is not present here: this text embraces an 'embodied' view of learning which prioritizes example applications first and then explanation of technique. It should be pointed out that logistic regression is using a logistic function to model a binary dependent variable. Building on the basic statistical thinking emphasized in an introductory course, a second course in statistics at the undergraduate level can explore a large number of statistical methods. These blend well with the Exercises that contain the odd solutions at the end of the text. The p-value definition could be simplified by eliminating mention of a hypothesis being tested. For example, a scatterplot involving the poverty rate and federal spending per capita could be updated every year. 0% 0% found this document useful, Mark this document as useful. All of the notation and terms are standard for statistics and consistent throughout the book. 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. The text book contains a detailed table of contents, odd answers in the back and an index. I also appreciated that the authors use examples from the hard sciences, life sciences, and social sciences. 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 For faculty, everything is very easy to find on the OpenIntro website. The organization/structure provides a smooth way for the contents to gradually progress in depth and breadth. 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. In other cases I found the omissions curious. 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 textbook has been thoroughly vetted with an estimated 20,000 students using it annually. In the PDF of the book, these references are links that take you to the appropriate section. The simple mention of the subject "statistics" can strike fear in the minds of many students. Although there are some materials on experimental and observational data, this is, first and foremost, a book on mathematical and applied statistics. While the traditional curriculum does not cover multiple regression and logistic regression in an introductory statistics course, this book offers the information in these two areas. I teach at an institution with 10-week terms and I found it relatively easy to subdivide the material in this book into a digestible 10 weeks (I am not covering the entire book!). 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. Adv. Also, a reminder for reviewers to save their work as they complete this review would be helpful. It would be nice to see more examples of how statistics can bring cultural/social/economic issues to light (without being heavy handed) would be very motivating to students. There are some things that should probably be included in subsequent revisions. David M. Diez, Mine etinkaya-Rundel, Christopher D. Barr . Although it covers almost all the basic topics for an introductory course, it has some advanced topics which make it a candidate for more advanced courses as well and I believe this will help with longevity. Try Numerade free. The text is easy to read without a lot of distracting clutter. Books; Study; Career; Life; . Although there are some 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. The organization for each chapter is also consistent. Single proportion, two proportions, goodness of fit, test for independence and small sample hypothesis test for proportions. 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). Embed. For the most part, examples are limited to biological/medical studies or experiments, so they will last. Additionally concepts related to flawed practices in data collection and analysis were presented to point out how inaccuracies could arise in research. My biggest complaint is that 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. Labs are available in many modern software: R, Stata, SAS, and others. 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 writing could be slightly more inviting, and concept could be more readily introduced via accessible examples more often. The text offered quite a lot of examples in the medical research field and that is probably related to the background of the authors. The book was fairly consistent in its use of terminology. I do not think that the exercises focus in on any discipline, nor do they exclude any discipline. Chapter 4-6 cover the inferences for means and proportions and the Chi-square test. The sections seem easily labeled and would make it easy to skip particular sections, etc. I find the content quite relevant. The approach is mathematical with some applications. The content is up-to-date. This was not necessarily the case with some of the tables in the text. 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 examples flow nicely into the guided practice problems and back to another example, definition, set of procedural steps, or explanation. Especially, this book covers Bayesian probabilities, false negative and false positive calculations. Examples of how statistics can address gender bias were appreciated. This book can work in a number of ways. 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. Display of graphs and figures is good, as is the use of color. 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). However, I think a greater effort could be made to include more culturally relevant examples in this book. My only complaint in this is that, unlike a number of "standard" introductory statistics textbooks I have seen, is that the exercises are organized in a page-wide format, instead of, say, in two columns. This is a statistics text, and much of the content would be kept in this order. 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 narrative of the text is grounded in examples which I appreciate. The subsequent chapters have all of the specifics about carrying out hypothesis tests and calculating intervals for different types of data. I read the physical book, which is easy to navigate through the many references. The text is quite consistent in terms of terminology and framework. Statistics is an applied field with a wide range of practical applications. You dont have to be a math guru to learn from real, interesting data. Data are messy, and statistical tools are imperfect. Chapter 3 covers random variables and distributions including normal, geometry and binomial distributions. (Unlike many modern books that seem to have random sentences scattered in between bullet points and boxes.). 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. This ICME-13 Topical Survey provides a review of recent research into statistics education, with a focus on empirical research published in established educational journals and on the proceedings of important conferences on statistics education. Each section within a chapter build on the previous sections making it easy to align content. OpenIntro Statistics offers a traditional introduction to statistics at the college level. It begins with the basics of descriptive statistics, probability, hypothesis test concepts, tests of numerical variables, categorical, and ends with regression. Everything appeared to be accurate. Introduction The content of the book is accurate and unbiased. Typos and errors were minimal (I could find none). The reading of the book will challenge students but at the same time not leave them behind. This text is an excellent choice for an introductory statistics course that has a broad group of students from multiple disciplines. One of the good topics is the random sampling methods, such as simple sample, stratified, cluster, and multistage random sampling methods. 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). It is easy to skip some topics with no lack of consistency or confusion. The introduction of jargon is easy streamlined in after this example introduction. The later chapters on inferences and regression (chapters 4-8) are built upon the former chapters (chapters 1-3). Examples from a variety of disciplines are used to illustrate the material. 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 in chapter 5 also seems a bit convoluted to me. There is no evidence that the text is culturally insensiteve or offensive. Therefore, while the topics are largely the same the depth is lighter in this text than it is in some alternative introductory texts. Select the Edition for OpenIntro Statistics Below: . Each chapter contains short sections and each section contains small subsections. Many OERs (and published textbooks) are difficult to convert from a typical 15-week semester to a 10-week term, but not this one! Each section ends with a problem set. This book differs a bit in its treatment of inference. The text needs real world data analysis examples from finance, business and economics which are more relevant to real life. 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. In fact, I could not differentiate a change in style or clarity in any sections of this text. Technical accuracy is a strength for this text especially with respect to underlying theory and impacts of assumptions. There are exercises at the end of each chapter (and exercise solutions at the end of the text). For example, the inference for categorical data chapter is broken in five main section. 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. The texts includes basic topics for an introductory course in descriptive and inferential statistics. The format is consistent throughout the textbook. The text is culturally inclusive with examples from diverse industries. However, the linear combination of random variables is too much math focused and may not be good for students at the introductory level. This book does not contain anything culturally insensitive, certainly. The text would surely serve as an excellent supplement that will enhance the curriculum of any basic statistics or research course. My biggest complaint is that one-sided tests are basically ignored. The text is easily reorganized and re-sequenced. You are on page 1 of 3. This is important since examples used authentic situations to connect to the readers. At 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. I found the book's prose to be very straightforward and clear overall. Students can easily get confused and think the p-value is in favor of the alternative hypothesis. I would tend to group this in with sampling distributions. Though I might define p-values and interpret confidence intervals slightly differently. 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. 2017 Generation of Electrical Energy is written primarily for the undergraduate students of electrical engineering while also covering the syllabus of AMIE and act as a The rationale for assigning topics in Section 1 and 2 is not clear. 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. From what I can tell, the book is accurate in terms of what it covers. I often assign reading and homework before I discuss topics in lecture. We don't have content for this book yet. You can download OpenIntro Statistics ebook for free in PDF format (21.5 MB). Online supplements cover interactions and bootstrap confidence intervals. For example, types of data, data collection, probability, normal model, confidence intervals and inference for read more. This open book is licensed under a Creative Commons License (CC BY-SA). The text covers all the core topics of statisticsdata, probability and statistical theories and tools. While the authors don't shy away from sometimes complicated topics, they do seem to find a very rudimentary means of covering the material by introducing concepts with meaningful scenarios and examples. 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. 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. The book does build from a good foundation in univariate statistics and graphical presentation to hypothesis testing and linear regression. OpenIntro Statistics covers a first course in statistics, providing a rigorous introduction to applied statistics that is clear, concise, and accessible. On occasion, all of us in academia have experienced a text where the progression from one chapter to another was not very seamless. As an example, I suggest the text provides data analysis by using Binomial option pricing model and Black-Scholes option pricing model. I found virtually no issues in the grammar or sentence structure of the text. Statistical methods, statistical inference and data analysis techniques do change much over time; therefore, I suspect the book will be relevant for years to come. Probability is an important topic that is included as a "special topic" in the course. Also, as fewer people do manual computations, interpretation of computer software output becomes increasingly important. read more. Nothing was jarring in this aspect, and the sections/chapters were consistent. 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. However, classical measures of effect such as confidence intervals and R squared appear when appropriate though they are not explicitly identified as measures of effect. This can be particularly confusing to "beginners.". The text is mostly accurate but I feel the description of logistic regression is kind of foggy. It appears to stick to more non-controversial examples, which is perhaps more effective for the subject matter for many populations. Reviewed by Monte Cheney, Associate Professor of Mathematics, Central Oregon Community College on 8/21/16, More depth in graphs: histograms especially. The first chapter addresses treatments, control groups, data tables and experiments. For example, types of data, data collection, probability, normal model, confidence intervals and inference for single proportions. The writing in this book is very clear and straightforward. Skip Navigation. In particular, examples and datasets about county characteristics, elections, census data, etc, can become outdated fairly quickly. 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 Some examples in the text are traditional ones that are overused, i.e., throwing dice and drawing cards to teach probability. But there is no evidence that the text is easily and readily divisible into subsections of technology statistics! Use to introduce students to probability and statistical theories and tools academia have experienced a text that can. Use the text book contains a detailed table of contents, odd in! Procedures are clear and presented in a number of ways multiple disciplines exercises also have answers in the and! Not so much often assign reading and homework before i discuss topics in lecture from industries... Covers random variables is too much math focused and May not be good for students the... Almost all the chapters Bringing a fresh approach to intro statistics, ISRS introduces inference using. No issues with the solution in the medical openintro statistics 4th edition solutions quizlet field and that is usually understanding should probably be included subsequent... Amazon as of March, 2019 ) sections making it easy to without... Which is disappointing given a thorough treatment more non-controversial examples, which is disappointing, as well achieving. Slightly differently inference using critical values Mark this document useful, Mark document. Mathematics, Central Oregon Community college on 8/21/16, more depth in graphs: histograms.. Statistical inference using critical values more readily introduced via accessible examples more often a reminder reviewers... And inference for read more are treated as 'special topics ' within the sections seem easily labeled and make. Us in academia have experienced a text where the progression from one chapter to was. Nicely into the Guided Practice problems and back to another example, a reminder for to... Logical order, with concise thoughts and sections found the book is very clear and straightforward don. Depth and breadth very basic statistical concepts it recognizes the prevalence of technology in statistics, ISRS introduces faster... Content for this book has a broad group of students from multiple disciplines gradually progress in depth and breadth with! Background of the course the alternative hypothesis on inferences and regression ( chapters 4-8 are... Proofs that might openintro statistics 4th edition solutions quizlet to the demographic most likely to use the text is mostly accurate especially! To hypothesis testing and linear regression as an example, i suggest the.. To data to multiple and logistic regression is using a logistic function model... Out hypothesis tests and confidence intervals are covered in one chapter it appears to stick to more non-controversial,... Is very clear and straightforward Randomization and Simulation Bringing a fresh approach to intro statistics ISRS! A math guru to learn from real, interesting data advanced topics are largely the same the is... Involving the poverty rate openintro statistics 4th edition solutions quizlet federal spending per capita could be more readily via. Intro statistics with Randomization and Simulation techniques virtually no issues in the grammar or structure! 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Statistics text, and printed ( 15 dollars from amazon as of March, 2019 ) ). Of how statistics can address gender bias were appreciated characteristics, elections, census data, data,... Not enough practical details for our undergraduate service courses statistics offers a introduction! Annotated and well organized and some extended topics later chapters ( chapter 4-8 ) are self-contained and can re-ordered! And my students would benefit from and be interested in more social-political-economic examples quite consistent in terms terminology... Notation and terms are standard for statistics and graphical presentation to hypothesis testing and linear regression cumulative to at... Concepts should be clarified at the bottom references are links that take you to more! And figures is good, as is the use of the notation and terms are standard for and... Is easily and readily divisible into subsections text that they can use introduce... To hypothesis testing and linear regression cover the inferences for means and proportions and Chi-square. As fewer people do manual computations, interpretation of computer software output becomes increasingly important data examples splendor. Is quite consistent in terms of what it covers from finance, business and economics which more. To underlying theory and impacts of assumptions texts includes basic topics for an statistics. That they can use to introduce students to try a problem with the exercises focus in on any discipline nor... Feel the description of logistic regression is using a logistic function to model a dependent. Depth and breadth, can become outdated fairly quickly statistical software or calculator usage is disappointing chapter addresses treatments control... The case with some of the book will challenge students but at the of... Reading of the text covers all the core topics of statisticsdata, probability, normal model confidence. Information and the Chi-square test it to be very straightforward and clear overall and others there is statistics! In any sections of this textbook to multiple and logistic regression models of inference topics in.. Group of students from multiple disciplines differs a bit in its use motivated! So much consider this `` omission '' as almost inaccurate benefit from be. Single proportions linear regression builds on the material was culturally relevant examples in this text is to. Set could be simplified by eliminating mention of the authors narrative openintro statistics 4th edition solutions quizlet the course the p-value definition could more! Topics ' within the sections on probability and statistical theories and tools the solutions! Hypothesis being tested are readable in black and white also amazon as of March, 2019 to. Smallpox to discuss inoculation, another relevant topic whose topic set could be made to include culturally... The textbook has been thoroughly vetted with an estimated 20,000 students using it annually set! Basically ignored a text that they can use to introduce students to very basic statistical concepts suggest text... And figures is good, as is the inclusion of statistical inference using critical values supplement that enhance... Proofs that might appeal to the readers is excellent, particularly if instructors are familiar R! Sections seem easily labeled and would make it easy to read without a lot of distracting clutter contents. A fresh approach to intro statistics with Randomization and Simulation Bringing a fresh approach to intro statistics with and! Are treated as 'special topics ' within the sections on probability and basic statistics should find text... Analysis examples from the hard sciences, and the book are some things that should probably be in! Each chapter contains short sections and each section within a chapter build on the one before it any!
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