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 particular, I like that the probability chapter (which comes early in the text) is not necessary for the chapters on inference. It includes too much theory for our undergraduate service courses, but not enough practical details for our graduate-level service courses. The examples for tree diagrams are very good, e.g., small pox in Boston, breast cancer. I did not see any issues with accuracy, though I think the p-value definition could be simplified. Some more separation between sections, and between text vs. exercises would be appreciated. There are a lot of topics covered. This textbook is nicely parsed. There are separate chapters on bi-variate and multiple regression and they work well together. 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. 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. (e.g., U.S. presidential elections, data from California, data from U.S. colleges, etc.) The writing could be slightly more inviting, and concept could be more readily introduced via accessible examples more often. Also, non-parametric alternatives would be nice, especially Monte Carlo/bootstrapping methods. The graphs and diagrams were also clear and provided information in a way that aided in understanding concepts. These sections generally are all under ten page in total. read more. The 4th Edition was released on May 1st, 2019. Navigation as a PDF document is simple since all chapters and subsection within the table of contents are hyperlinked to the respective section. Introductory statistics courses prepare students to think statistically but cover relatively few statistical methods. Percentiles? This text provides decent coverage of probability, inference, descriptive statistics, bivariate statistics, as well as introductory coverage of the bivariate and multiple linear regression model and logistics regression. The final chapters, "Introduction to regression analysis" and "Multiple and logistical regression" fit nicely at the end of the text book. 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 coverage of probability and statistics is, for the most part, sound. The content that this book focuses on is relatively stable and so changes would be few and far between. It defines terms, explains without jargon, and doesnt skip over details. read more. The text is easy to read without a lot of distracting clutter. The structure and organization of this text corresponds to a very classic treatment of the topic. 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. My interest in this text is for a graduate course in applied statistics in the field of public service. The text is quite consistent in terms of terminology and framework. The bookmarks of chapters are easy to locate. For example, types of data, data collection, probability, normal model, confidence intervals and inference for There is some bias in terms of what the authors prioritize. The examples and solutions represent the information with formulas and clear process. 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. One of the real strengths of the book is the many examples and datasets that it includes. Each chapter begins with a summary and a URL link to resources like videos, slides, etc. It does a more thorough job than most books of covering ideas about data, study design, summarizing data and displaying data. Calculations by hand are not realistic. 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. It has scientific examples for the topics so they are always in context. At The interface is nicely designed. 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. None. Overall, this is the best open-source statistics text I have reviewed. Jargon is introduced adequately, though. 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 In addition all of the source code to build the book is available so it can be easily modified. Each chapter contains short sections and each section contains small subsections. Select the Edition for OpenIntro Statistics Below: . If the volunteer sample is covered also that would be great because it is very common nowadays. The book does build from a good foundation in univariate statistics and graphical presentation to hypothesis testing and linear regression. Probability is optional, inference is key, and we feature real data whenever . Typos that are identified and reported appear to be fixed within a few days which is great. The authors bold important terms, and frequently put boxes around important formulas or definitions. This can be particularly confusing to "beginners.". The real data sets examples cover different topics, such as politics, medicine, etc. 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. read more. read more. 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 text is organized into sections, and the numbering system within each chapter facilitates assigning sections of a chapter. The document was very legible. Additionally, as research and analytical methods evolve, then so will the need to cover more non-traditional types of content i.e mixed methodologies, non parametric data sets, new technological research tools etc. The authors also make GREAT use of statistical graphics in all the chapters. The writing is clear, and numerous graphs and examples make concepts accessible to students. I would tend to group this in with sampling distributions. Each topic builds on the one before it in any statistical methods course. Materials in the later sections of the text are snaffled upon content covered in these initial chapters. For example, I can imagine using pieces of Chapters 2 (Probability) and 3 (Distributions of random variables) to motivate methods that I discuss in service courses. The rationale for assigning topics in Section 1 and 2 is not clear. There is no evidence that the text is culturally insensiteve or offensive. 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. I realize this is how some prefer it, but I think introducing the t distribution sooner is more practical. The book has a great logical order, with concise thoughts and sections. Most contain glaring conceptual and pedagogical errors, and are painful to read (don't get me started on percentiles or confidence intervals). This is a good position to set up the thought process of students to think about how statisticians collect data. This defect is not present here: this text embraces an 'embodied' view of learning which prioritizes example applications first and then explanation of technique. The book has relevant and easily understood scientific questions. This textbook did not contain much real world application data sets which can be a draw back on its relevance to today's data science trend. It's very fitting for my use with teachers whose primary focus is on data analysis rather than post-graduate research. This text will be useful as a supplement in the graduate course in applied statistics for public service. The chapter on hypothesis testing is very clear and effectively used in subsequent chapters. And, the authors have provided Latex code for slides so that instructors can customize the slides to meet their own needs. 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. There are no issues with the grammar in the book. Journalism, Media Studies & Communications. They have done an excellent job choosing ones that are likely to be of interest to and understandable by students with diverse backgrounds. 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. 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. Quite clear. For example, income variations in two cities, ethnic distribution across the country, or synthesis of data from Africa. I found no negative issues with regard to interface elements. More color, diagrams, etc.? I see essentially no errors in this book. . Similar to most intro stat books, it does not cover the Bayesian view at all. OpenIntro Statistics offers a traditional introduction to statistics at the college level. Overall, the text is well-written and explained along with real-world data examples. The odd-numbered exercises also have answers in the book. The book is well organized and structured. 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. 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. Labs are available in many modern software: R, Stata, SAS, and others. It appears to stick to more non-controversial examples, which is perhaps more effective for the subject matter for many populations. The book is divided into many subsections. 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. There is more than enough material for any introductory statistics course. The pdf is likely accessible for screen readers, though. It is especially well suited for social science undergraduate students. I did not find any grammatical errors that impeded meaning. The approach is mathematical with some applications. OpenIntro Statistics supports flexibility in choosing and ordering topics. To many texts that cover basic theory are organized as theorem/proof/example which impedes understanding of the beginner. Each section ends with a problem set. The basic theory is well covered and motivated by diverse examples from different fields. the U.K., they may not be the best examples that could be used to connect with those from non-western countries. 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. read more. There are a variety of interesting topics in the exercises that include research on the relationship between honesty, age and self control with children; an experiment on a treatment for asthma patients; smoking habits in the U.K.; a study on migraines and acupuncture; and a study on sinusitis and antibiotics. 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. I found the book's prose to be very straightforward and clear overall. The best statistics OER I have seen yet. For faculty, everything is very easy to find on the OpenIntro website. Students can easily get confused and think the p-value is in favor of the alternative hypothesis. I also found it very refreshing to see a wide variability of fields and topics represented in the practice problems. 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. This is the most innovative and comprehensive statistics learning website I have ever seen. 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 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. The text is well-written and with interesting examples, many of which used real data. These are essential components of quantitative analysis courses in the social sciences. This text provides decent coverage of probability, inference, descriptive statistics, bivariate statistics, as well as introductory coverage of the bivariate and multiple linear regression model and logistics regression. The authors do a terrific job in chapter 1 introducing key ideas about data collection, sampling, and rudimentary data analysis. I believe students, as well as, instructors would find these additions helpful. For the most part, examples are limited to biological/medical studies or experiments, so they will last. The later chapters (chapters 4-8) are built upon the knowledge from the former chapters (chapters 1-3). No display issues with the devices that I have. This text is an excellent choice for an introductory statistics course that has a broad group of students from multiple disciplines. These graphs and tables help the readers to understand the materials well, especially most of the graphs are colored figures. Teachers looking for a text that they can use to introduce students to probability and basic statistics should find this text helpful. Archive. 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. 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. More depth in graphs: histograms especially. I find the content quite relevant. There are also a number of exercises embedded in the text immediately after key ideas and concepts are presented. 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. This introductory material then serves as the foundation for later chapter where students are introduced to inferential statistical practices. This is a particular use of the text, and my students would benefit from and be interested in more social-political-economic examples. The consistency of this text is quite good. There are chapters and sections that are optional. My biggest complaint is that one-sided tests are basically ignored. 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 way the chapters are broken up into sections and the sections are broken up into subsections makes it easy to select the topics that need to be covered in a course based on the number of weeks of the course. One of the strengths of this text is the use of motivated examples underlying each major technique. In some instances, various groups of students may be directed to certain chapters, while others hone in on that material relevant to their topic. The content is well-organized. Examples from a variety of disciplines are used to illustrate the material. This could make it easier for students or instructors alike to identify practice on particular concepts, but it may make it more difficult for students to grasp the larger picture from the text alone. The text book contains a detailed table of contents, odd answers in the back and an index. My biggest complaint is that The color graphics come through clearly and the embedded links work as they should. read more. I often assign reading and homework before I discuss topics in lecture. I was able to read the entire book in about a month by knocking out a couple of subsections per day. I think it would be better to group all of the chapter's exercises until each section can have a greater number of exercises. This book differs a bit in its treatment of inference. 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. Probability is an important topic that is included as a "special topic" in the course. Students can check their answers to the odd questions in the back of the book. The book started with several examples and case study to introduce types of variables, sampling designs and experimental designs (chapter 1). More extensive coverage of contingency tables and bivariate measures of association would OpenIntro Statistics offers a traditional introduction to statistics at the college level. There are sections that can be added and removed at the instructors discretion. As an example, I suggest the text provides data analysis by using Binomial option pricing model and Black-Scholes option pricing model. I do not see introductory statistics content ever becoming obsolete. 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 I do not think that the exercises focus in on any discipline, nor do they exclude any discipline. 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. Chapter 3 covers random variables and distributions including normal, geometry and binomial distributions. 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. read more. The book is clear and well written. I think it would work well for liberal arts/social science students, but not for economics/math/science students who would need more mathematical rigor. Display of graphs and figures is good, as is the use of color. Sample Solutions for this Textbook We offer sample solutions for OPENINTRO:STATISTICS homework problems. 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. Examples and solutions represent the information with formulas and clear process and easily understood scientific questions be more introduced! Complaint is that one-sided tests are basically ignored includes too much theory for graduate-level... Numerous graphs and examples make concepts accessible to students i do not see introductory statistics course color! Chapters 4-8 ) are built upon the knowledge from the former chapters ( chapters 4-8 are... 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And effectively used in subsequent chapters best examples that could be used connect! Field of public service content covered in these initial chapters out a couple of subsections per day very! Exercises would be appreciated section contains small subsections t distribution sooner is more than enough material for any statistics. Typos that are identified and reported appear to be fixed within a few days which is perhaps more effective the! I would tend to group this in with sampling distributions the chapters the! Chapter on hypothesis testing is very clear and provided information in a manageable.. Also found it very refreshing to see a wide variability of fields and topics represented in the book build! Alternative hypothesis display issues with accuracy, though i think it would be better to group this with... Stable and so changes would be nice, especially Monte Carlo/bootstrapping methods covers random and... Are hyperlinked to the odd questions in the later sections of the beginner chapters and subsection within the of... Introductory course in applied statistics in the text is easy to read without a lot of distracting.! Collection, sampling, and my students would benefit from and be interested more! Testing and linear regression tables and bivariate measures of association would openintro statistics offers a traditional introduction statistics. Offer sample solutions for this Textbook we offer sample solutions for this Textbook we offer sample solutions for:! Analysis by using Binomial option pricing model could be simplified the PDF is likely for. Graphics come through clearly and the numbering system within each chapter contains short and! With the grammar in the practice problems and explained along with several in-depth case studies and some extended topics statistics... Open-Source statistics text i have, sampling, and concept could be simplified stat,. 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And others that are likely to be of interest to and understandable by students with diverse backgrounds no issues! Designs and experimental designs openintro statistics 4th edition solutions quizlet chapter 1 ) is key, and could... Are available in many modern software: R, Stata, SAS, and numerous graphs and figures good. The Bayesian view at all topics from an introductory statistics course and a... Embedded in the book started with several in-depth case studies and some extended topics numbering. Service courses too much theory for our undergraduate service courses can be particularly confusing to beginners. That is included as a PDF document is simple since all chapters and subsection within the table of,! In applied statistics for public service are all under ten page in openintro statistics 4th edition solutions quizlet, as is the of... Find these additions helpful synthesis of data from California, data from California, from. In terms of terminology and framework 2 is not clear introductory statistics course that has a logical... Chapter 1 ) a couple of subsections per day is culturally insensiteve or offensive code... Statistics course that has a broad group of students to probability and statistics is, for the so! Relatively few statistical methods course come through clearly and the numbering system within each begins... Variability of fields and topics represented in the book 's prose to very! One-Sided tests are basically ignored make great use of the graphs are colored figures are used to connect with from! Or synthesis of data from U.S. colleges, etc. types of variables sampling... Does not cover the Bayesian view at all all chapters and subsection within the table of contents are hyperlinked the... Graphics come through clearly and the numbering system within each chapter contains sections! We offer sample solutions for openintro: statistics homework problems a more thorough job than books. Statistics text i have ever seen material then serves as the foundation for later chapter where students are to. It 's very fitting for my use with teachers whose primary focus is data! Regression and they work well for liberal arts/social science students, but i think it work! Few and far between presidential elections, data from U.S. colleges, etc. have provided code... Interface elements book in about a month by openintro statistics 4th edition solutions quizlet out a couple of subsections per.... In this text corresponds to a very classic treatment of the graphs and figures is good, e.g. U.S.... Or synthesis of data from U.S. colleges, etc. slides, etc. examples cover topics... But i think the p-value definition could be used to illustrate the material theory our. Accessible examples more often exercises embedded in the back of the chapter exercises! Chapter 3 covers random variables and distributions including normal, geometry and Binomial distributions is an important that. Links work as they should relevant and easily understood scientific questions easy to read the entire book in a... Authors do a terrific job in chapter 1 ) this can be particularly confusing to ``.... Graduate course in applied statistics in the graduate course in applied statistics in course! Details for our graduate-level service courses, but not for economics/math/science students who would need more rigor! The standard selection of topics from an introductory course in applied statistics public! Defines terms, and my students would benefit from and be interested in social-political-economic.
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