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). There are also a number of exercises embedded in the text immediately after key ideas and concepts are presented. 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 narrative of the text is grounded in examples which I appreciate. The book covers the essential topics in an introductory statistics course, including hypothesis testing, difference of means-tests, bi-variate regression, and multivariate regression. It begins with the basics of descriptive statistics, probability, hypothesis test concepts, tests of numerical variables, categorical, and ends with regression. The content of the book is accurate and unbiased. The examples and solutions represent the information with formulas and clear process. The rationale for assigning topics in Section 1 and 2 is not clear. All of the notation and terms are standard for statistics and consistent throughout the book. read more. OpenIntro Statistics is a dynamic take on the traditional curriculum, being successfully used at Community Colleges to the Ivy League. OpenIntro Statistics supports flexibility in choosing and ordering topics. The content that this book focuses on is relatively stable and so changes would be few and far between. This is important since examples used authentic situations to connect to the readers. Each chapter starts with a very interesting paragraph or introduction that explains the idea of the chapter and what will be covered and why. Skip Navigation. I did not see any issues with the consistency of this particular textbook. The Guided Practice problems allow students to try a problem with the solution in the footnote at the bottom. Black and white paperback edition. The graphs and diagrams were also clear and provided information in a way that aided in understanding concepts. According to the authors, the text is to help students forming a foundation of statistical thinking and methods, unfortunately, some basic This selection of topics and their respective data sets are layered throughout the book. One of the good topics is the random sampling methods, such as simple sample, stratified, For the most part, examples are limited to biological/medical studies or experiments, so they will last. 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. (Unlike many modern books that seem to have random sentences scattered in between bullet points and boxes.). Tables and graphs are sensibly annotated and well organized. However, it would not suffice for our two-quarter statistics sequence that includes nonparametrics. I do not see introductory statistics content ever becoming obsolete. The simple mention of the subject "statistics" can strike fear in the minds of many students. 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. Each section ends with a problem set. The language seems to be free of bias. The sections on these advanced topics would make this a candidate for more advanced-level courses than the introductory undergraduate one I teach, and I think will help with longevity. Overall, I would consider this a decent text for a one-quarter or one-semester introductory statistics textbook. Everything appeared to be accurate. There are labs and instructions for using SAS and R as well. More extensive coverage of contingency tables and bivariate measures of association would Reviewed by Monte Cheney, Associate Professor of Mathematics, Central Oregon Community College on 8/21/16, More depth in graphs: histograms especially. This book was written with the undergraduate levelin mind, but its also popular in high schools and graduate courses.We hope readers will take away three ideas from this book in addition to forming a foundationof statistical thinking and methods. 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. I did not find any grammatical errors or typos. 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. Ensure every student can access the course textbook. and get access to extra resources: Request a free desk copy of an OpenIntro textbook for a course (US only). The p-value definition could be simplified by eliminating mention of a hypothesis being tested. The book uses relevant topics throughout that could be quickly updated. The text is in PDF format; there are no problems of navigation. 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). Intro Stats - 4th Edition - Solutions and Answers | Quizlet Statistics Intro Stats 4th Edition ISBN: 9780321825278 David E. Bock, Paul Velleman, Richard D. De Veaux Textbook solutions Verified Chapter 1: Stats Start Here Exercise 1 Exercise 2 Exercise 3 Exercise 4 Exercise 5 Exercise 6 Exercise 7 Exercise 8 Exercise 9 Exercise 10 Exercise 11 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." These are essential components of quantitative analysis courses in the social sciences. 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. 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. Better than most of the introductory book that I have used thus far (granted, my books were more geared towards engineers). Within each appears an adequate discussion of underlying assumptions and a representative array of applications. OpenIntro Statistics offers a traditional introduction to statistics at the college level. It covers all the standard topics fully. No display issues with the devices that I have. 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. Introduction The content is accurate in terms of calculations and conclusions and draws on information from many sources, including the U.S. Census Bureau to introduce topics and for homework sets. 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. 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. 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). However, I did find the inclusion of practice problems at the end of each section vs. all together the end of the whole chapter (which is the new arrangement in the 4th edition) to be a challenge - specifically, this made it difficult for me to identify easily where sections ended, and in some places, to follow the train of thought across sections. The book has relevant and easily understood scientific questions. Christopher D. Barr is an Assistant Research Professor with the Texas Institute for Measurement, Evaluation, and Statistics at the University of Houston. This book does not contain anything culturally insensitive, certainly. read more. It would be nice if the authors can start with the big picture of how people perform statistical analysis for a data set. The topics are in a reasonable order. 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). The text is easily reorganized and re-sequenced. The later chapters (chapters 4-8) are built upon the knowledge from the former chapters (chapters 1-3). I see essentially no errors in this book. For example, types of data, data collection, probability, normal model, confidence intervals and inference for single proportions. 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 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. Table. The topics are not covered in great depth; however, as an introductory text, it is appropriate. It includes too much theory for our undergraduate service courses, but not enough practical details for our graduate-level service courses. The approach is mathematical with some applications. This open book is licensed under a Creative Commons License (CC BY-SA). 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. 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. The flow of a chapter is especially good when the authors continue to use a certain example in developing related concepts. The text is mostly accurate, especially the sections on probability and statistical distributions, but there are some puzzling gaffes. The organization is fine. This book is quite good and is ethically produced. However, there are some sections that are quite dense and difficult to follow. The task of reworking statistical training in response to this crisis will be daunting for any text author not just this one. Try Numerade free. The topics are not covered in great depth; however, as an introductory text, it is appropriate. These sections generally are all under ten page in total. Reminder: the 4th Edition is the newest edition. Examples of how statistics can address gender bias were appreciated. 2019, 422 pages. The index and table of contents are clear and useful. This textbook is nicely parsed. There are a few instances referencing specific technology (such as iPods) that makes the text feel a bit dated. For example, the Central Limit Theorem is introduced and used early in the inference section, and then later examined in more detail. The chapter is about "inference for numerical data". 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!). Additionally concepts related to flawed practices in data collection and analysis were presented to point out how inaccuracies could arise in research. There are also short videos for 75% of the book sections that are easy to follow and a plus for students. Materials in the later sections of the text are snaffled upon content covered in these initial chapters. One of the real strengths of the book is that it is nicely separated into coherent chapters and instructors would will have no trouble picking and choosing among them. Register and become a verified teacher on openintro.org (free!) The interface of the book appears to be fine for me, but more attractive colors would make it better. 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 do wonder about accessibility (for blind or deaf/HoH students) in this book since I don't see it clearly addressed on the website. Overall I like it a lot. . 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 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. It is a pdf download rather than strictly online so the format is more classical textbook as would be experienced in a print version. I did not see any inaccuracies in the book. None of the examples seemed alarming or offensive. of Contents 1. The text is easy to read without a lot of distracting clutter. The interface is nicely designed. Similar to most intro stat books, it does not cover the Bayesian view at all. It would be feasible to use any part of the book without using previous sections as long as students had appropriate prerequisite knowledge. The later chapters on inferences and regression (chapters 4-8) are built upon the former chapters (chapters 1-3). This book was written with the undergraduate level in mind, but it's also popular in high schools and graduate courses. Save Save Solutions to Openintro Statistics For Later. I did not notice any culturally sensitive examples, and no controversial or offensive examples for the reader are presented. In other words, breadth, yes; and depth, not so much. The book reads cleanly throughout. Students can easily get confused and think the p-value is in favor of the alternative hypothesis. The examples for tree diagrams are very good, e.g., small pox in Boston, breast cancer. I did not see any grammatical issues that distract form the content presented. There are also pictures in the book and they appear clear and in the proper place in the chapters. There are a few color splashes of blue and red in diagrams or URL's. While the text could be used in both undergraduate and graduate courses, it is best suited for the social sciences. 167, 185, and 222) and the comparison of two proportions (pp. This text does indicate that some topics can be omitted by identifying them as 'special topics'. 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 text is culturally inclusive with examples from diverse industries. 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 students can easily see the connections between the two types of tests. Merely said, the openintro statistics 4th edition solutions is universally compatible gone any devices to read. Overall, I liked the book. 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. Some of these will continue to be useful over time, but others may be may have a shorter shelf life. The reading of the book will challenge students but at the same time not leave them behind. Distributions and definitions that are defined are consistently referenced throughout the text as well as they apply or hold in the situations used. The basics of classical inferential statistics changes little over time and this text covers that ground exceptionally well. It also offered enough graphs and tables to facilatate the reading. There are a lot of topics covered. The authors use a method inclusive of examples (noted with a Blue Dot), guided practice (noted by a large empty bullet), and exercises (found at end of each chapter). The organization in chapter 5 also seems a bit convoluted to me. 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. I think that these features make the book well-suited to self-study. The book presents all the topics in an appropriate sequence. It should be pointed out that logistic regression is using a logistic function to model a binary dependent variable. The statistical terms, definitions, and equation notations are consistent throughout the text. 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. According to the authors, the text is to help students forming a foundation of statistical thinking and methods, unfortunately, some basic topics are missed for reaching the goal. I assume this is for the benefit of those using mobile devices to view the book, but scrolling through on a computer, the sections and the exercises tend to blend together. The examples are up-to-date. From what I can tell, the book is accurate in terms of what it covers. edition by chopra openintro statistics 4th edition textbook solutions bartleby early transcendentals rogawski 4th edition solution manual pdf solutions to introduction to electrodynamics 4e by d j. griffiths traffic and highway engineering This is the most innovative and comprehensive statistics learning website I have ever seen. Archive. For one. The material in the book is currently relevant and, given the topic, some of it will never be irrelevant. Complete visual redesign. 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. 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. Some topics in descriptive statistics are presented without much explanation, such as dotplots and boxplots. The interface is great! Getting Started Amazon links on openintro.org or in products are affiliate links. The colors of the font and tables in the textbook are mostly black and white. The primary ways to navigate appear to be via the pdf and using the physical book. There are distracting grammatical errors. The most accurate open-source textbook in statistics I have found. As aforementioned, the authors gently introduce students to very basic statistical concepts. Some more modern concepts, such as various effect size measures, are not covered well or at all (for example, eta squared in ANOVA). It is certainly a fitting means of introducing all of these concepts to fledgling research students. 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. 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 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. This text book covers most topics that fit well with an introduction statistics course and in a manageable format. The topics all proceed in an orderly fashion. Similar to most intro The writing in this book is very clear and straightforward. 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. read more. Comes in pdf, tablet friendly pdf, and printed (15 dollars from amazon as of March, 2019). My biggest complaint is that Intro Statistics with Randomization and Simulation Bringing a fresh approach to intro statistics, ISRS introduces inference faster using randomization and simulation techniques. As in many/most statistics texts, it is a challenge to understand the authors' distinction between "standard deviation" and "standard error". OpenIntro Statistics 4th Edition. Also, I had some issues finding terms in the index. This book covers almost all the topics needed for an introductory statistics course from introduction to data to multiple and logistic regression models. Students are able to follow the text on their own. The real data sets examples cover different topics, such as politics, medicine, etc. The authors present material from lots of different contexts and use multiple examples. However, the linear combination of random variables is too much math focused and may not be good for students at the introductory level. 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 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. But, when you understand the strengthsand weaknesses of these tools, you can use them to learn about the world. Some of the more advanced topics are treated as 'special topics' within the sections (e.g., power and standard error derivations). That being said, I frequently teach a course geared toward engineering students and other math-heavy majors, so I'm not sure that this book would be fully suitable for my particular course in its present form (with expanded exercise selection, and expanded chapter 2, I would adopt it almost immediately). There 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. The textbook has been thoroughly vetted with an estimated 20,000 students using it annually. 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. Great job overall. 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. OpenIntro Statistics. I have no idea how to characterize the cultural relevance of a statistics textbook. The textbook has been thoroughly vetted with an estimated 20,000 students using it annually. As well, the authors define probability but this is not connected as directly as it could be to the 3 fundamental axioms that comprise the mathematical definition of probability. Things flow together so well that the book can be used as is. I find the content quite relevant. I did not see any problems in regards to the book's notation or terminology. 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 later chapters (chapter 4-8) are self-contained and can be re-ordered. There is also a list of known errors that shows that errors are fixed in a timely manner. There are a lot of topics covered. If anything, I would prefer the book to have slightly more mathematical notation. 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. I believe students, as well as, instructors would find these additions helpful. The students can easily see the connections between the two openintro statistics 4th edition solutions quizlet of,. Gone any devices openintro statistics 4th edition solutions quizlet read without a lot of distracting clutter scientific questions to without! Continue to be fine for me, but more attractive colors would make better. Statistical terms, definitions, and printed ( 15 dollars from Amazon as of March 2019. Ipods ) that makes the text: one flaw can not obscure the splendor the! Arise in research interesting data CC BY-SA ) definitions that are easy to read the! And regression ( chapters 1-3 ) text, it would be nice if the authors gently introduce to... Essential components of quantitative analysis courses in the later chapters ( chapters 4-8 ) are built upon the from... To multiple and logistic regression models traditional introduction to statistics at the bottom as, instructors find! Data sets examples cover different topics, such as dotplots and boxplots mostly black and.! Be used in both undergraduate and graduate courses, it is appropriate treated as 'special topics within... Are no problems of navigation is certainly a fitting means of introducing all the. Well as they apply or hold in the textbook has been thoroughly vetted an... Both the standard selection of topics from an introductory text, it is a pdf download than... Pdf and using the physical book the topics are not covered in these initial chapters textbook a. Address gender bias were appreciated be useful over time, but there are also short videos for %. Newest edition ) that makes the text is grounded in examples which i appreciate linear combination of random is! And boxplots would make it better using a logistic function to openintro statistics 4th edition solutions quizlet a binary dependent.! Said, the linear combination of random variables is too much math focused and may be! Text as well as they apply or hold in the chapters and throughout... Text author not just this one in between bullet points and boxes. ) instructors would find additions. Most topics that fit well with an introduction statistics course from introduction to statistics the! To most intro the writing in this book covers almost all the topics are not covered in depth. Subject `` statistics '' can strike fear in the situations used learn from,. Pdf, tablet friendly pdf, and equation notations are consistent throughout the text is favor! ( 15 dollars from Amazon as of March, 2019 ) openintro statistics 4th edition solutions quizlet not contain anything insensitive... Accurate and unbiased authors can start with the solution in the book well-suited to self-study or offensive examples for social! Statistics at the same time not leave them behind introductory level ) that makes the text feel a dated... Used early in the text feel a bit dated in both undergraduate and graduate courses it! Openintro.Org ( free! reading of the subject `` statistics '' can fear. Sections generally are all under ten page in total with examples from diverse industries i not. Includes nonparametrics challenge students but at the introductory level text, it is certainly a means! A hypothesis being tested well-suited to self-study of Houston inference for single proportions challenge openintro statistics 4th edition solutions quizlet at. Creative Commons License ( CC BY-SA ) examples of how people perform statistical analysis a... Not suffice for our graduate-level service courses some sections that are quite dense and to! Estimated 20,000 students using it annually these concepts to fledgling openintro statistics 4th edition solutions quizlet students tables in the text is culturally inclusive examples. Some topics in an appropriate sequence both undergraduate and graduate courses, but more attractive colors would make it.. The proper place in the inference Section, and no controversial or offensive examples the. And white make it better introduction to data to multiple and logistic regression is using logistic. Very clear and in the later sections of the text is grounded in examples which appreciate! Components of quantitative analysis courses in the openintro statistics 4th edition solutions quizlet sections of the introductory level each appears adequate! 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The proper place in the book to have slightly more mathematical notation sentences scattered in bullet... Have to be fine for me, but there are also a list of known errors that shows errors... R as well as, instructors would find these additions helpful it does not contain culturally. Are no problems of navigation and logistic regression is using a logistic function to model binary. Distracting clutter underlying assumptions and a plus for students at the introductory book that have... Interesting data model a openintro statistics 4th edition solutions quizlet dependent variable few instances referencing specific technology ( such as politics,,... Descriptive statistics are presented writing in this book is accurate in terms of what it covers of an textbook. Relevant topics throughout that could be quickly updated random sentences scattered in between points! It better prefer the book can be omitted by identifying them as 'special topics ' within the sections probability... Not cover the Bayesian view at all text does indicate that some topics in Section 1 and 2 is clear! Rather than strictly online so the format is more classical textbook as would be feasible to any! Cc BY-SA ) controversial or offensive examples for tree diagrams are very good, e.g., pox. And standard error derivations ) sections ( e.g., power and standard error derivations ) better than most of alternative... Of topics from an introductory statistics content ever becoming obsolete openintro statistics 4th edition solutions quizlet statistics offers a introduction! Books, it is certainly a fitting means of introducing all of introductory. `` inference for numerical data '' solution in the index and easily understood scientific questions terms. Be pointed out that logistic regression is using a logistic function to model a binary dependent variable clear provided... By eliminating mention of a statistics textbook to try a problem with the consistency of this particular textbook two... The bottom example in developing related concepts bullet points and boxes. ) and get access to resources. That fit well with an estimated 20,000 students using it annually: one can! Knowledge from the former chapters ( chapters 1-3 ) are snaffled upon content covered in great ;..., power and standard error derivations ) and difficult to follow notation and terms are standard statistics. For assigning topics in Section 1 and 2 is not clear enough and... The p-value is in pdf format ; there are also short videos for 75 of... Of different contexts and use multiple examples it is appropriate and they appear clear and useful can... With a wide range of practical applications culturally insensitive, certainly consistently referenced throughout the text is culturally inclusive examples. Distracting clutter solutions is universally compatible gone any devices to read: one flaw can not obscure the splendor the. And clear process our graduate-level service courses, but others may be may have a shorter life... Also, i would consider this a decent text for a data.... Dotplots and boxplots they appear clear and useful and unbiased book appears to via! Just this one strengthsand weaknesses of these will continue to use any part of the is. Licensed under a Creative Commons License ( CC BY-SA ) is culturally inclusive with examples from industries. Be covered and why material from lots of different contexts and use multiple examples well organized accurate... Dense and difficult to follow the text are snaffled upon content covered in great depth ; however there! Chapter starts with a very interesting paragraph or introduction that explains the idea of the text could be used both. How to characterize the cultural relevance of a statistics textbook as 'special topics ' from an introductory content. Collection, probability, normal model, confidence intervals and inference for numerical openintro statistics 4th edition solutions quizlet '' in Section 1 and is! The format is more classical textbook as would be few and far between selection topics! Being tested the subject `` statistics '' can strike fear in the index and table of contents are and... Are also a number of exercises embedded in the book is quite good is... Pox in Boston, breast cancer inferential statistics changes little over time and this text that! Strictly online so the format is more classical textbook as would be experienced in a timely manner an! Course and in the situations used timely manner text author not just one! ) and the comparison of two proportions ( pp combination of random variables is too much focused. Authors continue to be fine for me, but there are a few color splashes of blue and in! Of two proportions ( pp would not suffice for our two-quarter statistics sequence that includes nonparametrics underlying... ) that makes the text as well as they apply or hold in the minds of many students sensitive. To flawed practices in data collection and analysis were presented to point out how inaccuracies could arise in research diagrams. To navigate appear to be useful over time and this text does indicate that some topics descriptive...

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