The overall length of the book is 436 pages, which is about half the length of some introductory statistics books. 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. The interface is great! 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. A teacher can sample the germane chapters and incorporate them without difficulty in any research methods class. Complete visual redesign. of Contents 1. As an example, I suggest the text provides data analysis by using Binomial option pricing model and Black-Scholes option pricing model. 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. For example, the authors have intentionally included a chapter on probability that some instructors may want to include, but others may choose to excludes without loss of continuity. 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. This is sometimes a problem in statistics as there are a variety of ways to express the similar statistical concepts. The real data sets examples cover different topics, such as politics, medicine, etc. The B&W textbook did not seem to pose any problems for me in terms of distortion, understanding images/charts, etc., in print. Christopher D. Barr is an Assistant Research Professor with the Texas Institute for Measurement, Evaluation, and Statistics at the University of Houston. OpenIntro Statistics covers a first course in statistics, providing a rigorous introduction to applied statistics that is clear, concise, and accessible. Professors looking for in-depth coverage of research methods and data collection techniques will have to look elsewhere. Some examples are related to United States. Reviewed by Gregg Stall, Associate Professor, Nicholls State University on 2/8/17, The text covers the foundations of data, distributions, probability, regression principles and inferential principles with a very broad net. The authors present material from lots of different contexts and use multiple examples. 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. The learner cant capture what is logistic regression without a clear definition and explanation. The interface is fine. I do not think that the exercises focus in on any discipline, nor do they exclude any discipline. I think it would be better to group all of the chapter's exercises until each section can have a greater number of exercises. The discussion of data analysis is appropriately pitched for use in introductory quantitative analysis courses in a variety of disciplines in the social sciences . All of the chapters contain a number of useful tips on best practices and common misunderstandings in statistical analysis. The text is mostly accurate but I feel the description of logistic regression is kind of foggy. It is easy to skip some topics with no lack of consistency or confusion. 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 price was updated from $14.99 for the 3rd Edition to $20 for the 4th Edition, which we believe will be a sustainable price point that helps support OpenIntro as it scales into new subjects. Each chapter contains short sections and each section contains small subsections. 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. 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 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 flow of a chapter is especially good when the authors continue to use a certain example in developing related concepts. It is accurate. HS Statistics (2nd Ed) exercise solutions Available to Verified Teachers, click here to apply for access Intro Stat w/Rand & Sim exercise solutions Available to Verified Teachers, click here to apply for access Previous Editions Click below to explore the history of each textbook that is in its 2nd or later edition. The rationale for assigning topics in Section 1 and 2 is not clear. I would tend to group this in with sampling distributions. read more. For example, income variations in two cities, ethnic distribution across the country, or synthesis of data from Africa. I did have a bit of trouble looking up topics in the index - the page numbers seemed to be off for some topics (e.g., effect size). The organization for each chapter is also consistent. Percentiles? The approach of introducing the inferences of proportions and the Chi-square test in the same chapter is novel. 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. Technical accuracy is a strength for this text especially with respect to underlying theory and impacts of assumptions. 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. Chapter4 (foundations of inference), chapter 5 (inference of numerical data) and chapter 6 (inference of categorical data) provide clear and fresh logic for understanding statistics. The interface of the book appears to be fine for me, but more attractive colors would make it better. More modern approaches to statistical methods, however, will need to include concepts of important to the current replicability crisis in research: measures of effect, extensive applications of power analyses, and Bayesian alternatives. My interest in this text is for a graduate course in applied statistics in the field of public service. Errors are not found as of yet. The authors use the Z distribution to work through much of the 1-sample inference. 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. Especially, this book covers Bayesian probabilities, false negative and false positive calculations. Save Save Solutions to Openintro Statistics For Later. There are a lot of topics covered. 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. The final chapters, "Introduction to regression analysis" and "Multiple and logistical regression" fit nicely at the end of the text book. by David Diez, Mine Cetinkaya-Rundel, Christopher Barr. However, after reviewing the textbook at length, I did note that it did become easier to follow the text with the omission of colorful fonts and colors, which may also be noted as distraction for some readers. This open access textbook provides the background needed to correctly use, interpret and understand statistics and statistical data in diverse settings. I didn't experience any problems. web jul 16 2016 openintro statistics fourth edition the solutions are available online i would suggest this book to everyone who has no The later chapters (chapters 4-8) are built upon the knowledge from the former chapters (chapters 1-3). Although there are some Share. The text needs real world data analysis examples from finance, business and economics which are more relevant to real life. 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. Overall, the text is well-written and explained along with real-world data examples. Some of the sections have only a few exercises, and more exercises are provided at the end of chapters. This text covers more advanced graphical su Understanding Statistics and Experimental Design, Empirical Research in Statistics Education, Statistics and Analysis of Scientific Data. 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. The definitions are clear and easy to follow. Also, for how the authors seem to be focusing on practicalities, I was somewhat surprised about some of the organization of the inference sections. It is as if the authors ran out of gas after the first seven chapters and decided to use the final chapter as a catchall for some important, uncovered topics. The book has a great logical order, with concise thoughts and sections. 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. read more. I found virtually no issues in the grammar or sentence structure of the text. If you are looking for deep mathematical comprehensiveness of exercises, this may not be the right book, but for most introductory statistics students who are not pursuing deeper options in math/stat, this is very comprehensive. The nicely designed website (https://www.openintro.org) contains abundant resources which are very valuable for both students and teachers, including the labs, videos, forums and extras. OpenIntro Statistics textbook solutions from Chegg, view all supported editions. I did not see any issues with the consistency of this particular textbook. I have no idea how to characterize the cultural relevance of a statistics textbook. For example, it is claimed that the Poisson distribution is suitable only for rare events (p. 148); the unequal-variances form of the standard error of the difference between means is used in conjunction with the t-distribution, with no mention of the need for the Satterthwaite adjustment of the degrees of freedom (p. 231); and the degrees of freedom in the chi-square goodness-of-fit test are not adjusted for the number of estimated parameters (p. 282). The p-value definition could be simplified by eliminating mention of a hypothesis being tested. Percentiles? I realize this is how some prefer it, but I think introducing the t distribution sooner is more practical. Sample Solutions for this Textbook We offer sample solutions for OPENINTRO:STATISTICS homework problems. 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. None of the examples seemed alarming or offensive. This book is very clearly laid out for both students and faculty. 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. More extensive coverage of contingency tables and bivariate measures of association would be helpful. Parts I and II give an overview of the most common tests (t-test, ANOVA, correlations) and work out their statistical principles. The 4th Edition was released on May 1st, 2019. The overall organization of the text is logical. The text is quite consistent in terms of terminology and framework. 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. Overall I like it a lot. 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 book covers the essential topics in an introductory statistics course, including hypothesis testing, difference of means-tests, bi-variate regression, and multivariate regression. My biggest complaint is that Well, this text provides a kinder and gentler introduction to data analysis and statistics. 0% 0% found this document useful, Mark this document as useful. Also, non-parametric alternatives would be nice, especially Monte Carlo/bootstrapping methods. Our inaugural effort is OpenIntro Statistics. The authors bold important terms, and frequently put boxes around important formulas or definitions. The graphs are readable in black and white also. The graphs and diagrams were also clear and provided information in a way that aided in understanding concepts. The material was culturally relevant to the demographic most likely to use the text in the United State. In addition, some topics are marked as special topics. The topics are not covered in great depth; however, as an introductory text, it is appropriate. Also, a reminder for reviewers to save their work as they complete this review would be helpful. Generation of Electrical Energy, 7th Edition Gupta B.R. Reviewed by Paul Murtaugh, Associate Professor, Oregon State University on 7/15/14, The text has a thorough introduction to data exploration, probability, statistical distributions, and the foundations of inference, but less complete discussions of specific methods, including one- and two-sample inference, contingency tables, and For example, types of data, data collection, probability, normal model, confidence intervals and inference for single proportions. There are also a number of exercises embedded in the text immediately after key ideas and concepts are presented. It has scientific examples for the topics so they are always in context. Try Numerade free. 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. Any significant rearranging of those sections would be incredibly detrimental to the reader, but that is true of any statistics textbook, especially at the introductory level: Earlier concepts provide the basis for later concepts. I have not noted any inconsistencies, inaccuracies, or biases. 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 Create a clear way to explain this multi-faceted topic and the world will beat a path to your door. It should be pointed out that logistic regression is using a logistic function to model a binary dependent variable. That logistic regression is kind of foggy found virtually no issues in the grammar sentence. Regression without a clear definition and explanation in the United State the 4th Edition was released on May,... My interest in this text especially with respect to underlying theory and impacts assumptions. 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