There are a few instances referencing specific technology (such as iPods) that makes the text feel a bit dated. #. This book is very clearly laid out for both students and faculty. The text offered quite a lot of examples in the medical research field and that is probably related to the background of the authors. And why dump Ch.6 in between with hypothesis testing of categorical data between them? Reviewed by Greg McAvoy, Professor, University of North Carolina at Greensboro on 12/5/16, The book covers the essential topics in an introductory statistics course, including hypothesis testing, difference of means-tests, bi-variate regression, and multivariate regression. Also, the convenient sample is covered. The book uses relevant topics throughout that could be quickly updated. Part I makes key concepts in statistics readily clear. 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. They draw examples from sources (e.g., The Daily Show, The Colbert Report) and daily living (e.g., Mario Kart video games) that college students will surely appreciate. The p-value definition could be simplified by eliminating mention of a hypothesis being tested. This can be particularly confusing to "beginners.". Register and become a verified teacher on openintro.org (free!) I did not find any grammatical errors that impeded meaning. 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. structures 4th edition by chopra openintro statistics 4th edition textbook solutions bartleby early transcendentals rogawski 4th edition solution manual pdf solutions I am not necessarily in disagreement with the authors, but there is a clear voice. And, the authors have provided Latex code for slides so that instructors can customize the slides to meet their own needs. 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. None. These graphs and tables help the readers to understand the materials well, especially most of the graphs are colored figures. As a mathematician, I find this book most readable, but I imagine that undergraduates might become somewhat confused. Therefore, while the topics are largely the same the depth is lighter in this text than it is in some alternative introductory texts. It is certainly a fitting means of introducing all of these concepts to fledgling research students. The odd-numbered exercises also have answers in the book. Some examples are related to United States. 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. I use this book in teaching and I did not find any issues with accuracy, inconsistency, or biasness. There are separate chapters on bi-variate and multiple regression and they work well together. The text covers the foundations of data, distributions, probability, regression principles and inferential principles with a very broad net. The rationale for assigning topics in Section 1 and 2 is not clear. Reviewed by Monte Cheney, Associate Professor, Central Oregon Community College on 1/15/21, 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, While the examples did connect with the diversity within our country or i.e. 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 authors also make GREAT use of statistical graphics in all the chapters. The text is organized into sections, and the numbering system within each chapter facilitates assigning sections of a chapter. It does a more thorough job than most books of covering ideas about data, study design, summarizing data and displaying data. 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. The book has a great logical order, with concise thoughts and sections. I feel that the greatest strength of this text is its clarity. Some topics seem to be introduced repeatedly, e.g., the Central Limit Theorem (pp. Overall I like it a lot. 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. Similar to most intro stat books, it does not cover the Bayesian view at all. Journalism, Media Studies & Communications. It is certainly a fitting means of introducing all of these concepts to fledgling research students. The writing in this book is very clear and straightforward. It would be nice if the authors can start with the big picture of how people perform statistical analysis for a data set. The graphs and tables in the text are well designed and accurate. "Data" is sometimes singular, sometimes plural in the authors' prose. 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. The best statistics OER I have seen yet. Everything appeared to be accurate. #. The text covers the foundations of data, distributions, probability, regression principles and inferential principles with a very broad net. Skip Navigation. the U.K., they may not be the best examples that could be used to connect with those from non-western countries. This defect is not present here: this text embraces an 'embodied' view of learning which prioritizes example applications first and then explanation of technique. There are no issues with the grammar in the book. The text, though dense, is easy to read. Some of the more advanced topics are treated as 'special topics' within the sections (e.g., power and standard error derivations). 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. There are sections that can be added and removed at the instructors discretion. The simple mention of the subject "statistics" can strike fear in the minds of many students. David M. Diez, Harvard School of Public Health, Christopher D. Barr, Harvard School of Public Health, Reviewed by Hamdy Mahmoud, Collegiate Assistant Professor, Virginia Tech on 5/16/22, This book covers almost all the topics needed for an introductory statistics course from introduction to data to multiple and logistic regression models. 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. The index and table of contents are clear and useful. Technical accuracy is a strength for this text especially with respect to underlying theory and impacts of assumptions. This open access textbook provides the background needed to correctly use, interpret and understand statistics and statistical data in diverse settings. The code and datasets are available to reproduce materials from the book. This book covers almost all the topics needed for an introductory statistics course from introduction to data to multiple and logistic regression models. There are labs and instructions for using SAS and R as well. I did not find any grammatical errors or typos. This is a good position to set up the thought process of students to think about how statisticians collect data. The language seems to be free of bias. read more. In general I was satisfied. Reviewed by Elizabeth Ward, Assistant Professor , James Madison University on 3/11/19, Covers all of the topics usually found in introductory statistics as well as some extra topics (notably: log transforming data, randomization tests, power calculation, multiple regression, logistic regression, and map data). 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. The statistical terms, definitions, and equation notations are consistent throughout the text. It definitely makes the students more comfortable with learning a new test because its just the same thing with different statistics. Fisher's exact test is not even mentioned. Labs are available in many modern software: R, Stata, SAS, and others. 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. Overall, the text is well-written and explained along with real-world data examples. OpenIntro Statistics supports flexibility in choosing and ordering topics. OpenIntro Statistics offers a traditional introduction to statistics at the college level. 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. However, it would not suffice for our two-quarter statistics sequence that includes nonparametrics. I also appreciated that the authors use examples from the hard sciences, life sciences, and social sciences. The chapters are well organized and many real data sets are analyzed. It begins with the basics of descriptive statistics, probability, hypothesis test concepts, tests of numerical variables, categorical, and ends with regression. OpenIntro Statistics covers a first course in statistics, providing a rigorous introduction to applied statistics that is clear, concise, and accessible. 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. As in many/most statistics texts, it is a challenge to understand the authors' distinction between "standard deviation" and "standard error". These blend well with the Exercises that contain the odd solutions at the end of the text. The texts includes basic topics for an introductory course in descriptive and inferential statistics. It includes too much theory for our undergraduate service courses, but not enough practical details for our graduate-level service courses. I see essentially no errors in this book. Supposedly intended for "introductory statistics courses at the high school through university levels", it's not clear where this text would fit in at my institution. The final chapter (8) gives superficial treatments of two huge topics, multiple linear regression and logistic regression, with insufficient detail to guide serious users of these methods. At The bookmarks of chapters are easy to locate. I think that the book is fairly easy to read. There is more than enough material for any introductory statistics course. A teacher can sample the germane chapters and incorporate them without difficulty in any research methods class. The material in the book is currently relevant and, given the topic, some of it will never be irrelevant. 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 interface of the book appears to be fine for me, but more attractive colors would make it better. It is accurate. read more. To many texts that cover basic theory are organized as theorem/proof/example which impedes understanding of the beginner. The document was very legible. See examples below: Observational study: Observational study is the one where researchers observe the effect of. I think that these features make the book well-suited to self-study. This is a statistics text, and much of the content would be kept in this order. No display issues with the devices that I have. There are a lot of topics covered. More extensive coverage of contingency tables and bivariate measures of association would be helpful. The text is mostly accurate, especially the sections on probability and statistical distributions, but there are some puzzling gaffes. I found the book's prose to be very straightforward and clear overall. Ensure every student can access the course textbook. However, when introducing the basic concepts of null and alternative hypotheses and the p-value, the book used different definitions than other textbooks. 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. While it would seem that the data in a statistics textbook would remain relevant forever, there are a few factors that may impact such a textbook's relevance and longevity. I was impressed by the scope of fields represented in the example problems - everything from estimating the length of possums' heads, to smoke inhalation in one's line of work, to child development, and so on. Chapter 3 covers random variables and distributions including normal, geometry and binomial distributions. 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. However, I think a greater effort could be made to include more culturally relevant examples in this book. You can download OpenIntro Statistics ebook for free in PDF format (21.5 MB). The task of reworking statistical training in response to this crisis will be daunting for any text author not just this one.
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