I think it would be better to group all of the chapter's exercises until each section can have a greater number of exercises. I do not think that the exercises focus in on any discipline, nor do they exclude any discipline. This could be either a positive or a negative to individual instructors. I think in general it is a good choice, because it makes the book more accessible to a broad audience.
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. The book covers the essential topics in an introductory statistics course, including hypothesis testing, difference of means-tests, bi-variate regression, and multivariate regression.
The authors make effective use of graphs both to illustrate the The authors make effective use of graphs both to illustrate the subject matter and to teach students how to construct and interpret graphs in their own work. Examples from a variety of disciplines are used to illustrate the material.
The discussion of data analysis is appropriately pitched for use in introductory quantitative analysis courses in a variety of disciplines in the social sciences. However, to meet the needs of this audience, the book should include more discussion of the measurement key concepts, construction of hypotheses, and research design experiments and quasi-experiments. These are essential components of quantitative analysis courses in the social sciences.
The book covers familiar topics in statistics and quantitative analysis and the presentation of the material is accurate and effective. One of the real strengths of the book is the many examples and datasets that it includes. Some of these will continue to be useful over time, but others may be may have a shorter shelf life.
In particular, examples and datasets about county characteristics, elections, census data, etc, can become outdated fairly quickly. Given that this is an introductory textbook, it is clearly written and accessible to students with a variety of disciplinary backgrounds. The purpose of the course is to teach students technical material and the book is well-designed for achieving that goal.
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. 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. 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.
The book does build from a good foundation in univariate statistics and graphical presentation to hypothesis testing and linear regression. There are separate chapters on bi-variate and multiple regression and they work well together. The chapter on hypothesis testing is very clear and effectively used in subsequent chapters. The formatting and interface are clear and effective. There are lots of graphs in the book and they are very readable. There are also pictures in the book and they appear clear and in the proper place in the chapters.
The authors present material from lots of different contexts and use multiple examples. They have done an excellent job choosing ones that are likely to be of interest to and understandable by students with diverse backgrounds. The supplementary material for this book is excellent, particularly if instructors are familiar with R and Latex. The code and datasets are available to reproduce materials from the book.
And, the authors have provided Latex code for slides so that instructors can customize the slides to meet their own needs. For a Statistics I course at most community colleges and some four year universities, this text thoroughly covers all necessary topics.
For example, types of data, data collection, probability, normal model, confidence intervals and inference for For example, types of data, data collection, probability, normal model, confidence intervals and inference for single proportions.
The content is accurate in terms of calculations and conclusions and draws on information from many sources, including the U. Census Bureau to introduce topics and for homework sets. Errors are not found as of yet. Some examples of this include the discussion of anecdotal evidence, bias in data collection, flaws in thinking using probability and practical significance vs statistical significance.
For example, a scatterplot involving the poverty rate and federal spending per capita could be updated every year. 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. The probability section uses a data set on smallpox to discuss inoculation, another relevant topic whose topic set could be easily updated.
This selection of topics and their respective data sets are layered throughout the book. The book uses relevant topics throughout that could be quickly updated. 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 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.
For example, a goodness of fit test begins by having readers consider a situation of whether or not the ethnic representation of a jury is consistent with the ethnic representation of the area.
The introduction of jargon is easy streamlined in after this example introduction. Notation is consistent and easy to follow throughout the text. Tables and graphs are sensibly annotated and well organized. Distributions and definitions that are defined are consistently referenced throughout the text as well as they apply or hold in the situations used.
Each chapter consists of sections. These sections generally are all under ten page in total. This easily allow for small sets of reading on a class to class basis or larger sets of reading over a weekend. Each section within a chapter build on the previous sections making it easy to align content. For example, the inference for categorical data chapter is broken in five main section.
Single proportion, two proportions, goodness of fit, test for independence and small sample hypothesis test for proportions. This keeps all inference for proportions close and concise helping the reader stay uninterrupted in the topic. The topics are presented in a logical order with each major topics given a thorough treatment. 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.
Perhaps an even stronger structure would see all the types of content mentioned above applied to each type of data collection. That is, do probability and inference topics for a SRS, then do probability and inference for a stratified sample and each time taking your probability and inference ideas further so that they are constantly being built upon, from day one!
Navigation as a PDF document is simple since all chapters and subsection within the table of contents are hyperlinked to the respective section. Graphs and tables are clean and clearly referenced, although they are not hyperlinked in the sections. The only visual issues occurs in some graphs, such as on page , which have maps of the U.
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. For example, income variations in two cities, ethnic distribution across the country, or synthesis of data from Africa. The book has a great logical order, with concise thoughts and sections. While section are concise they are not limited in rigor or depth as exemplified by a great section on the "power" of a hypothesis test and numerous case studies to introduce topics.
The reading of the book will challenge students but at the same time not leave them behind. Overall I like it a lot. The best statistics OER I have seen yet. More depth in graphs: histograms especially. The most accurate open-source textbook in statistics I have found. Though I might define p-values and interpret confidence intervals slightly differently. I did not see much explanation on what it means to fail to reject Ho.
I would consider this "omission" as almost inaccurate. 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. Also, as fewer people do manual computations, interpretation of computer software output becomes increasingly important.
Quite clear. The text, though dense, is easy to read. More color, diagrams, photos? Great job overall. However, the introduction to hypothesis testing is a bit awkward this is not unusual. Create a clear way to explain this multi-faceted topic and the world will beat a path to your door. No problems, but again, the text is a bit dense. More color, diagrams, etc.? Overall it was not offensive to me, but I am a college-educated white guy.
Examples of how statistics can address gender bias were appreciated. Overall, this is the best open-source statistics text I have reviewed. Most contain glaring conceptual and pedagogical errors, and are painful to read don't get me started on percentiles or confidence intervals. Also, a reminder for reviewers to save their work as they complete this review would be helpful. 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 Comprehensiveness rating: 3 see less.
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 regression treatment of categorical predictors is limited to dummy coding though not identified as such with two levels in keeping with the introductory nature of the text. There is a bit of coverage on logistic regression appropriate for categorical specifically, dichotomous outcome variables that usually is not part of a basic introduction. Within each appears an adequate discussion of underlying assumptions and a representative array of applications.
Some of the more advanced topics are treated as 'special topics' within the sections e. Some more modern concepts, such as various effect size measures, are not covered well or at all for example, eta squared in ANOVA. 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.
Technical accuracy is a strength for this text especially with respect to underlying theory and impacts of assumptions.
The basics of classical inferential statistics changes little over time and this text covers that ground exceptionally well. 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. The task of reworking statistical training in response to this crisis will be daunting for any text author not just this one. One of the strengths of this text is the use of motivated examples underlying each major technique.
These examples and techniques are very carefully described with quality graphical and visual aids to support learning. 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 consistency of this text is quite good. Notation, language, and approach are maintained throughout the chapters. It is difficult for a topic that in inherently cumulative to excel at modularity in the manner that is usually understanding.
Each topic builds on the one before it in any statistical methods course. This text does indicate that some topics can be omitted by identifying them as 'special topics'. The structure and organization of this text corresponds to a very classic treatment of the topic.
It begins with the basics of descriptive statistics, probability, hypothesis test concepts, tests of numerical variables, categorical, and ends with regression.
I have seen other texts begin with correlation and regression prior to tests of means, etc. This is the third edition and benefits from feedback from prior versions.
I found no negative issues with regard to interface elements. It is a pdf download rather than strictly online so the format is more classical textbook as would be experienced in a print version. It is clear that the largest audience is assumed to be from the United States as most examples draw from regions in the U. The language seems to be free of bias. This text is an excellent choice for an introductory statistics course that has a broad group of students from multiple disciplines.
The basic theory is well covered and motivated by diverse examples from different fields. 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.
This book covers topics in a traditional curriculum of an introductory statistics course: probabilities, distributions, sampling distribution, hypothesis tests for means and proportions, linear regression, multiple regression and logistic 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 book started with several examples and case study to introduce types of variables, sampling designs and experimental designs chapter 1. It would be nice if the authors can start with the big picture of how people perform statistical analysis for a data set.
Chapter 2 covers the knowledge of probabilities including the definition of probability, Law of Large Numbers, probability rules, conditional probability and independence and linear combinations of random variables. However, the linear combination of random variables is too much math focused and may not be good for students at the introductory level.
Chapter 3 covers random variables and distributions including normal, geometry and binomial distributions. Chapter cover the inferences for means and proportions and the Chi-square test.
Chapter 7 and 8 cover the linear , multiple and logistic regression. The book used plenty of examples and included a lot of tips to understand basic concepts such as probabilities, p-values and significant levels etc. The book provides an effective index. 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. All of the calculations covered in this book were performed by hand using the formulas.
As the trend of analysis, students will be confronted with the needs to use computer software or a graphing calculator to perform the analyses. Calculations by hand are not realistic.
However, when introducing the basic concepts of null and alternative hypotheses and the p-value, the book used different definitions than other textbooks. Students can easily get confused and think the p-value is in favor of the alternative hypothesis. The content is up-to-date. Especially, this book covers Bayesian probabilities, false negative and false positive calculations. The text also provides enough context for students to understand the terminologies and definitions, especially this textbook provides plenty of tips for each concept and that is very helpful for students to understand the materials.
The organization for each chapter is also consistent. Each chapter contains short sections and each section contains small subsections. The text is easily reorganized and re-sequenced. The later chapters chapter are self-contained and can be re-ordered. The later chapters chapters are built upon the knowledge from the former chapters chapters The later chapters on inferences and regression chapters are built upon the former chapters chapters But there are instances where similar topics are not arranged very well: 1 when introducing the sampling distribution in chapter 4, the authors should introduce both the sampling distribution of mean and the sampling distribution of proportion in the same chapter.
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 order of introducing independence and conditional probability should be switched. The approach of introducing the inferences of proportions and the Chi-square test in the same chapter is novel. The students can easily see the connections between the two types of tests. The graphs and tables in the text are well designed and accurate. These graphs and tables help the readers to understand the materials well, especially most of the graphs are colored figures. Some examples are related to United States.
Most of the examples are general and not culturally related. 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 text provides enough examples, exercises and tips for the readers to understand the materials. It also offered enough graphs and tables to facilatate the reading. 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. Although there are some Although there are some materials on experimental and observational data, this is, first and foremost, a book on mathematical and applied statistics.
Professors looking for in-depth coverage of research methods and data collection techniques will have to look elsewhere. The coverage of probability and statistics is, for the most part, sound. Most essential materials for an introductory probability and statistics course are covered. The authors do a terrific job in chapter 1 introducing key ideas about data collection, sampling, and rudimentary data analysis.
Chapters 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.
Teachers might quibble with a particular omission here or there e. In other cases I found the omissions curious. 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 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.
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. Appendix A contains solutions to the end of chapter exercises. The index is decent, but there is no glossary of terms or summary of formula, which is disappointing. There are some things that should probably be included in subsequent revisions.
All of the chapters contain a number of useful tips on best practices and common misunderstandings in statistical analysis.
There are also a number of exercises embedded in the text immediately after key ideas and concepts are presented. I suspect these will prove quite helpful to students. Overall, the book is heavy on using ordinary language and common sense illustrations to get across the main ideas. They draw examples from sources e. There are no proofs that might appeal to the more mathematically inclined. 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.
I also appreciated that the authors use examples from the hard sciences, life sciences, and social sciences. This will increase the appeal of the text. A teacher can sample the germane chapters and incorporate them without difficulty in any research methods class.
Things flow together so well that the book can be used as is. The book presents all the topics in an appropriate sequence. The color graphics come through clearly and the embedded links work as they should. It might be asking too much to use it as a standalone text, but it could work very well as a supplement to a more detailed treatment or in conjunction with some really good slides on the various topics. 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, It includes too much theory for our undergraduate service courses, but not enough practical details for our graduate-level service courses.
For example, it is claimed that the Poisson distribution is suitable only for rare events p. 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. Normal approximations are presented as the tool of choice for working with binomial data, even though exact methods are efficiently implemented in modern computer packages.
The section on model selection, covering just backward elimination and forward selection, seems especially old-fashioned. Some topics seem to be introduced repeatedly, e. 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.
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.
One-way analysis of variance is introduced as a special topic, with no mention that it is a generalization of the equal-variances t-test to more than two groups. 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.
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 availability of data sets and functions at a website www. OpenIntro Statistics covers a first course in statistics, providing a rigorous introduction to applied statistics that is clear, concise, and accessible. We hope readers will take away three ideas from this book in addition to forming a foundation of statistical thinking and methods.
But, when you understand the strengths and weaknesses of these tools, you can use them to learn about the world. David M. Not in Library. Libraries near you: WorldCat. Classifications Dewey Edition Notes Includes bibliographical references 3rd section, p.
Classifications Dewey Decimal Class C The Physical Object Pagination xv, , 66, 41, 5 p. Community Reviews 0 Feedback? Lists containing this Book. Lisco from lisco Loading Related Books. June 2, Edited by ImportBot. March 15, July 30, Brown Taking the soft documents can be conserved or saved in computer system or in your laptop. Brown that you have. Brown in your ideal and available gadget. Brown in the downtimes more than chatting or gossiping.
It will not make you have bad habit, but it will lead you to have much better practice to review book General Statistics, By Warren Chase, Fred Bown, F. This volume is designed as a first course in statistics with an emphasis on statistical inference. The concepts are introduced and reinforced with examples and exercises covering a wide range of fields, from sports to medicine.
This edition includes an expanded treatment of P values. Brown Kindle. Posting Komentar. Rabu, 21 November [Y Brown Kindle [Y Brown Doc [Y Brown Doc.
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