Customer Reviews

Statistics in a Nutshell: A Desktop Quick Reference (In a Nutshell (O'Reilly))

5 star:		(8)
4 star:		(3)
3 star:		(3)
2 star:		(2)
1 star:		(1)

 

 

On the one hand I like the book because of its scope and the overall presentation. What I find disturbing is the high amount of errors in all kinds of content (typos, formular errors, table errors, false figures, and so on). Also not great is that the solutions to the problems are given right after the problem itself so it is really hard not to look at the solution before starting to work on the problem. Somebody corrects all those errors and this is a great book on statistics. Right now the errata page at the publisher's web site is just too long.

This book is a reasonably well written introduction to a variety of useful statistical concepts. It is far more readable than the average stats textbook. However, there clearly was some sort of failure in the copy editing process. This book is riddled with small, niggling errors which taken individually aren't so bad, but as a group are very annoying. These errors are not just typos; figures are mislabeled and referenced, the worked through examples contain mathematical errors (including miscalculation of means, etc.), and at least one formula is simply incorrect! These annoying quirks keep this book from being the clear concise text it could be, and no book can be a "Quick Reference" if you can't be sure that what you are looking up is correct! That said, if you take the time as a reader to work through the examples and make sure that the each formula makes mathematical sense, you can get something out of this book.

This book is probably not what you're expecting since most O'Reilly Nutshell books assume you already have thorough knowledge of a subject and you are just looking for "cues" in case you forget something. This book is more of a "Head First" type of book in that it assumes no prior knowledge of the subject. Since O'Reilly is planning a Head First book on Statistics, I'd like to see the difference between this book and that one.

This book focuses on using and understanding statistics in a research or applications context, not as a discrete set of mathematical techniques but as part of the process of reasoning with numbers. It integrates the discussion of issues such as measurement and data management into an introductory statistics text. It serves as an introductory statistics book that is compact, inexpensive, and easy for beginners to understand without being condescending or overly simplistic.

The audience for this book includes students taking introductory statistics classes in high schools, colleges, and universities, professionals who need to learn statistics as part of their current jobs, and finally people who are interested in learning about statistics out of intellectual curiosity.

The book focuses on statistical reasoning. In particular, the book focuses on thinking about data, and using statistics to aid in that process.

The book is organized into four parts: introductory material (Chapters 1-6) that lays the necessary foundation for the chapters that follow; elementary inferential statistical techniques (Chapters 7-11); more advanced techniques (Chapters 12-16); and specialized techniques (Chapters 17-19). The following is the detailed table of contents:

Chapter 1, Basic Concepts of Measurement - Discusses foundational issues for statistics, including levels of measurement, operationalization, proxy measurement, random and systematic error, measures of agreement, and types of bias. Statistics demonstrated include percent agreement and kappa.

Chapter 2, Probability - Introduces the basic vocabulary and laws of probability, including trials, events, independence, mutual exclusivity, the addition and multiplication laws, and conditional probability. Procedures demonstrated include calculation of basic probabilities, permutations and combinations, and Bayes's theorem.

Chapter 3, Data Management - Discusses practical issues in data management, including procedures to troubleshoot an existing file, methods for storing data electronically, data types, and missing data.

Chapter 4, Descriptive Statistics and Graphics - Explains the differences between descriptive and inferential statistics and between populations and samples, and introduces common measures of central tendency and variability and frequently used graphs and charts. Statistics demonstrated include mean, median, mode, range, interquartile range, variance, and standard deviation. Graphical methods demonstrated include frequency tables, bar charts, pie charts, Pareto charts, stem and leaf plots, boxplots, histograms, scatterplots, and line graphs.

Chapter 5, Research Design - Discusses observational and experimental studies, common elements of good research designs, the steps involved in data collection, types of validity, and methods to limit or eliminate the influence of bias.

Chapter 6, Critiquing Statistics Presented by Others - Offers guidelines for reviewing the use of statistics, including a checklist of questions to ask of any statistical presentation and examples of when legitimate statistical procedures may be manipulated to appear to support questionable conclusions.

Chapter 7, Inferential Statistics - Introduces the basic concepts of inferential statistics, including probability distributions, independent and dependent variables and the different names under which they are known, common sampling designs, the central limit theorem, hypothesis testing, Type I and Type II error, confidence intervals and p-values, and data transformation. Procedures demonstrated include converting raw scores to Z-scores, calculation of binomial probabilities, and the square-root and log data transformations.

Chapter 8, The t-Test - Discusses the t-distribution, the different types of t-tests, and the influence of effect size on power in t-tests. Statistics demonstrated include the one-sample t-test, the two independent samples t-test, the two repeated measures t-test, and the unequal variance t-test.

Chapter 9, The Correlation Coefficient - Introduces the concept of association with graphics displaying different strengths of association between two variables, and discusses common statistics used to measure association. Statistics demonstrated include Pearson's product-moment correlation, the t-test for statistical significance of Pearson's correlation, the coefficient of determination, Spearman's rank-order coefficient, the point-biserial coefficient, and phi.

Chapter 10, Categorical Data - Reviews the concepts of categorical and interval data, including the Likert scale, and introduces the R x C table. Statistics demonstrated include the chi-squared tests for independence, equality of proportions, and goodness of fit, Fisher's exact test, McNemar's test, gamma, Kendall's tau-a, tau-b, and tau-c, and Somers's d.

Chapter 11, Nonparametric Statistics - Discusses when to use nonparametric rather than parametric statistics, and presents nonparametric statistics for between-subjects and within-subjects designs. Statistics demonstrated include the Wilcoxon Rank Sum and Mann-Whitney U tests, the median test, the Kruskal-Wallis H test, the Wilcoxon matched pairs signed rank test, and the Friedman test.

Chapter 12, Introduction to the General Linear Model - Introduces linear regression and ANOVA through the concept of the General Linear Model, and discusses assumptions made when using these designs. Statistical procedures demonstrated include simple (bivariate) regression, one-way ANOVA, and post-hoc testing.

Chapter 13, Extensions of Analysis of Variance - Discusses more complex ANOVA designs. Statistical procedures demonstrated include two-way and three-way ANOVA, MANOVA, ANCOVA, repeated measures ANOVA, and mixed designs.

Chapter 14, Multiple Linear Regression - Extends the ideas introduced in Chapter 12 to models with multiple predictors. Topics covered include relationships among predictor variables, standardized coefficients, dummy variables, methods of model building, and violations of assumptions of linear regression, including nonlinearity, autocorrelation, and heteroscedasticity.

Chapter 15, Other Types of Regression - Extends the technique of regression to data with binary outcomes and nonlinear models, and discusses the problem of overfitting a model.

Chapter 16, Other Statistical Techniques - Demonstrates several advanced statistical procedures, including factor analysis, cluster analysis, discriminant function analysis, and multidimensional scaling, including discussion of the types of problems for which each technique may be useful.

Chapter 17, Business and Quality Improvement Statistics - Demonstrates statistical procedures commonly used in business and quality improvement contexts. Analytical and statistical procedures covered include construction and use of simple and composite indexes, time series, the minimax, maximax, and maximin decision criteria, decision making under risk, decision trees, and control charts.

Chapter 18, Medical and Epidemiological Statistics - Introduces concepts and demonstrates statistical procedures particularly relevant to medicine and epidemiology. Concepts and statistics covered include the definition and use of ratios, proportions, and rates, measures of prevalence and incidence, crude and standardized rates, direct and indirect standardization, measures of risk, confounding, the simple and Mantel-Haenszel odds ratio, and precision, power, and sample size calculations.

Chapter 19, Educational and Psychological Statistics - Introduces concepts and statistical procedures commonly used in the fields of education and psychology. Concepts and procedures demonstrated include percentiles, standardized scores, methods of test construction, the true score model of classical test theory, reliability of a composite test, measures of internal consistency including coefficient alpha, and procedures for item analysis. An overview of item response theory is also provided.

Two appendixes cover topics that are a necessary background to the material covered in the main text, and a third provides references to supplemental reading.

I recently received this book and immediately went to the O'Reilly errata [...]There was an extensive list, but after going through it I found that about 80% of the errors noted on it had already been corrected despite the fact that the book I received is still marked "First Edition".

I have yet to read the book, so please take my 4 star rating with a grain of salt, but I had to include that to publish this review. That being said, the fear of excessive typos and errors should no longer deter you from considering this book.

I purchased this book to brush up on some of the more advanced topics in statistics. As I remembered my undergrad stats experience to be a lot of proofs, I was drawn in by the "solid understanding without the numbing complexity of most textbooks" on the back cover.

There are just too many errors to be useful. I found myself going back more and more to my old statistics textbook from college. The examples are clearer and there are better problems to work through. And guess what? You can skip over the "numbing complexity" and still get more from a textbook than you will from "Statistics in a Nutshell."

Furthermore, I don't trust this title as a reference, as I typically have to validate what I'm researching with another textbook. It's quicker and easier to go to a source you know is correct from the start.

O'Reilly really needs to step up for this sloppy book: correct the mistakes and offer those of us with the first versions a free trade-in to the corrected version.

This book has so many mistakes that it becomes difficult to interpret what the authors meant. Incorrect conclusions are drawn from the examples given. I would be ashamed to put my name on this work, especially as a proofreader or editor. Totally unacceptable for a mathematical text. I wouldn't trust the results of the author's statistical work, even though she enjoys belittling other people's work throughout.

For a very long time I had been looking for a basic book--a sort of map if you will--to the myriad number of statistical tests available for conducting research. After speaking to multiple people and reviewing many books, I finally stumbled into two gems. The first one is Learning to use statistical tests in psychology by Judith Greene and Manuela D'Oliveira (209 pp.) Learning to Use Statistical Tests in Psychology. I loved the second edition but bought the third edition, which follows a similar line. In the third edition, however, the most important feature of the book, a set of decision charts fell off. The third edition only includes one chart and so you better make sure it is included before you buy, or write to Open University Press and they will send you a PDF you can print and paste in the back cover. The book is interesting and reads like a page turning novel. The focus is on helping you decide which of the many statistical tests should be selected when conducting a research study. I was looking for a book that spoke about Likert-type questions and the analysis required (answer = Chi-square) and was surprised that Likert scales are not mentioned. The advantage of the Greene-D'Oliveira book is that it has the decision chart and is translated into Spanish (which is important for me). A week later I found the second book, Statistics in a Nutshell: A Desktop Quick Reference by Sarah Boslaugh and Paul Andrew Watters (O'Reilly, 452 pp.) that had everything I was looking for and more. The book is also very well written and entertaining. It has a better index and includes a discussion of Likert scales and the Chi-square. Besides being more thorough, Boslaugh-Watters provide a better discussion of statistical packages. If I could only choose one of the two books, I would purchase Boslaugh-Watters, but I am thrilled to have found both of these superb reference books.

Others have described this book as excellent and I agree. The content is exceptionally well written and it should be ideal for someone with only highschool level math or the well educated math phobics out there.

The one thing other people have not focused on is the lack of problems. Each chapter does have a few problems to work but they are very basic and I don't think there are enough for the typical person to walk away from the book feeling that they can "do" basic statistics. If you want to be able to understand stats this is a great place to start but if you need to get good at doing the statistics you will need a second book.

A related point is that the authors do not focus in on how to do the statistics with any software package. There are lots of references to SAS, SPSS and Excel but no direct advice on how to do the stats with any of those tools (but there is an appendix with references). So, again plan on needing a second book to implement the stuff that is presented here. [...]

Despite those two weaknesses, the quality of the writing and the coverage of a few areas (especially data management in chapter 3) more than makes up for these shortcomings.

I have read many popular statistics books and textbooks. This is quite possibly the best-written book of it's type, a concise introduction/review, and introductory (first-year stats) reference. I'm writing this because I don't think the existing reviews generally give this book enough credit. What's so good about it?

(1) The writing: very clear and concise. But not so concise so as to be difficult or "mysterious." When reading the book, there there several times when I read something, didn't quite understand the point, was certain that the author had too quickly skimmed over the topic, only to turn the page and see a clear two or three paragraph explanation of the point I was trying to understand. The level at which the material is covered is just perfect for this sort of use: not too short so as to leave something out; not too long so as to make topics too complicated. The questions at the end of chapters are "just right" too. They are well chosen, clear, not superficial, but not too difficult.

(2) The organization of topics is very well done. The flow is very natural, and lends itself to effective and efficient coverage of the material.

This may not be the best book to learn statistics from scratch (perhaps a bit too concise, but actually still not too bad), and certainly not a good choice if you are looking for coverage or a reference for advanced topics. But if you are looking for a review, and perhaps an easy to read basic statistics reference, it can't be beat. Quite possibly the best book for this purpose available.

Hope this helps...

While many of the "Nutshell" books assume you already know the subject and simply need a reference book, Statistics In A Nutshell takes a decidedly different approach. While this book assumes you understand basic math concepts, it does not assume you have any prior background in statistics. It then proceeds to cover nearly every fundamental concept taught during an introductory statistics course.

While many introductory statistics courses go through basic descriptive statistics and move through more advanced concepts like ANOVA or liner regression, I found that this book also covered such concepts as non-parametric tests, design of experiments, and the general linear model. Certainly, these concepts are not covered in as much depth as say a college-level courses dedicated to non-parametric statistics. However, the concepts are there, and the authors provide enough information to make the discussion valuable.

The last few sections of the book discuss the use of statistics in a variety of professions, including manufacturing, business, medical, and education fields. Individuals who may not feel comfortable with their math skills can take comfort in the fact that the book provides a section on basic math skills, however, those who may be mathematically challenged may argue with the term "basic".

All in all, I think this is an excellent book for individuals who are looking to implement the scientific method and statistics in their business. The author provides sound explanations of the concepts and plenty of figures and tables to explain difficult concepts. I'd highly recommend this book for the reader who is not afraid of a little math and wants to understand statistics and statistical concepts.