Book Description

This reader-friendly book focuses on building linear statistical models and developing skills for implementing regression analysis in real-life situations. It includes applications for a range of fields including engineering, sociology, and psychology, as well as traditional business applications. The authors use the latest material available from news articles, magazines, professional journals, the Internet, and actual consulting problems to illustrate real business situations and how to solve them using the tools of regression analysis. In addition, this book emphasizes model building and multiple regression models and pays special attention to model validation and spline regression. For professionals in any number of fields, including engineering, sociology, and psychology, who would benefit from learning how to use regression analysis to solve problems.

Reviews (2)

DID NOT GET IT
the order date was July 2nd. made payment with paypal. it is July 19th today...no book

clear
It is one of my best book that I have had for all my life. It has a clear english and also I can make good comments on the examples given in the book. ... Read more

Book Description

Every year 472,000 college students enroll in a statistics course. The odds are good they'll need some help.

Aimed at high school and college students who need to take statistics to fulfill a degree requirement, this book follows a standard statistics curriculum with topics that include frequency distributions, probability, binomial distribution, poisson distribution, normal distribution, hypothesis testing, simple regression analysis, and more. ... Read more

Reviews (2)

Great book
This is a great book for those who are interested in truly understanding statistics. With an easy-reading format and plenty of examples, the author walks the reader through examples. The "Stat Facts" boxes were especially helpful.

Great Statistics Book
Great book for learning the basics of application statistics.
Book contains a lot of clear and simple examples, is well illustrated. It also contains examples based on Microsoft Excel.
The book can be used for statistics courses regardless the book's title that some students can find insulting. ... Read more

Book Description

The SPSS 13.0 Guide to Data Analysis is a friendly introduction to both data analysis and SPSS.  Easy-to-understand explanations and in-depth content make this guide both an excellent supplement to other statistics texts and a superb primary text for any introductory data analysis course. With the book, you get a jump-start on describing data, testing hypotheses, and examining relationships using SPSS.  The goal of this book is to provide an unintimidating introduction to data analysis and to SPSS. This edition focuses on topics that interest today's students-in particular, the role of the Internet in society. It is designed for use with SPSS 13.0, including the Student Version.  A data CD is included with this book.

Reviews (53)

Sets the stage for all information architects
This book will teach you some basics on how to most effectively present quantitative information using various sorts of graphs and charts. Afterwards you will know how and why you should get rid of chart junk (gridlines, tick marks, ornaments, etc.) or alternatively using some of the examples on bad design presented, you will see how to manipulate your audience using the "Lie Factor". Actually the advice given in this book could easily fit within just one piece of paper, but then: This book is simply beautiful. It is state of the art for printed books, you almost feel a passion for it. Mr. Tufte takes his own medicine: No words in this book are superfluous. Illustrations and examples are carefully selected and reprinted with the utmost care. It takes no more than some hours to read the book, but afterwards you can use more than just a few hours to study the examples of timeless graphic displays. The only reason why this book is short of five stars is the following: Mr. Tufte uses quite some space providing statistics about charts found in different publications (chart junk percentages, lie factor. Personally I find this information fairly irrelevant and would have preferred more examples of chart remakes. However this book is definately still a MUST have!

I'd give it 6 stars if they'd let me....
Instead I give this book regularly to my students & wish to goodness that more of my colleagues had copies. From the opening pages -where Tufte gives us 4 data sets that are statistically indistinguishable but graphicly at different points of the compass- through the beautifully rendered examples of classical and modern examples of meaningful graphics & "chartjunk" Tufte serves as a wry, witty, and informative guide to the perils & joys of informing or confusing an audience with charts and graphs.
Although in some ways a polemic against the misuse of graphical techniques, Tufte never loses his sense of humor & gives us plenty of really GOOD examples as well as a harsh deconstruction of some truly horrendous images. While this, the first in what has become a series, predates the muddy dawn of computer graphical "presentations" the basic principles outlined in its pages are every bit as applicable to the PowerPoint generation as they were to transparencies & posters. Buy it. read it Use it.

You'll Never Make a Chart the Same Way Again
Edward Tufte is a prophet of the Information Age come to warn us that we must repent or be consigned to oblivion.

One of the great advances which has made the Information Age possible has been the development of easy-to-use graphing software to swiftly create charts which used to take skilled draftsmen days to produce.

Unfortunately, the commoditization and automation of this once-dear skill set has resulted in the proliferation of lies, damned lies, and lousy statistics.

Tufte, a Princeton professor and polymath with passionate interest in statistics, information design, and public policy, offers up a thorough diagnosis of what ails our data-rich, information poor society:

- Poor graphical integrity, where the visual proportions are out of synch with the data's proportions

- Chartjunk, unnecessary clutter which reduces the proportion of data-ink in a graphic

- Poor labeling, which robs data of context

- Low-density presentations, where complex and nuanced data are "dumbed down" for the sake of a fleeting aesthetic

Fear not---Dr. Tufte also provides the reader with a course of treatment (called "Graphical Excellence") thoroughly illuminated with real-world examples drawn throughout history.

This is one of those rare works which feeds both your right and left brain. It is a closely-argued work on behalf of clean and clear communications. It is also a wonderful art book depicting the evolution of an often-misunderstood art form.

Whether you're an engineer, a statistician, a businessman, or a teacher, this beautifully-designed book will help you become a more effective communicator.

Superb Introduction to Quantitative Information Display
Prof. Tufte uses an excellent assortment of charts and graphics to illustrate his points. I found this book to be a quick read; and one I could return to for years to come, as the principles he describes are quite applicable to web site design. I would recommend this book, in fact, I was impressed enough to sign up for the design seminar.

Very Short on Substance; Has Essentially Only A Single Point
Other reviewers have mentioned a few negatives. To me, these mostly boil down to short-on-substance problems. The author is a bit pompous -- which wouldn't matter that much if he had a lot to say. Alas, he does not. In essence, the author makes one -- and only one! -- point with the whole book: eliminate "chart junk" (e.g. 3-D effect bars, etc). He is manically obsessive-compulsive about this point so that he takes it to extremes -- get this: computing "data ink" to "junk ink" ratios he even eliminates the axis line (to increase the ratio). While he's at it, just put tics and only where data are (thus giving marginal distributions of x and y) -- cute idea and it does increase "info"-to-junk ink to the max, but these ideas are nearly absurd extremes. If you really want to learn new techniques and real-value PRINCIPLES get William Cleveland's "Elements of Graphing Data" (original or revised). Don't be put off by publication date -- Cleveland's book is a superbly enjoyable read with eminently useful ideas. I've used principles from Clevelend's book to great effect. I've been graphing for decades, but with Cleveland's book I recently made a very large jump in the quality of my graphical communication. Skip the low-on-substance, one-note Tufte and go for the full-of-substance, emminently useful Cleveland. ... Read more

Book Description

Designed for the one-term introduction to business statistics course with a college algebra prerequisite, this text places emphasis on data and the common techniques and methods used to analyze them in business.It introduces concepts using practical examples and illustrates them with computer output from MINITAB, Systat, Execustat, Microsoft® Excel and other software packages.Review problems requiring students to use previously learned concepts also appear throughout to promote understanding of the relationships among statistical methods. ... Read more

Book Description

Balanced coverage of the methodology and theory of numerical methods in finance

Numerical Methods in Finance bridges the gap between financial theory and computational practice while helping students and practitioners exploit MATLAB for financial applications.

Paolo Brandimarte covers the basics of finance and numerical analysis and provides background material that suits the needs of students from both financial engineering and economics perspectives. Classical numerical analysis methods; optimization, including less familiar topics such as stochastic and integer programming; simulation, including low discrepancy sequences; and partial differential equations are covered in detail. Extensive illustrative examples of the application of all of these methodologies are also provided.

The text is primarily focused on MATLAB-based application, but also includes descriptions of other readily available toolboxes that are relevant to finance. Helpful appendices on the basics of MATLAB and probability theory round out this balanced coverage. Accessible for students–yet still a useful reference for practitioners–Numerical Methods in Finance offers an expert introduction to powerful tools in finance. ... Read more

Reviews (1)

Too much introductive
Since there is few books on financial application of Matlab, I would say that Mr. Brandimarte has done a good pretty good job. I liked especially the fact that the book covers many topics (bond pricing, derivatives, optimization), however, even if the title says "an introduction", it is still too much introductive and you don't get a grip on the amazing capabilities of Matlab. This book is suitable for people discovering Matlab and Finance at the same time. ... Read more

Book Description

Monte Carlo simulation has become an essential tool in the pricing of derivative securities and in risk management. These applications have, in turn, stimulated research into new Monte Carlo methods and renewed interest in some older techniques.

This book develops the use of Monte Carlo methods in finance and it also uses simulation as a vehicle for presenting models and ideas from financial engineering.It divides roughly into three parts. The first part develops the fundamentals of Monte Carlo methods, the foundations of derivatives pricing, and the implementation of several of the most important models used in financial engineering.The next part describes techniques for improving simulation accuracy and efficiency.The final third of the book addresses special topics: estimating price sensitivities, valuing American options, and measuring market risk and credit risk in financial portfolios.

The most important prerequisite is familiarity with the mathematical tools used to specify and analyze continuous-time models in finance, in particular the key ideas of stochastic calculus. Prior exposure to the basic principles of option pricing is useful but not essential.

The book is aimed at graduate students in financial engineering, researchers in Monte Carlo simulation, and practitioners implementing models in industry. ... Read more

Reviews (5)

Mathematically disappointing book
Don't be fooled by the name of the Springer series where this book appeared: if you are looking for a nice list of examples and applications, then this book may be ok, but otherwise, this is not the place to look for proofs and rigorous results.

Excellent Read
Very well written book , all you need to know about MC Methods.
If you want to buy one book buy this one, if you have deep pockets then may be you should get the Peter Jaeckal book along with this. There is another introductory book on Simulation by Sheldon Ross.

a great buy
This is the best book I've read in the last year on mathematical finance. It is a tightly focussed text on Monte Carlo methods no more no less. So you won't find things like day count fracs because that's not what it's about.

Glasserman is a true expert on the topic. My highlight was the chapter on variance reduction where the vast amount of detailed knowledge taught me a lot, although I implement monte carlo pricing models on a day to day basis.

Compared to the best, this is average.
This book has a good explanation of Monte Carlo methods, but so do many others. Given that the focus of this book is interest rate models, I must compare it with the best in the field, and this book falls short. The definitive encyclopedia is "Interest Rate Modelling: Financial Engineering" by Jessica James and Nick Webber. Ms. James's Ph.D. in physics and on-line experience shows through in the sound explanation and application of theory.

Glasserman falls down in the actual applications, since some of the key real-world ingredients such as day counts and quirks of the market are missing.

"Interest Rate Modelling" covers these features and more. It also reviews hundreds of publications. All the methods for term structure modeling are clearly discussed, and the authors made improvements on some of the original works. "Interest Rate Modelling" still the standard for serious professionals, and while this book is good, compared to a superior work it only merits 2.5 stars.

Monte Carlo applications and much more!
I just got this book and start reading a few topics of interest like Risk Management. The book covers a lot of material in various financial products (heavy on interest rate products) and disciplines and does a fairly detailed job. It would have been great to have expanded the book to cover some areas more in depth (credit and operational risk), but otherwise this book is pretty comprehensive in terms of Monte Carlo applications. The book also has a nice appendix section that covers stochastic calculus and other topics. I took a course by Professor Glasserman at Columbia University ages ago and the book as well as the course delivers. This book is an excellent reference for any practitioner or academic alike (highly recommended). If you had to choose, I also think this book is better than the Peter Jaeckel's book on Monte Carlo. Enjoy... ... Read more

Book Description

Gerald Keller's new APPLIED STATISTICS WITH MICROSOFT® EXCEL integrates Excel into the general introductory statistics course. Keller, the co-author of the market-leading STATISTICS FOR MANAGEMENT AND ECONOMICS, Fifth Edition, incorporates his proven three-step problem-solving process throughout this book.The first step, "Identify," is the work a statistician does before the calculations are performed, which entails organizing the experiment, gathering the data, and deciding which statistical techniques to employ.The second step, "Compute," is the computation with Excel.In this step, Keller shows the manual calculation for the simplest of techniques only.For example, he describes how to calculate the sample mean, variance, and standard deviation, how to compute the z-interval estimate of, and the z-test of.The third step, "Interpret," is the interpretation of the computer output, which requires an understanding of statistical concepts. ... Read more

Book Description

This market-leading text provides a comprehensive introduction to probability and statistics for students in engineering and the physical and natural sciences. It is a proven, accurate book with great examples from an outstanding author, Jay Devore. Through the use of lively and realistic examples, students go beyond simply learning about statistics--they actually experience its potential. The book emphasizes concepts, models, methodology and applications, as opposed to rigorous mathematical development and derivations. ... Read more

Reviews (20)

Utility Depends on Your Background
Given any particular university level textbook, there's always a compromise between the author's need to write a professional text and concurrently make it easy to understand. This textbook is no exception, and it leans toward the professional aspect. It is therefore good reference or supplementary material for those who already have some background in statistics, but it is very difficult to learn from the text alone.

As for the book itself, it has strengths and weaknesses. On the good side, it has excellent examples that use real world data, albeit largely from esoteric sources. You can see right off that knowing the material will be very useful in real-world applications, which isn't something you get from many other textbooks.

If you happen to be interested in statistical theory, this book has everything you'd want to know and more. Some of the details get pretty gory, but if you like that sort of thing, it's all here. At the same time, the text is organized so you can easily skip those parts if it's not your ballgame.

The layout and organization, in general, are well thought out and implemented. Important formulas are boxed for easy identification, and key terms are well referenced. The book size and weight is also very reasonable for a textbook. This is attained by very concise, mathematical language. Also, a useful CD is included with the text, containing all the data used in the exercises (various program formats) so you don't have to type it all in manually. All-important tables are located in the back of the book, where you can always find them. Additionally, the appendix section has answers to the odd-numbered exercises. It's not that much of a problem that you only have half the solutions, because concurrent problems are usually similar.

Downsides to the text include the language, which is highly technical, relying heavily on symbols, terminology, and acronyms. Of course, statistics in general is like this, but this book really forces you to learn this rather distasteful aspect of the field. Anyway, it could certainly be more user-friendly, although as such it might be less concise. For those who are well accustomed to such things (e.g. statisticians, mathematicians, military people) the material might be an easy thing to pick up. For others, it can be frustrating when you have to flip back a few hundred pages to remind yourself what a particular Greek alphabet was supposed to represent. Personally, I feel a table of all the symbols with a brief description of each would have been a very welcome addition to the appendix.

Though the exercises are generally well done and challenging, I do have some issues with them. From time to time, one would refer to a problem or data set from way back in the textbook- with no executive summary. It would have been nice to be able to see what was being asked without flipping back hundreds of pages. Additionally, the answers provided in the appendix are often nearly useless, since a terse numerical answer says little about how you might arrive at it. For this purpose there is a solutions manual available, but you might be disinclined to pay for it.

The most prominent difficulty with the text- and I know it's not exclusively mine- is the simple fact that it is a professional work. If you have no knowledge of statistics beforehand, it can be an extremely difficult read. For a while I tried to browse the text before lectures, but I found that it wasn't worth the effort. It took so long to plow through to the 'moral of the story' that I ended up just using the book for review. Fortunately, I was lucky enough to have an excellent statistics professor (the author himself) and so was able to pick up the concepts by simply going to class. If, in the admittedly improbable event that you're new to statistics and are looking for an extra book to make up for a poor instructor, you might want to look at a different one. If you already know something about statistics and want a useful reference, then this is your resource.

I had to get a second textbook for this class...
I'm only on chapter 3, to be fair, but so far I've hated this book. I dumped $120 into it and the example problems are way too simple, and nothing like the actual exercises I'm being graded on. The sections and explanations are short, variables aren't explained well, a lot of assumptions are made. I just finished calculus 1/2/3 with the excellent Stewart book, to then go to this one is a real nightmare. Luckily for me, I've got another book on statistics and probability from a past class, and between the two of them, I can puzzle out much of it. NOT recommended.

Too Wordy, Too Expensive
This is a pretty unhelpful textbook.

Here is part of a typical problem from this book (pg. 83):
"Construct a normal probability plot for the following sample of observations on coating thickness of low-viscosity paint ("Achieving a Target Value for a Manufacturing Process: A Case Study," J. of Quality Technology, 1992: 22-26)...
Does the author really think any student will care about the source of the data and go look it up? If he really felt the need to include citations, they should have been in the back, or better yet, online. The author could have just written "Construct a normal probability plot for the following sample:". It's an utter waste of paper (and of students' money) to create such a wordy textbook.

It's ok when some problems and examples are from the "Real World", but this book insists on doing that for literally EVERY SINGLE ONE. The lack of conciseness makes the book very difficult to study from.

Another pet peeve I have with this book is that while the author does put blue rectangles around key formulas and information to highlight them, the variable definitions are not included in the rectangles. So you have to go back and pore through the previous paragraphs to figure out what the variables in the formulas are!

Finally, too many of the sample problems are, as another reviewer put it, "out of left field". They don't flow from the examples or are just worded in a very confusing way.

I didn't have the heart to give this one star, because clearly a lot of hard work went into creating such an impressive-looking tome. I can keep it on my bookshelf, and my friends can come over and be impressed by what a sophisticated textbook I have. However, I'm afraid the author is less interested in teaching than in merely putting his erudition on display.

I hope you don't get stuck in a class with this book!

Terribly written
I would not recommend this book for anyone who actually needs to understand the concepts of probability and statistics. The notation is bad and most exercises are tedious and add little insight. Instead, I would recommend Milton and Arnold's book (which is a bit outdated, but wonderfully written) for any serious stats student. I gave this book two stars (I think I may be acting a little overgenerous) because it has a nice list of tables of distribution functions in the back and because of the abundance of exercises, some of which are worth working through.

This book is a no no
I'm an engineering student using this book now and i find it is useless. The examples dont flow smoothly for people who have never seen this stuff before, and the reading is unbelievably dry and confusing. ... Read more

Book Description

The first practical look at spatial statistics for the scientist and engineer. Designed for the scientific and engineering professional eager to exploit its enormous potential, Statistics for Spatial Data is a primer to the theory as well as the nuts-and-bolts of this influential technique. Focusing on the three areas of geostatistical data, lattice data, and point patterns, the book sheds light on the link between data and model, and reveals how spatial statistical models can be used to solve a host of problems in science and engineering. Revised to reflect state-of-the-art developments, this edition also features many detailed examples, numerous illustrations, and over 1,000 references. The first fully comprehensive introduction, Statistics for Spatial Data is an essential guide for professionals in biology, earth science, civil, electrical, and agricultural engineering, geography, epidemiology, and ecology. "This excellent book, which presents the most important branches of modern spatial statistics in an exemplary manner, will become a basic reference book and will popularize many methods which have been used until now only by small circles of specialists." -Mathematical Reviews "The majority of geoscientists should make sure they have access to this volume, while for those who are actively involved in applying statistical methods to data (of all sorts) this book should be in their personal libraries. As a compendium of statistical procedures to use to analyze data, this volume will surely provide a good solid framework for some time to come." -Geophysics ... Read more

Reviews (3)

Bible or Paperweight? I hope you are a mathematical guru!
This text is more of a formula-reference with examples and is written for persons with substantial statistical / mathematical background. While I am sure that the text represents substantial value for those with substantial backgrounds in statistics / mathematics/ calculus, the rest of us may be somewhat less enlightened by the text. Make sure you have read over your Statistics 101, 102, 201, 202, 301, 302, 401, 402, 501, 502, Calculus 101, 102, 201, 202, and committed these texts to memory before reading this text. If you never met a formula you didn't like...this text is for you. The author does not hold your hand through the math and if you lack the background required, the book may have better use as a paperweight.

the bible for spatial statistics
This is a wonderful source of information on methods for analyzing spatial data. It is still reasonably current for this rapidly developing area in statistics. You won't find some of the latest research on the joint modeling of space and time. But I expect that Cressie is working on a book that will cover it. It is an encyclopedia of references. The author covers the gamut of techniques. This explains why the book is so lengthy. I think that if he plans a revision he will be more selective and only cover those methods that have proved to be the most promising. Here you can finally learn what kriging is and even see how the bootstrap can be applied to spatial data.

The best book for Geostatistician and not only ...
The book covers very wide spectrum of different simulation and prediction methods and techniques. One can really merit from this book but under good mathematical background. The large number of practical examples helps for better understanding. The most of them can be recommended to apply for the development and testing of own statistical and mathematical software. ... Read more

Book Description

Chris Albright and Wayne Winston have brought their hallmark teach-by-example approach to the undergraduate spreadsheet modeling course. Renowned for their other successful texts in operations research/management science, Winston and Albright successfully show how spreadsheets are used in real life to model and analyze real business problems. By modeling problems using spreadsheets from the outset, SPREADSHEET MODELING AND APPLICATIONS prepares future managers for the types of problems they will encounter on the job. Real cases throughout the text further cement this book's status as the most relevant of its kind on the market. This text is also accompanied by Palisade Corporation's professional spreadsheet add-ins, DecisionTools® Suite. ... Read more

Amazon.com

"There is terror in numbers," writes Darrell Huff in How to Lie with Statistics. And nowhere does this terror translate to blind acceptance of authority more than in the slippery world of averages, correlations, graphs, and trends. Huff sought to break through "the daze that follows the collision of statistics with the human mind" with this slim volume, first published in 1954. The book remains relevant as a wake-up call for people unaccustomed to examining the endless flow of numbers pouring from Wall Street, Madison Avenue, and everywhere else someone has an axe to grind, a point to prove, or a product to sell. "The secret language of statistics, so appealing in a fact-minded culture, is employed to sensationalize, inflate, confuse, and oversimplify," warns Huff.

Although many of the examples used in the book are charmingly dated, the cautions are timeless. Statistics are rife with opportunities for misuse, from "gee-whiz graphs" that add nonexistent drama to trends, to "results" detached from their method and meaning, to statistics' ultimate bugaboo--faulty cause-and-effect reasoning. Huff's tone is tolerant and amused, but no-nonsense. Like a lecturing father, he expects you tolearn something useful from the book, and start applying it every day. Never be a sucker again, he cries!

Even if you can't find a source of demonstrable bias, allow yourself some degree of skepticism about the results as long as there is a possibility of bias somewhere. There always is.

Read How to Lie with Statistics. Whether you encounter statistics at work, at school, or in advertising, you'll remember its simple lessons. Don't be terrorized by numbers, Huff implores. "The fact is that, despite its mathematical base, statistics is as much an art as it is a science." --Therese Littleton ... Read more

Reviews (50)

Some things never change
How to Lie with Statistics by Darrell Huff gives an explanation of common statistical errors. The book is clearly written and is understandable to a reader without a mathematics or statistics background. At only one hundred and forty two pages the book is a quick and easy read.

The book was originally published in 1954. The many copious examples were current at the time of writing, but are extremely dated now. Depending on the readers attitude this may be distracting, or faintly amusing. The advanced age of the examples does not make the text any harder to understand.

While the examples are dated, the concepts appear to be timeless. The same statistical manipulations still seem to be going on nearly fifty years later. The Author covers a wide range of statistical errors, or abuse. All of the types of errors will be familiar to anyone who pays attention to the news, or has seen an advertisement that uses numbers.

How to Lie with Statistics gives the reader the knowledge to detect common statistical skulduggery. If this knowledge were more widely spread, perhaps advertisers, political spinmiesters and sloppy journalists would not be able to get away with that sort of abuse.

Clear, Concise, and Fun
How to Lie with Statistics is a fun and informative look at the was in which statisticians try to decieve the public with misleading statistics. Every chapter contains plenty of real-world examles that provide excellent insight into the concepts. The book is a quick read (only 142 pages) and it holds the reader's attention; both are necessities for an educational work. How to Lie with Statistics is perfect for the beginning statistics student or anyone who wants to learn how they can be decieved through the manipulation of numbers. The reading level and math make this more appropriate for high school or college students than for younger students. Using three randomly selected paragraphs, the mean Flesch-Kincaid grade level was 10.4; thus this book is excellent for sophomores or advanced freshman. Although the writing is a little dated (1954), it is still clear and highly relevant. Overall, this is a highly recommended and worthwhile read.

Defend yourself from the number-tossers
How to Lie with Statistics, by Darrel Huff, should be required reading for everyone. The cachet of numbers are used all the time in modern society. Usually to end arguments--after all, who can argue with "facts"? Huff shows how the same set of numbers can be tweaked to show three different outcomes, depending on where you start and what you use. The fundamental lesson I learned from this book is that mathematical calculation involves a whole set of conditions, and any number derived from such a calculation is meaningless without understanding those conditions.

He also mentions that colleagues have told him that the flurry of meaningless statistics is due to incompetence--he dispatches this argument with a simple query: "Why, then, do the numbers almost always favor the person quoting them?" Huff also provides five questions (not unlike the five d's of dodgeball) for readers to ask, when confronted with a statistic:

1. Who says so?

2. How does he know?

3. What's missing?

4. Did somebody change the subject?

5. Does it make sense?

All this is wrapped up in a book with simple examples (no math beyond arithmetic, really) and quaint 1950s prose. In addition humor runs from the beginning (the dedication is "To my wife with good reason") to the end (on page 135, Huff says "Almost anybody can claim to be first in something if he is not too particular what it is"). This book is well worth a couple hours of your time.

Tools of Lying Liars and those who believe
(...)

One particular "statistic" tells it all. "A study showed that 98% of all heroin addicts started out by drinking milk. Therefore the conclusion is that milk consumption leads to heroin addiction and to protect society we should ban the sale of milk."

It seems pretty farfetched, but the (il)logic applied above is still used today to sell products, ideas and even legislation that controls our lives.

My advice, Read This Book and learn to see beyond the faulty studies and conclusions still used to manipulate us for other's gain!

An Entertaining Primer on the Validity of Statistics
Although "How to Lie with Statistics" is a bit dated (having been written in the 1950's), the principles it puts forth are still valid today--if not moreso than ever--and the material is delivered in clear, concise, and even entertaining anecdotes and illustrations.

How often do you hear statistics bandied about in the media or used to try to prove some special-interest point? "Of course" the people quoting the figures must be right with numbers on their sides... until you look at just how those numbers were arrived at.

This book isn't truly a guide on how to lie with statistics, but it is an excellent text that informs the reader both how others will lie to them using statistics and on how to interpret the validity of purported statistical data. ... Read more

Book Description

STATISTICAL SLEUTH is an innovative treatment of general statistical methods, taking full advantage of the computer, both as a computational and an analytical tool. The material is independent of any specific software package. In "The American Statistician" (February 2000, Vol. 54, No. 1), George Cobb commented, "What is new and different about Ramsey and Schafer's book, what makes it a 'larger contribution,' is that it gives much more prominence to modeling and interpretation of the sort that goes beyond the routine patterns." His students did "substantially better" on term papers based on the analysis of data. In the book, the focus is on a serious analysis of real case studies; on strategies and tools of modern statistical data analysis; on the interplay of statistics and scientific learning; and on the communication of results. With interesting examples, real data, and a variety of exercise types (conceptual, computational, and data problems), the authors get students excited about statistics. ... Read more

Reviews (4)

Crappy book
Horrible, solution manual is a joke. I could have written a better book than this piece of garbage.

Great Intro Level Statistics Book
This is a great introductory statistics book, for students who are taking beginning level stats courses. The examples are cleary laid out and it's not too heavy on the theory.

The Statistical Sleuth has it's good points...
This unique book has it's good points. It attmepts to show how to use different types of statistical analyses work together and what order to use them in.

However, it is poorly written. The authors will start to use terms and ideas that may or may not be defined two or ten pages later, or maybe in a following chapter and you're never quite sure if something will be explained or whether this is something you should already know. This makes for a slow and aggrevating read to the neophyte. Sometimes it seems like a concise statement of the subjects it addresses, but maybe only after you already have mastered them.

If the authors reread the book with a fresh eye, it could be edited and rewritten into a real treasure. Excellent start

An excellent graduate-level text
This is a superb text for graduate students or advanced undergraduates. The case studies are excellent, and the approach is innovative. The strength of the book is its coverage of regression models (linear algebra not used - a weakness if you want to program in Matlab, but ok for SPSS). The examples are drawn from Ecology (esp. Island biogeography), Evolutionary biology, geochemistry, environmental policy, medicine and law. There is something of interest for even the most diverse audience. ... Read more

Book Description

A rigorous, systematic presentation of modern longitudinal analysis
Longitudinal studies, employing repeated measurement of subjects over time, play a prominent role in the health and medical sciences as well as in pharmaceutical studies. An important strategy in modern clinical research, they provide valuable insights into both the development and persistence of disease and those factors that can alter the course of disease development.
Written at a technical level suitable for researchers and graduate students, Applied Longitudinal Analysis provides a rigorous and comprehensive description of modern methods for analyzing longitudinal data. Focusing on General Linear and Mixed Effects Models for continuous responses, and extensions of Generalized Linear Models for discrete responses, the authors discuss in detail the relationships among these different models, including their underlying assumptions and relative merits. The book features:
* A focus on practical applications, utilizing a wide range of examples drawn from real-world studies
* Coverage of modern methods of regression analysis for correlated data
* Analyses utilizing SAS(r)
* Multiple exercises and "homework" problems for review
An accompanying Web site features twenty-five real data sets used throughout the text, in addition to programming statements and selected computer output for the examples. ... Read more

Book Description

Going beyond the conventional mathematics of probability theory, this study views the subject in a wider context. It discusses new results, along with applications of probability theory to a variety of problems. The book contains many exercises and is suitable for use as a textbook on graduate-level courses involving data analysis. Aimed at readers already familiar with applied mathematics at an advanced undergraduate level or higher, it is of interest to scientists concerned with inference from incomplete information. ... Read more

Reviews (7)

Ontological and Epistomological Probability
I read this book before it was published; I downloaded it from a WU website. It has been of immense use to me in my career, it is a very practical book. Other reviews that say Dr. Jaynes' ideas are at odds with traditional measure theoretic probability are mistaken. Dr. Jaynes is a true Baysian. A Baysian is one who believes that probabilities do not model serendipity in nature, but do model subjective certainty. The Bayesian concept of probability is epistomological, i.e. the uncertainty is in our minds, not in objective reality. Traditional probability takes the reverse view: probabilities model unpredictable events, they are a model of objective reality like any science, i.e. probabilities are ontological. The trick is to realize the two are not mutually exclusive! There can be true ontological randomness in nature, and our minds can have uncertainty from incomplete knowledge as well. Probability theory as a branch of mathematics makes no claim what it models. The beauty is that probabiltity distributions integrate the two seamlessly. Thus, it is perfectly valid to put a distribution on an unknown parameter, epistomologically unknown, and derive that distribution from an experiment with, presumably, ontological randomness. Dr. Jaynes' book is well worth reading for the many case studies he presents. His background as a physicist is key to understanding some of the esoteric philisophical points.

The most important book on probability theory in decades
Reading this book is an exhilarating intellectual adventure. I found that it shed light on many mysteries and answered questions that had long troubled me. It contains the clearest exposition of the fundamentals of probability theory that I have ever encountered, and its chatty style is a pleasure to read. Jaynes the teacher collaborates fully with Jaynes the scientist in this book, and at times you feel as if the author is standing before you at the blackboard, chalk in hand, giving you a private lesson. Jaynes's advice on avoiding errors in the application of probability theory -- reinforced in many examples throughout the book -- is by itself well worth the price of the book.

If you deal at all with probability theory, statistics, data analysis, pattern recognition, automated diagnosis -- in short, any form of reasoning from inconclusive or uncertain information -- you need to read this book. It will give you new perspectives on these problems.

The downside to the book is that Jaynes died before he had a chance to finish it, and the editor, although capable and qualified to fill in the missing pieces, was understandably unwilling to inject himself into Jaynes's book. One result is that the quality of exposition suffers in some of the later chapters; furthermore, the author is not in a position to issue errata to correct various minor errors. Volunteer efforts are underway to remedy these problems -- those who buy the book may want to visit the "Unofficial Errata and Commentary" website for it, or check out the etjaynesstudy mailing list at Yahoo groups.

Truthful
it offers a mathematical discussion of probability
from the point of view of information theory. It argues
against the frequentist approach. The author is absolutely
right: probability has meaning only as incomplete
knowledge (but still objective). Any frequentist approach
is problematic. A real diamond.

Invaluable
This book has been on the web in unfinished form for a number of years and has shaped my scientific thinking more than any other book. I believe it constitutes one of the most important scientific texts of the last hundred years. It convincingly shows that "statistics", "statistical inference", "Bayesian inference", "probability theory", "maximum entropy methods" , and "statistical mechanics" are all parts of a large coherent theory that is the unique consistent extension of logic to propositions that have degrees of plausibility attached to them. This is already a theoretical accomplishment of epic proportions. But in addition, the book shows how one actually solves real world problems within this frame work, and in doing so shows what a vastly wider array of problems is addressable within this frame work than in any of the forementioned particular fields.
If you work in any field where on needs to "reason with incomplete information" this book is invaluable.

As others have already mentioned, Jaynes never finished this book. The editor decided to "fill in" the missing parts by putting excercises that, when finished by the reader, provide what (so the editor guesses) Jaynes left out. I find this solution a bit disappointing. The excercises don't take away the impression that holes are left in the text. It would have been better if the editor had written the missing parts and then printed those in different font so as to indicate that these parts were not written by Jaynes. Better still would have been if the editor had invited researchers that are intimately familiar with Jaynes' work and the topic of each of the missing pieces to submit text for the missing pieces. The editor could then have chosen from these to provide a "best guess" for what Jaynes might have written.

Finally, there is the issue of Jaynes' writing style. This is of course largely a matter of taste. I personally like his writing style very much because it is clear, and not as stifly formal as most science texts. However, some readers may find his style too belligerent and polemic.

Brilliant but attended by many misunderstandings
To "pure" mathematicians, probability theory is measure theory in spaces of measure 1. To the extent to which you remain a "pure" mathematician, this book will be incomprehensible to you.

To frequentist statisticians, probability theory is the study of relative frequencies or of proportions of a population; those are "probabilities".

To Bayesian statisticians, probability theory is the study of degrees of belief. Bayesians may assign probability 1/2 to the proposition that there was life on Mars a billion years ago; frequentists will not do that because they cannot say that there was life on Mars a billion years ago in precisely half of all cases -- there are no such "cases".

To _subjective_ Bayesians, probability theory is about subjective degrees of belief. A subjective degree of belief is merely how sure you happen to be.

"Noninformative" _objective_ Bayesians assign "noninformative" probability distributions when they deal with uncertain propositions or uncertain quantities, and replace them with "informative" distributions only when they update them because of "data". "Data", in this sense, consists of the outcomes of random experiments.

"Informative" _objective_ Bayesians -- a rare species -- ask what degree of belief in an uncertain proposition is logically necessitated by whatever information one has, and they don't necessarily require that information to consist of outcomes of random experiments.

Jaynes is an "informative" objective Bayesian. This book is his defense of that position and his account of how it is to be used.

"Pure" mathematicians will not find that this book resembles that branch of "pure" mathematics that they call probability theory.

Jaynes rails against those he disagrees with at great length. Often he is right. But often he simply misunderstands them. For example, writing in the 1990s, he said that pure mathematicians reject the use of Dirac's delta function and its derivatives, and related topics. That is nonsense; the delta function has long been considered highly respectable, and required material in the graduate curriculum. Unfortunately Jaynes's misunderstandings may cause some others to misunderstand him when he is right. Statisticians are more informed than "pure" mathematicians and will disagree with Jaynes for better reasons. _Some_ statisticians will agree with him.

Jaynes has many flaws, made all the more annoying by the fact that we need to overlook them in order to understand him. His message is important. ... Read more

Reviews (7)

Excellent self-tutorial
The book is based upon biostatistics courses taught by the authors. It is designed to be used as a self-tutorial if so desired. The explainations and examples are excellent.

additional comments on second edition
I have previously reviewed this book. My review pertains to the second edition as that is the only edition I have.

Recently I did some consulting for a colleague. He had some data that he wanted to test for the presence of a single outlier. I referred him to the procedures due to Grubbs and Dixon. I also mention the book by Barnett and Lewis which has the most detailed account of outlier methods. However, Barnett and Lewis is so detailed that it can be overwhelming for a beginner. Fortunately my friend has a copy of Sokal and Rohlf's book. I believe he has the same second edition that I have. They provide a good elementary treatment of these methods and have tables to use. Unfortunately, I discovered that the tables are in a separate supplement. My colleague has the supplement but I don't. The reader should be aware that the supplement is needed to implement some of the procedures in the book that require tables. It is not expensive but it is essential. I imagine that the same is true for the third edition but I am not sure. Regardless this is an excellent refer for biostatisticians and practitioners including regulatory affairs analysts and medical writers.

nice reference for users of biostatistics
This book has served well as a reference source on biostatistical methods for statisticians and non-statisticians alike. It includes many of the important topics. It provides detailed descriptions of regression, correlation and analysis of variance. It emphasizes the required assumptions. It is written at an introductory level. It also covers aspects of biological data and special topics such as "combining probabilities' (i.e. meta-analysis), randomization tests (i.e. permutation methods such as Fisher's exact test), and the jackknife.

Important topics that are not included are survival analysis, sample size determination and Bayesian methods.

Excellent basic text of statistical methods
This book and BIOSTATISTICAL ANALYSIS by Jerrold Zar are my main "go to" books when I have questions about statistical methods, applications, or interpretation.

Zar's book is probably the more understandable primer text on statistics of the two, but BIOMETRY is better at addressing non-parametrics, though it is certainly not a comprehensive treatment of that field of statistical analysis.

I typically go to Zar first, then to Sokal and Rohlf -- a great one-two combination that takes care of most of my statistical needs.

I appreciate the inside covers of BIOMETRY, with its summary table that provides a starting place for choosing the most likely statistical tests for a give comination of numbers of samples and numbers of variables in an experiment.

You should be advised that the book BIOMETRY does not contain tables of critical values. You will need to purchase the book STATISTICAL TABLES by Rohlf and Sokal to get them.

All in all, an excellent book on statistical methods.

4.5 to 5 stars...I'll give it 5 stars.

Alan Holyoak

Excellent for developing intuition, but that's about it
I took Biometry as a course at Stony Brook with Rohlf. I think the book does an excellent job at building a conceptual understanding of what different statistical techniques are used for. Coming out of the course (which was essentially Rohlf reading his book to us), one develops an excellent ability to look at an experiment and determine what type of analysis should be done. This is pretty damn important. What the book fails to do is, once you know which test to use, help you analyze your data using popular statistical programs. Other texts (e.g., Tabachnik and Fidel) appreciate that knowing which test to use is only half the battle. Biometry is weak at helping you actually run tests.

One great stength of Biometry is its treatment of non-parametric data. It is by far the best treatment I have seen in an introductory text. I would highly recommend this book to anyone whose data violates assumptions of the typical ANOVA model. ... Read more

Book Description

A comprehensive look at learning and generalization theory. The statistical theory of learning and generalization concerns the problem of choosing desired functions on the basis of empirical data. Highly applicable to a variety of computer science and robotics fields, this book offers lucid coverage of the theory as a whole. Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more. ... Read more

Reviews (2)

new approach to inference based on VC dimension
Vapnik and Chernovenkis extended the Glivenko-Cantelli Theorem in their work on classification and statistical learning. Vapnik in recent texts has described a form of nonparametric statistical inference based on approximating functions and the Vapnik-Chernovenkis dimension.

In an earlier book published by Springer-Verlag he develops the basics of the theory. However to keep the mathematical level excessible to computer scientists and engineers he avoided the mathematical proofs needed for mathematical rigor. This text is an advanced text that provides the rigorous development. Although the preface and chapter 0 give the reader a idea of what is to come the rest of the text is difficult reading.

The theory has been quite successful at attacking the pattern recognition/ classification problem and provides a basis for understanding support vector machines. However Vapnik sees a much broader application to statistical inference in general when the classical parametric approach fails.

If you have a strong background in probability theory you should be able to wade through the book and get something out of it. If not I recommend reading section 7.9 of "The Elements of Statistical Learning" by Hastie, Tibshirani and Friedman. That will give you an easily understandable view of the VC dimension. Also sections 12.2 and 12.3 of their text will give you some appreciation for support vector machines and the error rate bounds obtainable for them based on the VC dimension.

Rich & Valuable
This book aims at rigorours and deep treatment of statistical learning and is divided into three parts :

(I)THEORY OF LEARNING AND GENERALIZATION;

(II)SUPPORT VECTOR ESTIMATION OF FUNCTIONS;

(III)STATISTICAL FOUNDATION OF LEARNING THEORY'

For anyone intending to dive into this topic intriguing readers shull find their task rather not simple when exploring this mathematical exposition.This is because of the mature nature behind the basic theory .In order to gain most of the benefit ,interested and even involved researchers are urged and should assume all the requirements for a vast and solid mathematical background.

I Think the book constitutes a respectful and organized 'exhibition' that you will not find in any other place. Althought there are excellent books discussing SVMs and Machine-Learning/ Intelligence,eventually all emenate from the theory.Regarding the book rating it is was not rated upon how much you retrieve as concepts, but how well the propositions offer a precious appreciation of the substantial theory.In otherwords, this book is not the place for a first time learning, but it is serves as a bridge between interrelated elements of such incredibly growing area.

For the book: "The Nature of Statistical learning Theory" also by Vapnik you can find a review by Vladimir Cherkassky in The IEEE TRANSACTIONS ON NEURAL NETWORKS VOL. 8, NO. 6, NOVEMBER 1997 . ... Read more

Book Description

This book is a valuable reference to Basic Probability and related problems, featuring unique discussions published in recent journals to support individual investigation. Chapter topics include combinatorial methods, conditional probability and independence, random variables, distributions, and simulation. For professionals in the fields of computerand actuarial science, electrical and industrial engineering,, operations research, applied mathematics, and statistics, who desire additional input to help solve the indeterministic business, government, and engineering problems they encounter at work.

Reviews (9)

VERY POOR QUALITY
This is a pathetic book. You can study as hard as you like, but you will not be able to solve the problems at the end of each section. Why? Because those problems are totally unrelated to what's explained in the chapter. I know most people say this book is good -- its because they're either paid by the publisher to praise this book or they're phd or graduate students. And please don't think I'm making a bad review because I'm a weak student. I have an excellent background in calculus and other pre-requisits for this course. I have aced most of my math courses at my university, and never have I gotten anything less than B in any math course I took. I have done over 26 credits in math for my BS in EE -- calculus, linear algebra, Differential Equations, etc and I got A in 16 and B in 10 of those credits. However when I took this course, the book is so extremely poor that I got 60 in the first test. And thereafter the professor said exams will be open book and open notes because the material is simply too poor. Only students having a very strong background in probability would understand this. For a beginner in probability, this book sucks big time.

Don't buy another!
This is an excellent reference book for anyone taking a graduate level Math Stat class. I have read Hoag & Craig, Cassella, Mood Graybill & Boes, and Mukhopadhyay, and this book is clearly the easiest to read from simple distributions to moment generating functions, to transformations, etc.

After you read this, you are going to wish the author had part II covering inferential methods.

Ghahramani has manged to find the *perfect* balance!!
You won't believe what Ghahramani has accomplished until you BUY THIS BOOK (2nd Edition)!!

Instead of trying to intimidate the student with formulas from the abyss and his superior intellect, Ghahramani matter-of-factly shows you what needs to be seen, backs it up with *excellent* examples and side remarks, provides the opportunity to complete a problem set, and moves on quickly to the next section.

What we have here is the model of efficiency: Enough substance to retain clarity without being prolix.

The axiomatic presentation of probability allows the reader to develop the mental framework needed to have a *deep* and *comprehensive* grasp of probability that can truly be called understanding...

One of the reviewers here makes some pretty bizarre claims; So for the record: I am not a Graduate Student.. actually, I am an undergraduate Aerospace Engineering Major.

Ghahramani's English is Superior -- His sentences flow easily and makes for a quick read. If you cannot learn from this book, it is because you are either Illiterate, Lazy, or Inept at BASIC math.

There is *NO* Calculus until Chapter 6! Furthermore, nearly all the symbols used in the book are actually defined and illustrated in the beginning sections of the book.

This Book is Clear, Concise, and Well-Balanced. It is not filled to the brim with overly descriptive paragraphs that emphasize all the wrong things. Gharamani knows exactly where to place the emphasis so that you dont have to spend hours trying to figure out what is important and what is not. This is THE book to have for "Mathematical Statistics 1" or "Intro to Probability".

"An Excellent Book"
Saeed Ghahramani does a fine job with this text. This is a great text for those seeking a guided introduction to the fundamental elements of probability. I am a undergraduate mathematics major & find the material in this text pleasing to read and full of examples that aide in the learning process. This text reminds me of the Springer series of mathematics texts (with it's page style, fonts, & chapters). But one peace of advice, do not read through this book quickly. Take your time and do as many examples as possible (the odd numbered review questions are answered in the back so you may check your work).

Amazing Piece of Work!
Upon opening this book, I questioned its content. I said to myself blissfully "Is this going to be a good book about probability?" After reading some of these reviews I questioned this books style. After all, Saeed Ghahramani isn't one of the best writers on mathematics out there.

This book however, is amazing. It is a piece of art. With every page, I am given a new challenging problem. Each problem is for the most advanced mind who is willing to go beyond their capabilities. Every example is great in itself, and expands the readers mind as it did to me. Let's put it this way. I was a C+ sta