Statistics Books

This is the BEST book for learning statistics for business or a more advanced course in econometrics (i.e., dealing with problems presented by economics data, such as heteroscedasticity and autocorrelation). If you haven't taken a probability and statistics course immediately before the intermediate econometrics course, you MUST get this book and do as many of the exercises as possible. If you're starting a job in consulting, you MUST study this book before you start your job. The text and exercises rely on EXCEL. I've been teaching this stuff for 20 years, and I didn't really learn the "Data Analysis Tools" in EXCEL until to taught a course with this book. (And the author actually answers email!) The rate of return on this book is about 1000%, i.e., for $40 your lifetime earnings will increase over $4000 (not including the time you put into studying EVERY page). Just do it!

business statistics and at the same time learn how to use Excel statistical features, buy this book.

Neufeld's emphasis is very much on using the power of Excel as a "tool" to convey an good understanding of the statistics. And having recently tried to brush up my Statistics using the traditional text book approach...

So, if you're one of those who likes to really understand what's going on you'll like this book.

I'm going back to school and really needed to brush up on my statistics. This book did the trick and I'll use what I learned from it for the rest of my career.

This is one of the best text books in Business Statistics that I have come across which not only is theoretically excellent but also helps you master Excel. I think this author should seriously think of adding some more chapters like Conjoint analysis so that it is helpful to PhD students like me.
On a whole, an excellent text book for all graduate students. A must for all graduate business students.

I think this is one of the best books on this topic available. I have purchased many in an effort to merge statistical knowledge with the Excel application. While Excel is a powerful tool it can be tough to figure out how to harness all that power. It is an expertly written, no-frills book. The author clearly knows how frustrating most of the other Excel books on the market can be so he takes great care to walk you though the steps to success. A one star hit for a few noticeable things missing. One topic I really would like to have seen is creation of boxplots (very tough to do in Excel). A minor issue though because this book is loaded with great infromation and step-by-step approaches.

It is a bit pricey but I think well worth the price. If you can find it second hand then all the better because there is no data or sample disc included. I usually find the lack of a disc for such an expensive book annoying; however, there is a web site for you to download the data files referenced in the book so everything makes sense. As the title suggests the focus is business statistical applications. Bottom line: great book.

Nothing else to say. The best book for actuarial mathematics. Also good for risk managers, in particular for operational risk.

Nothing else to say. The best book for actuarial mathematics. Also good for risk managers, in particular for operational risk. It does not introduce many concepts but rather take to advance level. Excellent concepts that can be applicable in any topic or situation. A must buy in you want to have your grips on acturial mathematics and concepts

When I took a job to model prediction of loss reserves for workers compensation insurance, I began to realize that the traditional statistical methods that I generally relied n would not help me (without modification). The required modification would be either to transform variables or to model long-tailed probability distributions. This is because in the insurance business you have to reserve for those big catastrophies. The cost data for workers compensation data generally show a high frequency of low to moderate costs (say in the range of $1000 to $50,000). However occasionally there are a few cases of severe injury causing permanent disability which could run over 1 million dollars. Even though the probability of occurrence is small the cost is so high that it cannot be ignored. Such claims will surely be found when large insurance company cover millions of employees over many years.

The problem occurs when insuring for floods, earthquakes, fires and other disasters. Stuart Klugman and Bob Hogg in 1984 wrote the first introductory text to acquaint statisticians with such probability models that are important in the insurance business. Other books covering the subject were covered in books on risk theory designed for actuaries. This book covers all the topics and assumes mathematical and staistical knowledge at the level of the book by Hogg and Craig (so some calculus is required).

When I took a job to model prediction of loss reserves for workers compensation insurance, I began to realize that the traditional statistical methods that I generally relied n would not help me (without modification). The required modification would be either to transform variables or to model long-tailed probability distributions. This is because in the insurance business you have to reserve for those big catastrophies. The cost data for workers compensation data generally show a high frequency of low to moderate costs... . However occasionally there are a few cases of sever injury causing permanent disability which could run over 1 million dollars. Even though the probability of occurrence is small the cost is so high that it cannot be ignored. Such claims will surely be found when large insurance company cover millions of employees over many years.
The problem occurs when insuring for floods, earthquakes, fires and other disasters. Stuart Klugman and Bob Hogg in 1984 wrote the first introductory text to acquaint statisticians with such probability models that are important in the insurance business. Other books covering the subject were covered in books on risk theory designed for actuaries. This book covers all the topics and assumes mathematical and staistical knowledge at the level of the book by Hogg and Craig (so some calculus is required).

"Loss Models from Data to Decision" is an excellent book that covers many of the areas of mathematics and statistics that property and casualty insurance (aka general insurance) actuaries are required to know. Topics include: frequency and severity models; aggregate loss models; ruin models; Bayesian statistics; credibility and simulation. The theory is well explained; with worked examples throughout and numerous exercises at the end of each section (these questions are based on past SOA and CAS exam questions, so are directly relevant to people studying for either of these exams). Solutions to the exercises are not provided in this book, but a separate solutions manual is available.

I am a lecturer in Actuarial Studies at an Australian university and set this book for one of my (later-year undergraduate) units. In my opinion, this is the best General Insurance text book available and students whom I have spoken to tell me that they like this book very much, too. I highly recommend this text for all student actuaries.

So far, this is the only book I read every page of during my first semester.

Highly recommended !!! And as said above, having a Professor Thurman as an instructor of the course is a privelege. It really is!

The price is indeed surprising and he mentioned during the class that he agreed writing the book (which is actually the collection of his lecture notes) only if they made sure it was sold at a cheap price.

You'll only enjoy this book!

This book is a must-have for any aspiring MBA student. In fact, it's a must have for any business manager who wants to make real world decisions in the face of uncertainty.

As a student, do you always skip the statistics part when you read a scientific article and only read the end. Well, worry no more. This book really makes you learn.

I have the feeling that this book will be the next best-seller among MBA's after Graham and Dodd's Book on Value Investing.

I still can't believe this book is on the market with this price. It makes me feel I have been robbed when I spent $100 on all other big text books that I barely read a chapter.

I had the priviledge to study statistics with Professor Paul Thurman at Columbia University. I can honestly say he was one of the best professors I encountered at Columbia. He can teach a blind bat how to work on stata, I know because I was the equivilant of a blind bat in statistics. His course helped me learn how to use statistics in a practical manner-- helping me see the importance of stats in every day life. While not everyone has the luxury of having Professor Thurman as their professor, they can now have the next best thing, this book.

I teach MBA students and they often ask about a good intro to statistics. I have looked at many intro level books in the past, and most were either much too basic or assumed too much background knowledge. Thurman's book is really the Goldilocks text -- not too basic, not too complicated, just right for new MBA students.

This is what I wrote in an e-mail to the supplier shortly after the book arrived:

.........I'm absolutely delighted to have this wonderful [even stats can be wonderful sometimes!] book in my hands. It has arrived weeks earlier than I was led to expect.



I note that it has been in your personal collection for many years and that you no longer have a use for it. For my own part, it was my favourite stats book when I was doing psychology in the 1960s and now, having a renewed need to deal with non-parametric stats, I searched but failed to find my old paperback edition of Siegel. There are quite a few badly written/incomprehensible stats books around so it's great to have this old friend back.



Many thanks for your prompt service. Also I much appreciate that the condition of the book is just as you described it if not better.



Best wishes,



Alan



Alan G. Hodgson,

SfCI

This is an excellent first book for nonparametric statistical methods. It is a cookbook, but is a good introduction to the many nonparametric techniques for assessing data. These are oftentimes much better suited for your data than the standard stuff you get in intro to statistics. The book by David J. Sheskin or by Conover should your next book.

In the 1960s Siegel's book was the most popular and the most often cited. This is because except for Fraser it was the only useful test available to researchers. The book was written in a somewhat non-technical manner in order to be accessible to social scientists. At the time it became the standard book for all researchers. Theoretical books such as Hajek and Sidak's "Rank Tests" Came out at the end of the decade and the other good statistical books such as Hollander and Wolfe; Conover,; Lehmann; and Randles and Wolfe didn't come out until the 1970s.

So Siegel's book has historical significance but now the pratitioner and the theorists have many other good books to choose from. The text has been revised many times presumably to keep up with the research advances that have practical use for social scientists.

This is (together with Empirical Methods for Artificial Intelligence by Paul R. Cohen) the best of the statistics books I read.

For a non math major (or stats major) user, this book offers an easy way to have works done quickly. But be cautious, an first-class cookbook does not necessarily yeild a first-class meal on your table.

I found the Allison book well worth the money. Consider it an overview of missing value analysis and multiple imputation (MVA/ MI). It is like seeing the forest, but not really seeing the trees.

By trees, I mean the intricacies of how to deploy MVA and MI through a standard software package. I work in SPSS, which usually has excellent documentation. I find, though, that SPSS's MVA and MI routines were much easier for me to grasp given I had read Allison's book first.

I was almost not so lucky. My university could not get SPSS installed for about a week, giving me time to discover Allison's book. The one-day shipping worked well, even to my relatively remote location.

One of the software packages Allison mentions looks to me like a beta version or a graduate student's thesis. If you know as much about MVA and MI as the software's author, it might work well enough. I, personally, would be more comfortable with something well-established and commercial like SAS or SPSS.

This is a small little volume that has a wonderful writing style showing the author's comfort that comes through in the LOGIC and COHERENCE that is so rare these days. Many authors write so badly about these things, as if to prove how smart and rigorous they are (resulting in a useless document). This writer simply spills the goods about how to handle missing data, and a brief explanation of why. The booklet is designed to help you solve the problem. It is working for me. The strategies are the same as I got in a sit-down meeting with one of our statistics professors (a senior guy who wrote a lot of papers on this subject). Since he had the same idea, this booklet appears to tell us how to carry it out. Great!

Allison spells out all of the steps to multiple imputation - I found it very helpful.

I echo wired weird's comments about this monograph. Allison has written some very useful applied statistics books that often include instructions for implimenting the methods in SAS. He writes very well. The series of Sage monographs is usually of high quality, informative and concise and this one clearly fits that mold. These little and inexpensive paperback monographs are also good reference guides. You can't find anything better for under $20.

Beginning stats students never see the real world of dirty data. They imagine that everyone responds fully to their surveys, and that every experiment yields legible results. Oh, for such a simple world.

Allison deals with the harsh reality of incomplete data sets. The book starts with a brief description of techniques that drop incomplete data from analysis. The large majority of the book, however, discusses ways to fill in the blanks.

The author rightly points out that "imputation", or creating values to replace what's missing, is not to be taken lightly. He gives techniques, each suited to the statistical character of some set of problems, and each matched to some technique for analysis. The mathematical goal is to create proxy values that won't upset the outcome of analysis.

That is quite a bit different from finding values that represent reality. Even though imputation is supposed to be mathematically innocuous, faking experimental data seems almost immoral to me. My data sets are about as dirty as any around. Also, they have the opposite of usual form: instead of a few dozen measurements on large numbers of samples, they have thousands of measurements on relatively few individuals. I have not convinced myself that Allison's manipulations are valid in this case. I would have been grateful for more discussion of techniques for stepping around the dropouts, and for statistically deciding whether I can ignore them.

Still, this book has worthwhile content. It's brief, clear, and informative about a very important topic. I will refer back to it, but maybe not the way the author intended.

That's it. It's the best book for learning modeling in a practical fashion. The learning from this book is practical and you'll learn to build practical and useful models.

This is an excellent book if you want to go deep understanding the true meaning of basic math programs.

If there is anything that I would hold against my favorite Operations Research books - it would be the lack of emphasis on model and structure. Williams' book fills in that gap and is an essential companion to every Math Prog book. It is not a cookbook where one can look up a particular problem and the possible ways to model it. Instead, it takes a systematic and very sensible approach to modeling.
The three chapters on Integer Programming Models are amazingly easy to understand and were a real help during a graduate course in the subject. The huge number of practical examples in Parts 2, 3 and 4 of the book is the real value of the book. I would be hard-pressed for space to describe the range of problems that are modeled in Part 2... Part 3 covers a good deal of discussion on these formulations and Part 4 follows it up with solutions. Though solutions are not discussed in detail, they are a great help for someone who has worked hard through the problems and needs a verification of the solutions.
Another useful section in the book is a chapter on the interpretation of Linear Programming solutions. For a person without a Math Prog background (say, a manager), this kind of material is very useful. In fact, it once served as a good refresher for me in a hurry... and an excellent one at that.
The only sore point is a very limited discussion on nonlinear models.

This is one of the only books I have ever encountered that focuses on the practical aspects of model formulation. This is a frequently overlooked aspect of optimization, but models that are well formulated will often result in superior performance. It is particularly strong in the formulation of mixed-integer problems, with a variety of tips for linearizing variable products and for incorporation of logical constructs. It also shows how to model SOS1 and SOS2 variable types. One other area that I found to be particularly useful was a section covering convexity analysis. This was the only book that I've read that did a good job of explaining the concepts and ramifications of problem convexity. Finally, the examples in the book cover a wide range of practical problems. Most are fairly simple, but do a good job of illustrating important techniques.

I highly recommend this book for linear and mixed-integer modelers. However, if you don't use these types of solvers in your work, the book is less likely to be valuable.

Some books are good for mathematicians, some books are good for managers. This book is different. Williams did a good job to combine both mathematic and application perfective in a single book. Even you have only high school background, this book is readable. For senior researchers or grad students or strong math background person, this book is still enjoyable to recall your fundamental of math modeling. The references are not quite updated, however. Also, this book should added some current optimization tools. Even though the title is model building, not solving, it won't be harmful to have the metaheuristics (only introduction) or KKT.

For this U.S. trained forensic pathologist, anyway, Sir Sydney Smith was until recently not a familiar name. He and his perhaps more famous contemporary Spilsbury were the generation ahead of the "grand old men" with whom we are more familiar - Knight, for example. Smith's career was fascinating, though, coming as it did right on the edge of scientific death investigation. From the perspective of 2008, he made deductions and judgments that would simply never fly either scientifically or under contemporary cross examination. However, at his time he was truly at the top of his profession, and a terrific author to boot. This book should be part of any forensic pathology fellow's reading list, and for those outside the profession, offers unique insights into the pitfalls and pressures of this most fascinating craft.

In this book, the famous British Forensic Pathologist Dr. Sydney Smith narrates with a distinct Scottish charm and in factual manner the way he solved several noteworthy crimes committed in England and in some of its Colonies using his knowledge of Forensic Pathology during the mid 20th Century. Often, he would identify a murderer from a few bones as was once found in a dry well in Egypt where he established in Cairo, its first Forensic Pathology Laboratory. He gives the reader a glimpse of the court-room drama that attorney, Sir Bernard Spilsbury, for the Prosecution and Defence alternatively and Sydney Smith himself opposing Spilsbury produced in the courts of law in Britain. Several B&W photographs add greatly to the narrative of each solved case.

"Mostly Murder" by Sir Sydney Smith, late Professor of Forensic Medicine at the University of Edinburgh is another great true-crime autobiography from the first half of the twentieth century, very similar in its witty, epigrammatic style to British Home Office Pathologist, Professor Keith Simpson's "Forty Years of Murder." Both books are fascinating memoirs of 'mostly murders,' famous and obscure, although not always committed in Great Britain--Sir Sidney went to Egypt during the First World War and stayed on as the Principal Medico-Legal Expert to the Ministry of Justice until he returned to Edinburgh in 1928. He also relates cases from Ceylon, Australia (the Sydney Shark affair), and other far-flung ports of the British Empire.

The author most especially seemed to relish his medico-legal battles with the famous Home Office Pathologist, Sir Bernard Spilsbury. In one of his most interesting trials, Sir Sydney testified on behalf of Sidney Fox, a convicted forger, blackmailer, swindler, and thief who was also accused of murdering his own mother for the insurance money--she died less than an hour before her accidental death policy was due to expire.

Dear old mom was a confederate in most of her son's crimes, but Fox emphatically denied strangling her and setting her hotel room on fire, and Sir Sydney believed him. At least he believed that the con man's mother showed no physical evidence of strangulation. He and the great Spilsbury locked horns over the forensic evidence in court and Sir Sidney's client was condemned to the gallows, but was it for the wrong reason?

The fact that Fox renewed his mother's accidental death policy the day before she died was the evidence that hung him, but was he really guilty of murdering her? Sir Sidney thinks not.

Mordant wit abounds in this book, most especially in the chapter, "Accident, Suicide, or Murder?" Sir Sidney relates the suicide by coal-gas of a plumber from Aberdeen who "connected a tube to the gas-pipe before it entered the meter, and so all the way to the room where his body was found."

We've all heard stories about thrifty Scots, but Aberdonians seem to be a legend even amongst their own countrymen.

"Mostly Murder" contains several gruesome photographs from the author's forensic files, but nothing we haven't already seen on television.

Murder is not funny. Yet this obviously major first book on early forensic science turned out to be a 'snort of laughter' funny book. It's a very wryly written and very wise autobiography, with no backstabbing or self-congratulatory remarks. If anything, Smith was way too modest, in dealing with the many parts of forensic science (which are now dealt with by different departments in police, FBI, etc). He managed to deal with ballistic forensics, stringing a couple of microscopes together while in Eqypt in order to compare bullets and casings. This was way prior to the invention of comparison microscopes that are regularly used even in med school.

The stories he tells are usually not well-known, but he had a good reason for sharing the story because it showed a particular means of solving a crime (or not solving it) using what they had available in forensics during the early 1900's. Smith imagination and ability to 'make do' are something that is badly missed in most sciences today. He certainly lived a very productive and valuable life, and obviously his inventions and unique ideas have been built upon in forensic science. I think he would not be surprised, but would have enjoyed the other newer fields in forensics such as entymology.

This is an older book, found at my university library. Quite frankly, it would be worthwhile to publish again and recommend to the many people who are showing such an interest in forensics due to shows such as CSI. Many of the concepts Smith teaches are still valuable today. If readers cannot buy this book, try to find it at a university libary. It is extremely well-written and enjoyable.

Karen Sadler,
Science Education,
University of Pittsburgh

This book tells of the many interesting cases in his career. If you liked "Quincy ME" or "CSI" you'll love reading true stories about his pioneering work in the first half of the 20th century. He is an outstanding writer as well. This book shows how legal medicine can convict the guilty and exonerate the innocent. Most of these cases deal with murder, and tell how the doctor let the dead bones speak to the living.

Erle Stanley Gardner says a successful practitioner of forensic medicine must not only be outstanding in his field, but most be quick-thinking and keen of mind: a real version of Sherlock Holmes. A good medical expert should search for the truth, not the facts to support a pre-conceived theory; this usually results in a miscarriage of justice; chapter 20 illustrates this.

Page 90 tells of his analysis of the British .303 cartridge. The bullet had an "aluminium tip enclosed in a strong cupro-nickel jacket". This tip often broke off when the bullet entered a body. This could result in a blunt-edged bullet that could tumble in a body and create more damage; in effect, a dum-dum bullet.

On page 152 he says that in the British legal system, expert witnesses are made available to the defendants, and paid when the defendant is without means. This is an improvement over just providing a public defender. "While the life of a scoundrel may not be worth saving, the principles of justice always are."

Sir Sydney Smith writes with a dry, subtle sense of humor, and with understatements. This book cannot be easily summarized, except to say: get it and read it!

Learn continuous time finance from this book: you won't be disappointed. I have read almost all the most famous finance books and I must say that this is by far the best one of them. Although somewhat limited in scope, it is masterfully written: everything is explained clearly and carefully. All statements are rigorously proved. I would say it is suitable both for beginners, having a minimum exposure to measure-theoretic probability and willing to spend some time on it, and for advanced students. Personally, I first read the book as a beginner and found it extremely useful, but even now, that I understand and know most of the material, I find it to be an invaluable reference. The level of mathematical sophistication is quite high, so don't expect anything like Neftci, Baxter and Rennie, Mikosch or Bjork. The level is the same of Duffie, but, while Duffie presents a lot of material and most of the time he doesn't provide proofs and explanations (which, personally, I find irritating), this book is limited to few selected topics, but they are explained at length.
Unfortunately, the perfect finance book has not yet been written (finance professionals seem to be too busy and well paid to write good books), but this one is almost perfect. If you really want to understand quantitative finance, I strongly recommend that you invest a good amount of hours in studying this book. Two good books to acompany this one might be Resnick's book on probability and Steele's book on stochastic calculus.

Nielsen has written a virtually self-contained treatise on the subject. Reading this book was a beautiful learning experience: The author's clarity of thought was striking; the examples made particular points transparent; and the exercises made invaluable contributions to understanding.
The three appendices (on measure and probability, the Lebesque integral, and the heat equation), and the first three chapters make the book as self-contained as is possible.
Synopsis: I do not know of a better book on this subject.

This book is excellent. As anyone interested in this field knows, there is a lot of high-level math. The author has included several appendices which cover the required background, and he only includes proofs that are helpful to overall understanding. All theorems without proofs have references to the standard math texts.

In comparison to other texts, it does not leave many important ideas to intuition like Neftci's book. Baxter & Rennie is better than Neftci, but not as good as Elliot & Kopp or Lamberton & Laperyre. All of the above I have studied to some extent, and Nielsen's book seemed to include all that these did AND to fill in the gaps. This is the first book I have seen to actually define 'numeraire'.

Make no mistake, to truly understand this material one has to make an investment in learning a good amount of math. The texts I recommend for real analysis are Royden (tops among all for ease and clarity) and Folland (more comprehensive, but very well written); for probability I recommend Resnick's new book which includes a good chapter on discrete-time martingales (much more readable than Chung) and the legendary text by Billingsley.

If you are willing to learn about 4 chapters of Royden and keep Resnick at your side, then this is the only book you need. If not, then start with Baxter and Rennie.

I read it with Rudin's"Principle of Mathematical Analysis" & Royden's "Real Analysis" by self-study.In my view,you can replace Lamberton & Lapeyre with it.It is rigorous yet accessible for non-math major who is interested in derivatives pricing but frustrated with the high-level mathematics.Buy it and read it with your patience.It won't make you disappointed.

This is an excellent textbook on financial mathematics. It is quite mathematical, but self contained, clearly and carefully written. The appendices are very well written condensed reviews of basic technical facts. The book also contains discussions of a topics that I've never seen anywhere else, such as "Arbitrage and Admissibility" and "The doubling strategy". As mentioned in the preface, the book is based on a doctoral-level course, and the author clearly had the benefit of a large amount of feedback from students. Reading it, one can't help notice the presence of a very large number of extra remarks and hints, inserted on every page in order to clarify what must have been a denser original text. Finally, I have to mention the excellent editorial work done by Oxford University Press in producing this book, as compared to similar books published by Wiley.

This sender shipped this product for me "rush mail" so I wouldn't miss my assignment. I definitely recommend him.

This book can in on time and as promised, it was "new". No markings or anything.

The book arrived promptly and in stated condition. I would buy from this seller again.

Book was in condition as advertized and arrived quickly. The title makes one wish to delve into it thinking it is a mystery. The mind truly is a mystery and the book was interesting keeping your attention with much informative material that can be used in the future.

"The Psychologist as Detective" is a great book for any undergraduate research methods class.

The authors include several real-life examples to illustrate the material presented. The text itself is wonderfully written. Everything is very clear, but also very concise. Along with the examples (which are well used and helpful), the authors keep the subject readable by including a few witty remarks and observations. The text ranges from the simple steps of the scientific method to the construction of factorial designs and three-way ANOVAs. All of it is broken down to be accessible and interesting enough to keep a student's attention.

What's left is a simple, easy to understand guide on how the experimental method works in psychology.

The book was delivered on time and the quality of the book is great.

This was by far the most useful resource that I found for my dissertation proposal. Maxwell's writing is clear, smart, and informative. The sample proposal in the back of the book was extremely useful as well.

Having acquired a veritable library of qualitative methods books and journal articles, I still felt like I was floundering. This book gave me not only a new and more valuable way of thinking about what I was doing, but a step-by-step action guide, practical, practicable, and with a depth (both explicated and in specific references) that makes me much more confident about eventual orals. Short and extremely sweet!

This is the best book I have found that describes, in simple easy to read language, how to design qualitative research. I enjoyed reading it. The exercises included throughout the book facilitated my learning. I highly recommend it!

This brief, thorough, and engaging introduction to qualitative research design has no equal. Maxwell avoids the superficial and confused paradigm discussions that so many qualitative authors engage in. He takes a pragmatic and rigorous approach that builds on the potential strengths of qualitative research while promoting well-accepted principles of scientific validity and integrity.

The chapter on validity is especially noteworthy, recognizing that "Validity is a goal not a product; it is never something that can be proven or taken for granted" and that "Validity is also relative: It has to be assessed in relationship to the purposes and circumstances of the research, rather than being a context-independent property of methods or conclusions. Finally validity threats are made implausible by evidence not methods; methods are only a way of getting evidence that can help you rule out these threats." Amen.