Book Description
This new book provides a unified, in-depth, readable introduction to the multipredictor regression methods most widely used in biostatistics: linear models for continuous outcomes, logistic models for binary outcomes, the Cox model for right-censored survival times, repeated-measures models for longitudinal and hierarchical outcomes, and generalized linear models for counts and other outcomes.
Treating these topics together takes advantage of all they have in common. The authors point out the many-shared elements in the methods they present for selecting, estimating, checking, and interpreting each of these models. They also show that these regression methods deal with confounding, mediation, and interaction of causal effects in essentially the same way.
The examples, analyzed using Stata, are drawn from the biomedical context but generalize to other areas of application. While a first course in statistics is assumed, a chapter reviewing basic statistical methods is included. Some advanced topics are covered but the presentation remains intuitive. A brief introduction to regression analysis of complex surveys and notes for further reading are provided. For many students and researchers learning to use these methods, this one book may be all they need to conduct and interpret multipredictor regression analyses.
The authors are on the faculty in the Division of Biostatistics, Department of Epidemiology and Biostatistics, University of California, San Francisco, and are authors or co-authors of more than 200 methodological as well as applied papers in the biological and biomedical sciences. The senior author, Charles E. McCulloch, is head of the Division and author of Generalized Linear Mixed Models (2003), Generalized, Linear, and Mixed Models (2000), and Variance Components (1992).
From the reviews:
"This book provides a unified introduction to the regression methods listed in the title...The methods are well illustrated by data drawn from medical studies...A real strength of this book is the careful discussion of issues common to all of the multipredictor methods covered."
Journal of Biopharmaceutical Statistics, 2005
"This book is not just for biostatisticians. It is, in fact, a very good, and relatively nonmathematical, overview of multipredictor regression models. Although the examples are biologically oriented, they are generally easy to understand and follow...I heartily recommend the book"
Technometrics, February 2006
"Overall, the text provides an overview of regression methods that is particularly strong in its breadth of coverage and emphasis on insight in place of mathematical detail. As intended, this well-unified approach should appeal to students who learn conceptually and verbally."
Journal of the American Statistical Association, March 2006
Customer Reviews:
very good book, compact but comprehensive.......2007-05-12
This book covers a wide range of topics in Biostatistics, in a comprehensive, but not overwhelming way. In my opinion this book has the potential of being useful to a broad audience, from Statisticians to other professionals who do health related research.
Excellent book ..........2007-01-09
A very specific book, with a lot of details for a statistitian
Book Description
"This is a first-class book dealing with one of the most important areas of current research in applied statistics…the methods described are widely applicable…the standard of exposition is extremely high."
--Short Book Reviews from the International Statistical Institute
"The new chapters (10-14) improve an already excellent resource for research and instruction. Their content expands the coverage of the book to include models for discrete level-1 outcomes, non-nested level-2 units, incomplete data, and measurement error---all vital topics in contemporary social statistics. In the tradition of the first edition, they are clearly written and make good use of interesting substantive examples to illustrate the methods. Advanced graduate students and social researchers will find the expanded edition immediately useful and pertinent to their research."
--TED GERBER, Sociology, University of Arizona
"Chapter 11 was also exciting reading and shows the versatility of the mixed model with the EM algorithm. There was a new revelation on practically every page. I found the exposition to be extremely clear. It was like being led from one treasure room to another, and all of the gems are inherently useful. These are problems that researchers face everyday, and this chapter gives us an excellent alternative to how we have traditionally handled these problems."
--PAUL SWANK, Houston School of Nursing, University of Texas, Houston
Popular in the
First Edition for its rich, illustrative examples and lucid explanations of the theory and use of hierarchical linear models (HLM), the book has been reorganized into four parts with four completely new chapters. The first two parts, Part I on "The Logic of Hierarchical Linear Modeling" and Part II on "Basic Applications" closely parallel the first nine chapters of the previous edition with significant expansions and technical clarifications, such as:
* An intuitive introductory summary of the basic procedures for estimation and inference used with HLM models that only requires a minimal level of mathematical sophistication in Chapter 3
* New section on multivariate growth models in Chapter 6
* A discussion of research synthesis or meta-analysis applications in Chapter 7
* Data analytic advice on centering of level-1 predictors and new material on plausible value intervals and robust standard estimators
While the first edition confined its attention to continuously distributed outcomes at level 1, this second edition now includes coverage of an array of outcomes types in Part III:
* New Chapter 10 considers applications of hierarchical models in the case of binary outcomes, counted data, ordered categories, and multinomial outcomes using detailed examples to illustrate each case
* New Chapter 11 on latent variable models, including estimating regressions from missing data, estimating regressions when predictors are measured with error, and embedding item response models within the framework of the HLM model
* New introduction to the logic of Bayesian inference with applications to hierarchical data (Chapter 13)
The authors conclude in Part IV with the statistical theory and computations used throughout the book, including univariate models with normal level-1 errors, multivariate linear models, and hierarchical generalized linear models.
Customer Reviews:
pre-req: mid-level stats experience.......2006-07-12
I had taken a class in HLM before, and I bought this book to refresh myself on the details. It takes a good deal of attention to detail and concentration to really get the full measure from this book, although it's all in there. Despite the authors' best efforts, there is a good bit of stats jargon in the book, so a reader who is not familiar might have some difficulty. If you're at a point where learning HLM is a logical next step, you'll be fine and I recommend this book. However, if your over-eager faculty advisor told you to learn HLM, despite your minimal experience in stats, you're better off enrolling in a class or workshop.
Good but sometimes skipping ahead too fast.......2006-03-09
This book gives a detailed description of the use of an advanced method to deal with nested data sets.
At a general level the constructs and ideas are well written and can be followed reasonably easily.
However the mathematics is often written very dense, which makes reading and understanding complex.
My main problem with the book, is that in many of the examples they provide, the given formula's, and data skip rapidly to the solution. Thus it is often not insightfull at all, how the data led to the numerical outcome (and I and several of my colleagues could not reproduce all of the example outcomes). A more extensive discussion and a more step-by-step construction of the examples would have been helpful there.
So in short: Conceptually this book is fine, but for practical use mathematics are too dense, and examples are too hard to follow
Useful, but need solid background in stats.......2004-06-05
This book describes important advances in statistical analysis of social science data, circa 1992. Much of this data has a natural hierarchical grouping. But traditional statistical methods proved inadequate at coping. The biggest drawback was the failure of the assumption of independence. If at the lowest level, Items I1,...,In are grouped into sets J1,...,Jm, where m
To handle this, Hierarchical Linear Models were developed. The book gives a detailed treatment. A very comprehensive discussion. Including the handling of meta-analysis, where we wish to combine results across different studies. Which then involves using empirical Bayesian estimates. This method has also seen important usage in evaluating medical studies, conducted by different researchers on the same topic.
The book also illustrates the essential development of non-trivial computer programs to perform the gruntwork.
You will need a solid background in statistics to find this book useful. At a minimum, a year of statistics at the undergraduate level.
Book Description
Applied Linear Statistical Models 5e is the long established leading authoritative text and reference on statistical modeling, analysis of variance, and the design of experiments. For students in most any discipline where statistical analysis or interpretation is used, ALSM serves as the standard work. The text proceeds through linear and nonlinear regression and modeling for the first half, and through ANOVA and Experimental Design in the second half. All topics are presented in a precise and clear style supported with solved examples, numbered formulae, graphic illustrations, and "Comments" to provide depth and statistical accuracy and precision. Applications used within the text and the hallmark problems, exercises, projects, and case studies are drawn from virtually all disciplines and fields providing motivation for students in virtually any college. The Fifth edition provides an increased use of computing and graphical analysis throughout, without sacrificing concepts or rigor. In general, the 5e uses larger data sets in examples and exercises, and the use of automated software without loss of understanding.
Customer Reviews:
Outstanding Non-Theoretic Linear Models Book, HUGE.......2007-07-15
Second year Ph.D. student in Statistics at Iowa State University
I can't think of a single better non-theoretic linear models book. You need to have at least one semester of undergraduate statistics under your belt to follow this book, but it's useful and readable for everyone else. Undergraduates, graduates, professionals...whoever. Given its non-theoretic approach and extremely clear explanations, it can be read by undergraduates with only a minimal background in statistics, but it is comprehensive enough to be useful to anyone. There is no better linear models reference. The textbook is thick (almost 1400 pages) and covers most linear models topics in great detail including regression, ANOVA, and analysis of covariance. My only disappointment regarding content was the rather slim coverage of random and mixed effects models and GLM's. On a positive note, the book provides excellent coverage of diagnostics and remedial measures, which is very often skimmed over in linear models books. Additionally, it has exceptionally well-written, though fairly brief, coverage of model selection and validation, another topic that is a little lacking in many linear models books.
The explanations and choice of exercises are both well-done. The explanations and examples are both clear and thorough, although I would have definitely preferred to see more graphs. It's the kind of topic where visual illustration greatly increases understanding. Generally, the exercises seem a little bit too easy, especially for graduate students, but they do mix in a few harder problems and they pick good, non-contrived problems.
Whether you want a linear models book for learning purposes or if you just want a reference, this book is an excellent choice.
Emminetly Readable.......2007-04-07
This book was a required text for my Data Analysis course. I am not a stats person and have had only a rudimentary introduction to the subject, so I was surprised to find that this is a very approachable book. It is A TOME, but only because the authors are so thorough in their explanations. If you have seen hypothesis testing and are comfortable with the normal distribution, you will be able to face this book. If you are not, be aware that the exercises in the first chapter refer to the prerequisite material not covered by the book.
After the introductory chapter, the authors gave just the right amount of theory to explain the topic at hand and give extensive footnotes for further information. Lots of graphs and example software output are included, all very helpful. I found the text to be well-organized, with coverage given to explanation and examples of each topic.
My one complaint with the book is that it included no instruction on how to work with software programs to get the desired results, so if you are entirely new to the area and do not know how to use Statistix (which has a thorough and self-explanatory help system), R, Minitab, and SAS (which do not), going will be rough. One of the other reviewers mentioned a SAS guide. You may need it if your professor does not demonstrate software use in class.
a non-stat guy likes this book....worth the money........2007-02-23
This is truly an applied text. If you've had basic stats courses and a you have a competent professor then this text will not "run away" from you with wild references to theory and obscure terminology. The authors are quite deliberate and patient in their explanations when they introduce new terminology OR they feel a review of the terminology/concept is in order. The heft and look of the book is VERY intimidating, but it's just an illusion...since the book is truly applied, the theoretical stuff is kept to a minimum. The example data help to bring this book alive. Now don't get me wrong. I have done lots of outside reading on basic stuff like error, variance, and knowing the difference between a parameter and a statistic to get prepped for this class and it paid off. I will keep this book to refer back to it frequently.
Great reference.......2007-01-11
Thus book is comprehenisve and clear. A must-have for those who frequently to regression analysis.
Awesome book ! .......2007-01-09
This book if not for business majors , engineering students and psycology students.
This is an EXCELLENT book for statistics undergrad/grad and PhD students.
I spent over 10 hours weekly just reading the book every week. Plus my assignments will take another 10 hours . So be prepared for a 20 hr week.
YOU NEED TO TAKE A BASIC STAT / INTRO STAT course before this. If you dont know the meaning of P-values , T-test , F-test , DO NOT TAKE THIS COURSE. This book will not introduce you to those things. Unfortunately many buiness schools ( including top 10 ) dont offer a good intro stat course, so buiness majors jumping in to this course is a wrong idea.
This book is also a "good to own book". The first 15 or so chapters has regression and the second half ( next 15 chapters ) has DOE (design of experiments). GREAT BOOK !
One piece of advice - make sure you learn to use SAS with this course . In real world applications many industries are using SAS. Even if your teacher insists on using R package / splus , YOU MAKE SURE YOU know how to do those things in SAS . There is a SAS student manual with this book, specially written for this book . buy it ISBN - 0-07-302177-6
good luck !
Book Description
"I have never read a book on regression that reflects as broad and profound a grasp of the concepts of statistics as this book does. In every topic John Fox deals with--and he does not avoid the slippery ones--he shows a clarity and depth of understanding that goes beyond anything else I have seen in textbooks and that matches the works of the leading researchers within each field."
--Georges Monette, Department of Mathematics and Statistics, York University
"The selection of examples throughout the book is one of its strengths, as they are generally quite engaging in ''real-world'' interest, and demonstrate the practical use (and limitations) of the statistical methods far better than contrived data. I appreciate the fact that John Fox describes what each example ''means'' in terms of the substantive problem behind the data--students would find this quite useful."
--Michael Friendly, Psychology Department, York University
Aimed at researchers and students who want to use linear models for data analysis, John Fox's book provides an accessible, in-depth treatment of regression analysis, linear models, and closely related methods. Fox incorporates nearly 200 graphs and numerous examples and exercises that employ real data from the social sciences. He begins the book with a concise consideration of the role of statistical data analysis in social research. He next covers graphical methods for examining and transforming data, linear least-squares regression, dummy-variables regression, and analysis of variance. Fox also explores diagnostic methods for discovering whether a linear model fit to data adequately represents the data; extensions to linear least squares, including logit and probit models, time-series regression, nonlinear regression, robust regression, and nonparametric regression; and empirical methods for assessing sampling variation, including the bootstrap and cross-validation. More difficult material is segregated in separate sections and chapters and several appendixes are also included presenting background information. Scholars, professionals, researchers, and students in research methods, evaluation, education, sociology, and psychology will appreciate the enhanced and thorough treatment that regression analysis, linear models, and other related methods have received by author John Fox.
Customer Reviews:
Useful and understandable.......2007-02-19
This book takes an unusual start. It begin with the assumption that regression usually has to the data and illustrates how the assumption can be violated, illustrates why graphical analysis is important for data analysis and, in chapter 4, explains how to "fix" the violations of the requirement to the data, before actually starting to explain regression models. I find this unusual approach very insightful. Moreover, difficult parts are marked with an asterisk and can be left out if this is more convenient for the reader.
Although some math is required, I find this book very understandable throughout due to its focus on application. The book covers linear models and some extensions (for the large part of the book) and also Logit- and Probit models for nominal data (in Chapter 15). Chapter 16 deals with bootstrapping, and the appendices give some introduction to statistical and mathematical requirements that the book poses.
Overall, a good buy for people who apply regressions (as the title says), probably not so much for those who are in statistics, math, or econ.
Not suitable as an introduction to regression analysis.......2006-03-08
While this book is no doubt useful to students with a solid background in math and statistics, I certainly would NOT recommend it as an introduction to regression analysis. The explanations tend to be far too complex and inaccessible for most graduate students in the social sciences. Put it this way: after having spent almost $100 on this book, I've had more success finding useful explanations of some of the book's topics on Google than I have had by reading the book itself.
Useful but disorganized.......2006-02-15
This book is a useful first book in linear regression. Fox covers the basics effectively, and the book nicely complements Fox's guide to Regression in R and S Plus.
My biggest problem with the book is Fox's meandering prose style. Few of his paragraphs start with topic sentences, and the formatting of the text means it's almost impossible to figure out what the take-away points are. (It would have been nice for Fox to reverse-engineer his chapters from the end-of-chapter summaries, which are the most helpful guide to his book, and actually happen to be organized.
For a more organized and more clearly written guide to regression, see Basic Econometrics by Gujarati.
Good but Flawed.......2005-05-19
Dr. Fox has written in a thoughtful original manner. Example, pretty much all regression books starts out with a graph of simple linear regression model statisfying all the strong assumptions that went into it. Dr. Fox starts out by showing graph of data that violates every single assumption. This is the sort of innovative and creative approach that shows what is best about this book. Dr. Fox has a deep conceptual understanding of this material.
The book doesn't get 5 stars becuase a significant flaw. Dr. Fox (or perhaps the publishers) wanted every kind of student to be able to read this book. Both students with advanced and also students with no statistical/mathematical expertise and sophistication. The result is a fragmented text. For instance, the geometrical interpretation of least squares fit is not integrated into the initial discussion (it comes 130 pages later!). If it was integrated then many of the derivations and discussions would be far simpler and intuitive. This sepration allows a student with no linear algebra background to read this text but it also wastes the time of the advanced students who have to wait for the more simpler and intuitive approach.
Get it now!!! Best on the subject........2004-01-16
Dr. Fox makes an excellent contribution to the student community across geographies. The text is an excellent balance between theory and practical applications of the linear regression methodology. The author is extremely clear in explaining not only simple and multiple linear regression, but also topics such as bootstraping, logistic and other regression techniques for non normal response variables. The book do not fall down near your toes: the topics are covered in a depth that is amenable for a PhD student.
It is very interesting also to look at the many side comments and suggested readings that the author introduces many times in the book. I congratulate Dr. Fox for this clear, understandable and easy to follow text.
Book Description
The focus of Linear Statistical Models: An Applied Approach, Second Editon, is on the conceptual, concrete, and applied aspects of model building, data analysis, and interpretaion. Without sacrificing depth and breadth of coverage, Bruce L. Bowerman and Richard T. O'Connell's clear and concise explanantions make the material accessible even to those with limited statistical experience.
Customer Reviews:
a good introductory book.......2000-08-26
I think this book is a good book for those who learnt some basic ideas of statistics, and who wish to improve their understanding in the applied field of statistics. To start this book, some calculus and linear algebra knowledge (one term each is enough)is a prerequisite. This book starts easily, but it'll come to very deep concepts without your noticing it at all.
a good introductory book.......2000-08-26
I think this book is a good book for those who learnt some basic ideas of statistics, and who wish to improve their understanding in the applied field of statistics. To start this book, some calculus and linear algebra knowledge (one term each is enough)is a prerequisite. This book starts easily, but it'll come to very deep concepts without your noticing it at all.
Book Description
This new edition of Applied Linear Statistical Models retains the book's uniquely straightforward writing style and format while providing you with the latest information and knowledge. Updates include developments and methods in partial regression and residual plots, an entirely new introduction to the "Design of Experiments" section that frames and outlines the organization and concepts of design and ANOVA, and more.
Customer Reviews:
Must have reference.......2007-02-16
If you are going to spend money, buy the best. This book is the best and IS the standard. I'd consider this the "Gray's Anatomy" of Applied Linear Statiscal Models (i.e. Design of Experiments, Regression, hypothesis testing).
This book is geared for an entry level masters or 400 level student. If you don't fall into this category, this could be worthwhile, just know you'll need to put more time in to learn the material...or...you could get a book geared toward your level. Vardeman's applied statistics for engineers would be one that comes to mind for subject matter that is geared for knowledge below KNNW's Applied Linear Statistical Models.
Bottom line is that this is a must have in anyone's library who is going to do statistical analysis using linear models. It's one of my (and most of my co-workers) go to books if we need to refresh on a quick method to approach a problem.
All in all, it covers all the basics and for the money is a great applied book.
Cheaper Versions Available.......2007-02-12
This hard-bound text was received in excellent condition and should last for as long as I plan on using it; however, there are cheaper versions (like the international version) that contain exactly the same information (plus additional information about ANOVA designs). I am still happy with my purchase, but if you are low on cash, I would recommend purchasing a different edition of this book.
the author don't know how to express in simple language.......2007-01-16
the author don't know how to express in simple and understandable language, although he know very well in this major. I have already read some other books of this major, it is still confusing me a lot to understand some sentences in this book.
Super ! .......2007-01-09
This book if not for business majors , engineering students and psycology students.
This is an EXCELLENT book for statistics undergrad/grad and PhD students.
I spent over 10 hours weekly just reading the book every week. Plus my assignments will take another 10 hours . So be prepared for a 20 hr week.
YOU NEED TO TAKE A BASIC STAT / INTRO STAT course before this. If you dont know the meaning of P-values , T-test , F-test , DO NOT TAKE THIS COURSE. This book will not introduce you to those things. Unfortunately many buiness schools ( including top 10 ) dont offer a good intro stat course, so buiness majors jumping in to this course is a wrong idea.
This book is also a "good to own book". The first 15 or so chapters has regression and the second half ( next 15 chapters ) has DOE (design of experiments). GREAT BOOK !
One piece of advice - make sure you learn to use SAS with this course . In real world applications many industries are using SAS. Even if your teacher insists on using R package / splus , YOU MAKE SURE YOU know how to do those things in SAS . There is a SAS student manual with this book, specially written for this book . buy it ISBN - 0-07-302177-6
good luck !
Popularly accepted regression text book.......2006-11-06
I bought this book because I needed it for a class, and I have only used it a few times for the class. It's hard to learn stats from a textbook unless you start at the beginning, but this book is useful to accompany a previously-knowledgeable statistics mind seeking to learn more about regression.
Great book, but probably will not help a rookie to self-teach regression.
Book Description
Statistical physics concepts such as stochastic dynamics, short- and long-range correlations, self-similarity and scaling, permit an understanding of the global behavior of economic systems without first having to work out a detailed microscopic description of the system. This pioneering text explores the use of these concepts in the description of financial systems, the dynamic new specialty of econophysics. The authors illustrate the scaling concepts used in probability theory, critical phenomena, and fully-developed turbulent fluids and apply them to financial time series. They also present a new stochastic model that displays several of the statistical properties observed in empirical data. Physicists will find the application of statistical physics concepts to economic systems fascinating. Economists and other financial professionals will benefit from the book's empirical analysis methods and well-formulated theoretical tools that will allow them to describe systems composed of a huge number of interacting subsystems.
Customer Reviews:
Excellent Introduction.......2004-12-01
This book is an excellent introduction to financial analitics for Physicists and also for others. Though a little out dated, but what can you expect from such a fast changing subject?
This is not the first book I have read in this subject, but it is my favorite right now. I could have saved myself a lot of trouble if this would have been the first.
Nevertheless, it should be considered as an intial reference point and not as to expect it to contain all the details. After all it only has 148 pages.
target audience not defined.......2003-09-22
I find the book rather poorly written in the aspect of providing links between statistical physics and its application in economics. As a physicist with a background in stochastic processes, I was looking for an introduction to their applications to economic analysis, complete with examples and discussion of the methods' limitations. The book was somewhat disappointing in this respect. Quite often, in many chapters, the necessary math is explained, then some aspects of how it is manefest in economical data are presented and then the chapter ends, leaving the reader wonder what the specific cases may be and if it is practical to use those methods at all. Above all, there is very little discussion as to what the results actually mean, in economical terms.
I believe the book may be helpful for reseachers active in this field but I would not recommend it as a first introduction to econophysics. For economists, the math may be rather difficult to go through as some of the fundamental concepts are not defined consistently. For physicists with no previous exposure to econophysics, I would prefer to see more economics.
Not bad, considering..........2002-08-13
The book is not bad considering the total lack of existence of intelligible literature in this supposedly vast field.
The content is really a collection of quickie crib-sheets on a sundry of topics with nominally common theme: Finance.
A lot of the actually useful stuff is the author's previously published papers on price-return distributions.
Aside from his own previously published work, he has a good tutorial on the GARCH scheme though with precious little follow up reading resources for delving in deeper (or even sideways).
This book is priced far too high given its content and depth.
Look for a used copy, and do not count on the author to answer questions by email.
First in the new field.......2002-06-05
I found several parts of this book useful while preparing lectures for an introductory econophysics course in Fall, 2001. The discussions of convolutions of distributions, Levy distributions and scaling are well-written and easy to follow. In the brief discussion of the St. Petersburg Paradox I missed a critical discussion of expected utility, which was invented by Bernoullli to 'resolve' that paradox. Spurred by von Neumann and Morgenstern, neo-classical economics relies on the idea of expected utility, which seems empirically to be wrong. The chapter on time correlations is also very readable (although Wiener processes are not 1/f^2 noise!). ARCH and GARCH methods are discussed, saving the student from the pain of reading badly-written papers by mathematically-minded economists, but the chapters on options are too brief with nothing new. The best introduction to options is still the original Black-Scholes paper (excepting their erroneous claim that CAPM and the delta-hedge strategy produce option pricing pdes that agree with each other). Also, it would have been nice to have seen a discussion of CAPM. The discussion of algorithmic complexity left me cold (see my earlier books and papers on nonlinear dynamics), and I would like to have seen a critical discussion of the EMH. These criticisms are ok, though, the gaps leave something for the rest of us to work on.
Physicists Land On Planet Economics.......2001-06-11
SINCE the last decade, physicists have been trying to cope with the issues traditionally approached by economics using their own tools and methodologies. This research has been dubbed 'econophysics'. One reason why this incursion should be welcomed is the failure of mainstream economics to recognise financial systems as complex systems. Take mainstream international finance, for instance. In the most respectable workhorse model--so-called 'new open economy macroeconomics model'--foreign exchange rates always reach some sort of stable equilibrium. To put it bluntly, this means that currencies do not exhibit complex behaviour.
However, financial markets do demonstrate several of the properties that characterise complex systems. What is more, they are highly complex, open systems in which many subunits interact nonlinearly in the presence of feedback and stable governing rules. Earlier attempts to find chaos in financial data, for instance, have been disappointing exactly because the phenomenon is likely to emerge in systems which are only moderately complex. Although it cannot be ruled out that financial markets follow chaotic dynamics, econophysics assumes that asset price dynamics are stochastic processes.
A fundamental commitment of the mainline model of international finance is to theory itself, and not to data. Modelling is devoted to equipping the discipline with an underlying rational behaviour at the individual level. Yet this is at odds with the fact that financial markets are prone to collective 'irrational exuberance'. Instead, econophysics attemps to build up stochastic models that encompass essential features observed in the financial data. Now that the time evolution of many financial markets is continually monitored, it is possible to test the accuracy and predictive power of the developed models using available data. One common objection to such a practice is that it is impossible to perform large-scale experiments in economics that could falsify any given theory. The authors note that this limitation is not specific to economics, but also affects such well developed areas of physics as astrophysics, atmospheric physics, and geophysics. By analogy with the activity in these more established areas, we are able to test and falsify any theories associated with the current available sets of financial data.
Complex systems can sometimes behave in remarkable simple ways. These are reflected in power law distributions and scaling. The authors illustrate these concepts and others, and apply them to the financial time series. The book is thus useful not only for physicists but also for economists and people in the financial world. Some familiarity with probability theory or statistical physics is required, though. Economists dissatisfied with the mainline approach of their discipline will find the book opportune. The others might end up welcoming econophysics as well. After all, economists implicitly see physics as nature's economics. What is then wrong with physicists thinking of economics as social physics?
Average customer rating:
- this explains everything...
- The quanlity is fair
- Applied Linear Regression
- This is the ONE that will get you through advanced stats!
- Best Book Out There
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ASIN: 0256117365 |
Book Description
There are two approaches to undergraduate and graduate courses in linear statistical models and experimental design in applied statistics. One is a two-term sequence focusing on regression followed by ANOVA/Experimental design. Applied Linear Statistical Models serves that market. It is offered in business, economics, statistics, industrial engineering, public health, medicine, and psychology departments in four-year colleges and universities, and graduate schools. Applied Linear Statistical Models is the leading text in the market. It is noted for its quality and clarity, and its authorship is first-rate. The approach used in the text is an applied one, with an emphasis on understanding of concepts and exposition by means of examples. Sufficient theoretical foundations are provided so that applications of regression analysis can be carried out comfortably. The fourth edition has been updated to keep it current with important new developments in regression analysis.
Customer Reviews:
this explains everything..........2007-01-18
It's the only book in our library that (a) doesn't assume that you know everything already, and (b) doesn't assume that you want to skip over any of the details. Adding in an excess of examples it makes for a very long book, but when you find yourself needing to decypher someone else's statistical software (like I did) it's a life-saver.
The quanlity is fair.......2006-08-30
The quanlity is fair. the cover is somewhat broken.
someone split drink in the first few pages.
Applied Linear Regression.......2005-10-12
An extremely comprehensive and well written textbook. Very useful as a reference on linear statistical models but at 1400 pages not a straightforward read. Each section is supported by a number of well explained examples.I would strongly recommend this book as a reference but there are a number of alternatives which I prefer in terms of readability and accessibility of the material.
This is the ONE that will get you through advanced stats!.......2004-11-07
I cannot recommend this book highly enough, especially if one is struggling through a grad level stats course. I spent days looking through texts and reading material on the web, this is the book that is detailed enough and explanatory enough so that I finally understood what was going on with statistics. I went from confused about stats to enjoying them, primarily due to this book. I have it tabbed and write all my class notes in this one... this is a keeper. I am looking forward to the fifth edition if they ever do one.... this text covers regression, logistic regression, correlation, anova and study design (actually covers the material for 3 different stats programs in this one text)--- over 1300 pages and all worth reading!!!(reading hint for the confused.... pay little attention to the reams of formulae and lots of attention to the editorial comments, explanations and discussions that accompany the formulae) Update: This text is now in its 5th edition. The author list has changed slightly (Kutner, Nachtsheim, Neter, Li), and the book now has a CD rom with it (isbn 0072386886).
Best Book Out There.......2003-12-19
I have used this book when it was in its first edition and only by Neter and Wasserman. An absolute must for those who use linear models (regression and ANOVA) and want the most exhaustive book out there. This book is one of the rare texts that is also extremely well written. You can not go wrong reading it and studying it on your own. If you can take only one statistics book with you, this is the one. I use the 4th edition so much, I wore my first one out and am now on the second.
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