Book Description
Praise for
Financial Modeling with Crystal Ball(r) and Excel(r)
"Professor Charnes's book drives clarity into applied Monte Carlo analysis using examples and tools relevant to real-world finance. The book will prove useful for analysts of all levels and as a supplement to academic courses in multiple disciplines."
-Mark Odermann, Senior Financial Analyst, Microsoft
"Think you really know financial modeling? This is a must-have for power Excel users. Professor Charnes shows how to make more realistic models that result in fewer surprises. Every analyst needs this credibility booster."
-James Franklin, CEO, Decisioneering, Inc.
"This book packs a first-year MBA's worth of financial and business modeling education into a few dozen easy-to-understand examples. Crystal Ball software does the housekeeping, so readers can concentrate on the business decision. A careful reader who works the examples on a computer will master the best general-purpose technology available for working with uncertainty."
-Aaron Brown, Executive Director, Morgan Stanley, author of The Poker Face of Wall Street
"Using Crystal Ball and Excel, John Charnes takes you step by step, demonstrating a conceptual framework that turns static Excel data and financial models into true risk models. I am astonished by the clarity of the text and the hands-on, step-by-step examples using Crystal Ball and Excel; Professor Charnes is a masterful teacher, and this is an absolute gem of a book for the new generation of analyst."
-Brian Watt, Chief Operating Officer, GECC, Inc.
"Financial Modeling with Crystal Ball and Excel is a comprehensive, well-written guide to one of the most useful analysis tools available to professional risk managers and quantitative analysts. This is a must-have book for anyone using Crystal Ball, and anyone wanting an overview of basic risk management concepts."
-Paul Dietz, Manager, Quantitative Analysis, Westar Energy
"John Charnes presents an insightful exploration of techniques for analysis and understanding of risk and uncertainty in business cases. By application of real options theory and Monte Carlo simulation to planning, doors are opened to analysis of what used to be impossible, such as modeling the value today of future project choices."
-Bruce Wallace, Nortel
Customer Reviews:
goes beyond deterministic assumptions.......2007-06-24
The book is all about simulations. In financial modelling, as opposed to engineering or science. Readers from the latter 2 fields who have coded simulations will find much in common. The specific equations in the text for finance are largely different from what you've met before. But the basic treatment is essentially the same.
Typically, the text will describe some financial equation. The Crystal Ball program lets you easily generate random data as input to simulations, which it then runs.
Despite Excel in the book's title, the book is mostly about using Crystal Ball. Charnes shows how you can go well beyond a simple deterministic treatment of an income statement or balance sheet. Typically, most companies just use the deterministic approach. The danger is that this approach relies on certain assumptions. Using Crystal Ball and the book, you can test the effect of relaxing these assumptions on the balance sheet. A more robust approach to financial planning.
Financial Modeling with Crystal Ball and Excel.......2007-05-13
Acho que faltou um pouco mais de detalhes nos tópicos, porém o livro apresenta excelente modelos técnicos.
Average customer rating:
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Antenna and EM Modeling with Matlab
Sergey N. Makarov
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Similar Items:
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Antenna Theory: Analysis and Design, 3rd Edition
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Radar Systems Analysis and Design Using MATLAB Second Edition
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Antenna Engineering Handbook
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Antenna Theory and Design, 2nd Edition
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Antennas
ASIN: 0471218766 |
Book Description
An accessible and practical tool for effective antenna design
Due to the rapid development of wireless communications, the modeling of radiation and scattering is becoming more important in the design of antennas. Consequently, it is increasingly important for antenna designers and students of antenna design to have a comprehensive simulation tool.
Sergey Makarov's text utilizes the widely used Matlab(r) software, which offers a more flexible and affordable alternative to other antenna and electromagnetic modeling tools currently available. After providing the basic background in electromagnetic theory necessary to utilize the software, the author describes the benefits and many practical uses of the Matlab package. The text demonstrates how Matlab solves basic radiation/scattering antenna problems in structures that range from simple dipoles to patch antennas and patch antenna arrays. Specialized antenna types like fractal antennas and frequency selective surfaces are considered as well. Finally, the text introduces Matlab applications to more advanced problems such as broadband and loaded antennas, UWB pulse antennas, and microstrip antenna arrays.
For students and professionals in the field of antenna design, Antenna and EM Modeling with Matlab:
* Strikes an important balance between text and programming manual
* Provides numerous examples on how to calculate important antenna/target parameters
* Provides means for modifying existing codes for various individual projects
* Includes a CD-ROM with Matlab codes and antenna geometry files
The present MATLAB codes are only supported by MATLAB 5 and 6 (up to 2004).
Customer Reviews:
Very Good Book.......2002-11-21
The book is well written but not very extensive. It points to the applications right from the beggining and it is certainly of great value for students and engineers already engagged with Balanis Book on antenna theory. I strongly recommend Makarov's book for training students with a strong numerical methods background on electromagnetics, as a prerequisite before taking the course.
Book Description
Agent-based computational modeling is changing the face of social science. In Generative Social Science, Joshua Epstein argues that this powerful, novel technique permits the social sciences to meet a fundamentally new standard of explanation, in which one "grows" the phenomenon of interest in an artificial society of interacting agents: heterogeneous, boundedly rational actors, represented as mathematical or software objects. After elaborating this notion of generative explanation in a pair of overarching foundational chapters, Epstein illustrates it with examples chosen from such far-flung fields as archaeology, civil conflict, the evolution of norms, epidemiology, retirement economics, spatial games, and organizational adaptation. In elegant chapter preludes, he explains how these widely diverse modeling studies support his sweeping case for generative explanation.
This book represents a powerful consolidation of Epstein's interdisciplinary research activities in the decade since the publication of his and Robert Axtell's landmark volume, Growing Artificial Societies. Beautifully illustrated, Generative Social Science includes a CD that contains animated movies of core model runs, and programs allowing users to easily change assumptions and explore models, making it an invaluable text for courses in modeling at all levels.
Customer Reviews:
Annie Wu -- Book #2.......2007-08-10
I am a purchasing agent who buys books for my faculty, and as far as I know, this faculty member is very impressed with this book.
Excellent example of cross-disciplinary social science using theory.......2007-08-07
It's refreshing and exciting, in a quiet intellectual kind of way, to encounter a book that includes philosophy of science, music theory, Anasazi disappearance mysteries, ethnic cleansing, and an explanation of why CEOs exist. Josh has produced the book I've been wanting to read any time during the last 20 years, which have been a bit barren from the theory and modeling perspective in social science. He also makes clear the mathematical and philosophical basis of the agent-based approach, producing a baseline both for future work in the field and for competing paradigms such as systems dynamics, discrete simulations, and cellular automata (Wolfram's New Kind of Science), however incommensurable. I was particularly interested in the occasional use of probability modeling (negative exponential distributions generated through simple rules are a very interesting advance in understanding the waiting times between civil violence outbursts) and I'd love to see a deeper relationship established, say between Bayesian models of dynamic systems and agent-based models. Keep up the great work, Josh! Also, kudos to the publisher for the sheer quality of the book: excellent paper, great color plates, and priced to sell rather than as the work of art it is.
Excellent survey of the author's work.......2007-07-27
This book did a good job of introducing me to the current state of agent-based modeling. It also, perhaps inadvertently, highlighted some of the current weaknesses of the field. In particular, the models shown in each paper rarely shared common features, and there was little consistency in method.
Epstein argues persuasively that agent-based modeling is a tool, not a methodological approach, and you should no sooner expect consistent usage here than with differential calculus. That said, it was a bit disconcerting.
Also, while the goal espoused here was to use the bare minimum of constraints that retain explanatory power, I was disappointed that relevant work from other fields was often abstracted away. For example, a few models used social networks; but the networks presented were static, not dynamic, and were not built around power-law ratios. Such additional complexity may well have distracted from the main point; but it would have been nice to see at least some discussion of why the models were simplified.
Regardless, I was very pleased with the book and would highly recommend it.
A Landmark Publication.......2007-03-08
Josh Epstein's new Opus is a landmark publication in the emerging field of multiagent-based simulation of dynamic social systems. Since Josh is not only one of this still nascent (though burgeoning) field's ablest and most creative practitioners, but also among its most thoughtful critics, the reader of has two treats in store: (1) a generous, and wide-ranging, sampling of case studies (including social networks and evolution, population growth, emergence of economic classes, civil unrest, timing of retirement, the dynamics of adaptive organizations and the spread of infectious disease), and (2) a cogent "meta" discussion of what multiagent models ARE, ARE NOT and how (when their properties and limitations are *not* properly taken account of) they can easily be MISAPPLIED.
Far from suggesting that multiagent-based models are a panacea solution to all (or most) social dynamical systems, Josh's book carefully articulates the conditions for which such an approach IS (and is NOT) appropriate; an approach rarely taken by other, similar, overviews of the field. Indeed, the cogent philosophical discussion in Chapter One - alone! - in which the generativist's position is defined and put into a broader modeling/simulation context, is worth the price of admission; I have not seen a better "manifesto" of multiagent-based modeling elsewhere.
Finally, without taking away any of the inherent "beauty" (in the technical sense) of the often exaggerated concept of "emergence," Josh succeeds admirably in both defining the term, and de-mystifying it, stripping it of some of its unnecessary "quasi-mystical" baggage (at least as it is often portrayed in lay publications).
Anyone who is interested in understanding how agent models may be used to help explore the dynamics of social dynamical systems, should have this book firmly on top of their "must read" list! Josh has generously provided future generations of agent explorers their go-to source of both inspiration and ideas. Well done Josh!
Book Description
"Mr. Salov has taken one of my favorite creations – Perfect Profit – and provided an expanded description of his interpretation of it and put it in your hands with the included software. Like I said fifteen years ago, Perfect Profit is an important tool for the trading system developer. See for yourself."
—Robert Pardo, President, Pardo Capital Limited
"A very in-depth reference for programmers that should serve well into the future. The code herein lends itself well to other syntactically similar programming languages such as Java, PHP, and C#."
—Ralph Vince
The goal of trading is to make money, and for many, profits are the best way to measure that success. Author Valerii Salov knows how to calculate potential profit, and in Modeling Maximum Trading Profits with C++, he outlines an original and thought-provoking approach to trading that will help you do the same.
This detailed guide will show you how to effectively calculate the potential profit in a market under conditions of variable transaction costs, and provide you with the tools needed to compute those values from real prices. You'll be introduced to new notions of s-function, s-matrix, s-interval, and polarities of s-intervals, and discover how they can be used to build the r- and l-algorithms as well as the first and second profit and loss reserve algorithms. Optimal money management techniques are also illustrated throughout the book, so you can make the most informed trading decisions possible.
Filled with in-depth insight and expert advice, Modeling Maximum Trading Profits with C++ contains a comprehensive overview of trading, money management, and C++. A companion CD-ROM is also included to help you test the concepts described throughout the book before you attempt to use them in real-world situations.
Customer Reviews:
should have been a short journal article.......2007-06-29
SUMMARY: A potentially useful concept taken to ridiculous extremes.
The main theme is around the concept of *potential profit* offered by a particular market. When building automated trading strategies or evaluating human trader performance, the problem could be decomposed into a product of some intrinsic profit offered by the market and the percentage of that profit that is captured by a particular trader or strategy. This book spends most of its attention on the former factor and various derived performance metrics.
*Potential profit* is defined by an idealized strategy that has *perfect foresight about all future prices over a given time interval*. In addition to the original concept apparently suggested by Robert Pardo, the book's definition is made more realistic by constraining such a strategy with non-zero transaction costs. Although this connection is not made by the author, the concept is related to those used in offline algorithms and competitive analysis (see, for example, Online Computation and Competitive Analysis): in essence, you want to quantify the amount of *regret* you experience comparing your performance to that of a perfect adversary or algorithm that is allowed to solve the offline version of the same problem (with all of the future data available at the outset).
Three iterations of potential profit algorithms are introduced:
1. "r-/l-algorithms": position size is constrained with a given maximum. The perfect strategy can be proven to be pure reversal -- except for initial and final transactions, the maximum profit is obtained by always switching positions between +max_size and -max_size at certain transaction points. Although the underlying problem could be solved using a generic optimizer, Salov introduces a concept of *s-intervals* that makes it easy to see how the global maximum can be computed with a simple linear algorithm. This algorithm offers conceptual elegance of solving its related problem exactly -- the later two are heuristic improvements for less constrained versions of the problem.
2. "first P&L reserve algorithm": strategy remains a pure reversal strategy but position size is allowed to vary subject only to self-financing/account margin/buying power constraints. #2 can improve on #1 due to increases in account buying power after the initial entry/exit transactions.
3. "second P&L reserve algorithm": same as #2 but the strategy is allowed to change existing positions in between the transaction points used by #1 and #2, i.e. the strategy is no longer pure reversal. Again, #3 can improve on #1 due to increases in account buying power during trading.
Since both #2 and #3 consider how account equity grows over time subject to self-financing restrictions, connections to Kelly/Shannon
maximum growth rate formulas arise naturally. In later chapters, all algorithms are used to derive performance metrics and to compare potential profits offered by various markets using real-life price data.
Unfortunately, in my view the author should have stopped at #1 and not gone on for 180 more pages. Granted, the overall approach of comparing your actual performance to that of some idealized benchmark strategy is extremely sound and useful. As a benchmark, a single simple idealized strategy is sufficient. But the author does not emphasize enough that the single biggest advantage the idealized strategy has is the perfect knowledge of future prices. Algorithm #1 already has this advantage built in and there seems to be little practical value in gleaning further incremental benefits by adding optimal account growth heuristics. (Optimizing position sizes can't be done in real-life without simultaneously controlling risk etc.) In fact, the last chapter has a cocoa contract example making 13000% returns in just 5 days! Similarly, in some examples algorithms #2 and #3 manage to grow exponentially into such large position sizes that they overflow a 32-bit integer -- it is surprising that the author does not see the ridiculousness of that and reluctantly suggests that the algorithms should be applied to "short time intervals" or "with high transaction costs".
Other book shortcomings that seemed glaring to me:
- only deterministic strategies are considered. Real-life strategies have to incorporate uncertainty modeling in their decisioning. Furthermore, deterministic-only strategies can't reach Nash equilibria for some problems.
- only two commission cost models are considered: fixed per transaction and cost as a function of instrument price. Something like cost/share would invalidate much of the discussion.
- because they have perfect price foresight all idealized algorithms unavoidably go wild on max'ing out position sizes. In real life, risk management constraints on open positions can kick in sooner than margin constraints, unless you are diversified across many positions (situation not considered in the book).
- after observing how much the transaction costs can impact these idealized strategies, the author nonetheless goes on and makes very ballpark guesses about slippage etc -- it seems incongruent after all that energy spent on perfecting those strategies.
15 years is not enough: a new market property.......2007-02-25
I am a professional mathematician with a personal interest in mathematical finance and I know the author. I did not expect Dr. Salov to be writing a book but eventually I became one of the first readers. The book focuses on modeling and calculation of the potential profit - a new and fascinating market property. It also contains a gentle introduction explaining basic trading, financial and programming terms and helping better to understand the main topic. Therefore, the book should be interesting for all market participants: trading system developer, trader, theoretician working on finance or someone who wishes to learn the field of trading systems and quantitative finance.
It is interesting that the potential profit, as introduced by Robert Pardo, corresponds to a classical notion of a total variation of a function, the function being the time sequence of prices of a commodity. Computation of the potential profit in the presence of e.g. transaction costs becomes a sophisticated mathematical problem which Dr. Salov solves using the newly suggested r- and l-algorithms. In a systematic way, the author introduces s-function, s-matrix, polarity, s-intervals and proves their properties, producing an effective r- and l-algorithms.
While Robert Pardo introduced potential profit as a new concept, Valerii Salov brings this concept to a substantially higher level. He considers the maximum profit as a market property, which must be combined with a sequence of trading actions - trading strategy. He systematically comes the way from simple market and trading systems performance measures to a powerful and automatic tool filtering the most critical price events. This becomes possible because he takes into account transaction costs such as commissions, slippage, and others. A motivation for each decision leading to the complete software, new algorithms, or money management is carefully explained.
I highly recommend reading this book for anyone interested in development of trading systems and who wants to understand better the work of markets.
Book Description
Theoretical neuroscience provides a quantitative basis for describing what nervous systems do, determining how they function, and uncovering the general principles by which they operate. This text introduces the basic mathematical and computational methods of theoretical neuroscience and presents applications in a variety of areas including vision, sensory-motor integration, development, learning, and memory.
The book is divided into three parts. Part I discusses the relationship between sensory stimuli and neural responses, focusing on the representation of information by the spiking activity of neurons. Part II discusses the modeling of neurons and neural circuits on the basis of cellular and synaptic biophysics. Part III analyzes the role of plasticity in development and learning. An appendix covers the mathematical methods used, and exercises are available on the book's Web site.
Customer Reviews:
Good book for computational neuroscience.......2007-01-28
I am a mathematician and economist interested in how human brain works. To me, (so far) this is the best book using equations to describe the overall picture of brain functions. Even though it might not touch in-depth research topics, I am sure it gives anyone interested in neuroscience very solid foundations on which more advance topics are built. (It actually invites me to more in-depth research topics, such as reinforcement learning, reward-punishment system, etc.)
If math is your familiar language (says, system of differential equations and Bayesian probability), and you are interested to know, in technical details, how the brain functions, this book is for you. Then, I think, you can go into research topics of your interests after finishing reading this book.
"Theoretical Neuroscience" Dry but Informative.......2006-03-23
"Theoretical Neuroscience" is an in-depth introduction to modeling of neural systems from the chemical/electrical processes within neurons, up through small networks of neurons. It is a little dry, but provides a wealth of information on modeling the electrophysical and computational properties of neurons.
Good starting point for undergraduate students.......2005-07-05
This book covers a wide range of different and important subjects of this field and provides by this a good overview to students new in neuroscience. On the other hand side, the topics discussed are not described thoroughly, but stay on the surface. This maybe no big problem for undergraduates who try just to understand the basics but certainly this is not satisfactory for more advanced students or researches.
In my opinion, this book blurs the view of the reader by presenting results about experiments and theoretical models side by side in a way that no fair and solid discussion is provided indicating clearly the limitations and problems of current models. By this, one could get the feeling that the presented models are more than tool to analyse data. However, exactly this is not true for most of the models as can be seen by the fact that these models can also be found in other areas than neuroscience with other interpretations.
Theoretical Neurosciences from a Computational Perspective.......2004-06-11
This text will become a standard course book for Graduate Schools in Computational Neurosciences. You need to know advanced engineering mathematics & probability theory to be able to understand this book. Dayan & Abbott model primary visual cortical, MT, LIP, and Motor cortical neurons as single units, but also as populations (clusters) of firing cells. They discuss Bayes Theorem, probability theory as it applies to the brain, and parietal lobe function as well. They derive all the equations associated with these models for the student so that more advanced parts of the book are comprehensible. The book is not meant to be a general Neuroscience book, but rather a course book about neuronal modeling, computational neurobiology, and neural engineering. It serves these three purposes well. In my opinion, this is the best written account of neuron modeling out there for the graduate student and researcher. Methods in Neuronal Modeling by Christof Koch is the other great book on this subject. If you own these two books you should be able to advance in high level neural modelling. There are numerous equations and formulae of interest throughout each chapter in these two volumes. The price of 39.00 USD for the hardcover is really quite a bargain.
Great textbook and reference.......2003-08-16
This book is certainly the most thorough textbook currently available
on many aspects of computational neuroscience. It works very carefully
through the fundamental assumptions and equations underlying large
tracts of contemporary quantitative analysis in neuroscience. It is
an ideal introductory book for those with a quantitative background,
and is destined to become a standard course book in the field.
Book Description
Computational science is a quickly emerging field at the intersection of the sciences, computer science, and mathematics because much scientific investigation now involves computing as well as theory and experiment. However, limited educational materials exist in this field. Introduction to Computational Science fills this void with a flexible, readable textbook that assumes only a background in high school algebra and enables instructors to follow tailored pathways through the material. It is the first textbook designed specifically for an introductory course in the computational science and engineering curriculum.
The text embraces two major approaches to computational science problems: System dynamics models with their global views of major systems that change with time; and cellular automaton simulations with their local views of how individuals affect individuals. While the text is generic, an extensive author-generated Web-site contains tutorials and files in a variety of software packages to accompany the text.
- Generic software approach in the text
- Web site with tutorials and files in a variety of software packages
- Engaging examples, exercises, and projects that explore science
- Additional, substantial projects for students to develop individually or in teams
- Consistent application of the modeling process
- Quick review questions and answers
- Projects for students to develop individually or in teams
- Reference sections for most modules, as well as a glossary
- Online instructor's manual with a test bank and solutions
Book Description
Research in systems biology requires the collaboration of researchers from diverse backgrounds, including biology, computer science, mathematics, statistics, physics, and biochemistry. These collaborations, necessary because of the enormous breadth of background needed for research in this field, can be hindered by differing understandings of the limitations and applicability of techniques and concerns from different disciplines. This comprehensive introduction and overview of system modeling in biology makes the relevant background material from all pertinent fields accessible to researchers with different backgrounds.
The emerging area of systems level modeling in cellular biology has lacked a critical and thorough overview. This book fills that gap. It is the first to provide the necessary critical comparison of concepts and approaches, with an emphasis on their possible applications. It presents key concepts and their theoretical background, including the concepts of robustness and modularity and their exploitation to study biological systems; the best-known modeling approaches, and their advantages and disadvantages; lessons from the application of mathematical models to the study of cellular biology; and available modeling tools and datasets, along with their computational limitations.
Book Description
From controlling disease outbreaks to predicting heart attacks, dynamic models are increasingly crucial for understanding biological processes. Many universities are starting undergraduate programs in computational biology to introduce students to this rapidly growing field. In Dynamic Models in Biology, the first text on dynamic models specifically written for undergraduate students in the biological sciences, ecologist Stephen Ellner and mathematician John Guckenheimer teach students how to understand, build, and use dynamic models in biology.
Developed from a course taught by Ellner and Guckenheimer at Cornell University, the book is organized around biological applications, with mathematics and computing developed through case studies at the molecular, cellular, and population levels. The authors cover both simple analytic models--the sort usually found in mathematical biology texts--and the complex computational models now used by both biologists and mathematicians.
Linked to a Web site with computer-lab materials and exercises, Dynamic Models in Biology is a major new introduction to dynamic models for students in the biological sciences, mathematics, and engineering.
Customer Reviews:
An excellent recent overview of modeling.......2007-06-14
This is an excellent book for students or faculty interested in learning more about the current state of the art in modeling of biological systems. The authors make a great effort to keep the mathematical sophistication at a level that students (or faculty) who primarily have a biological background will still be able to follow in some detail. They are also able to suggest some of the exciting current areas of research and new areas for the future. All in all, well worth reading if you are interested in the topic of modeling of biological systems.
Book Description
Authors Ita G. G. Kreft and Jan de Leeuw have provided the first accessible and practical guide to using multilevel models in social research. Multilevel approaches are becoming increasingly important in social, behavioral, and educational research and it is clear from recent developments that such models are seen as more realisticùand potentially more revealingùthan ordinary regression models. While other books describe these multilevel models in considerable detail none focus on the practical issues and potential problems of doing multilevel analyses that are covered in Introducing Multilevel Modeling. The authorsÆ approach is user-oriented, keeping formal mathematics and statistics to a minimum. Other key features include the use of worked examples using real data sets, analyzed using the leading computer package for multilevel modeling.
Customer Reviews:
An excellent theoretical introduction to Hierchical Linear Models.......2005-09-10
As a foreword, I am a 2nd year psychology graduate student with ANOVA and multiple regression experience.
That said, I've found this work to be clear, precise, and straight-forward in introducing the logic and concepts behind why one would wish to use a hiearchical linear model, as well as the foundation of said statistical design. The lack of emphasis on heavy math-based calculations will undoubtedly expedite training and use for those in the behavioral sciences. On that same note, formulae are interjected only when necessary and more often clarify than confuse a given point or concept.
The only drawback of this text might be imparting exactly how one performs HLM. There are other books on the market that, though more technical and/or less clear, provide the heavy emphasis on analysis that this book does not quite have.
Even so, I am firmly convinced that there is no better book for HLM in terms of laying out the theoretical foundation. An added bonus for me was the fact that ANOVA and regression are richly elaborated upon--a feature that many graduate students might appreciate as a refresher.
In short, I highly recommend this book.
Good intro Multilevel modeling - uses "Englishlike" language.......2004-12-21
I liked the "comman-man-language" used by the authors to explain Multilevel Modeling. The use of MLn software, which is nearly 50% of the book, was a damper. With the commercial world dominated by SAS and SPSS, I would have liked the authors to give the examples for use with SAS
User friendly!.......2001-05-11
This book is the best intro to the subject that I've seen. The authors minimize the use of notation, mathematics and the like, and invoke the reader's intuition by developing some good, concrete examples. They present those examples as datasets (accessible on the web), as "run" regressions (i.e. with parameter estimates and standard errors), and graphically.
They also demo how to "run" each of the examples on a PC, using the program MLn. If you don't use MLn (and I had never heard of it); this part of the book is less helpful. It would be great if, in an accompanying website perhaps, they were to demo the same analyses using other packages (e.g. SYSTAT, SAS and the like). But this book is really quite good, and a terrific addition to any applied social scientist's library.
reasonable overview of a burgeoning technique.......2000-04-06
When analysing data, the relationships between people that belong in the same classroom, live in the same street or suburb, are part of the same family or therapy group,etc., are often ignored. Multilevel or hierarchical linear modelling is a statistical technique for taking into account such dependencies, arranged in hierarchies (e.g., correlations between students within classrooms, correlations between classrooms within schools, correlations between schools within school districts). In other words, multilevel modeling techniques attempt to model the hierarchical relationships that are found in the real world. In the last 10 years or so there has been a growing number of books and software packages concerned with multilevel analyses. Introducing Multilevel Modeling is shorter and slightly less 'mathematical' than most and gives quite a good introduction to the subject. The book makes reference to the British MLn (MLWiN) computer program in its examples, whereas an introductory text arguably should have used the HLM program, for which a cutdown student version is available free. Taking group dependencies into account is extremely important, but unfortunately many researchers will be discouraged by the dry and heavy-going feel of these texts, which is so often the case with anything involving statistical theory. A highly approachable and readable book remains to be written, but Introducing Multilevel Modeling is probably the best of the current crop.
Books:
- Finite Mathematics and Its Applications
- Foundations of Earth Science (4th Edition)
- Fredholm and Local Spectral Theory, with Applications to Multipliers
- Fundamentals of Algorithmics
- Fuzzy Controller Design: Theory and Applications (Control Engineering (Taylor & Francis))
- Fuzzy-Set Social Science
- Geometry and the Imagination (CHEL/87.H) (AMS Chelsea Publishing)
- Geometry of Differential Forms (Translations of Mathematical Monographs, Vol. 201) (Translations of Mathematical Monographs)
- Geophysical Fluid Dynamics
- Guide to LaTeX (4th Edition) (Tools and Techniques for Computer Typesetting)
Books Index
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