Book Description
This Schaum's Study Guide is the perfect tool for getting a handle on statistics. Fully stocked with solved problemsÑ508 of themÑit shows you how to work problems that may not have been fully explained in class. Plus you get 694 additional problems to use for practice, with answers at the back of the book. Ideal for independent study, brushup before exams, or preparation for professional tests, this Schaum's guide is clear, complete, and well-organized. It even prepares you for computer solutions of statistical problems, fully explaining the use of Minitab, the most popular statistical software. It's the perfect supplement for any course in statistics, and a super helper for the math-challenged.
Customer Reviews:
Effective refresher ... Efficient reference.......2007-01-07
I recommend this text to financial professionals I teach ... VERY helpful to clear the cobwebs several years after college!
Great for more advanced courses...........2006-12-12
I bought this book as a study aid for my Elementary Statistics class. Although the book does give you many examples to help solve, it gets a little too complicated for a beginning statistics class. It became hard to sort through the things I needed to gain a basic understanding of the fundamentals of statistics. If you are needing this book for your job or a more advanced statistics course, I would recommend it, but it's one for the beginner to leave alone.
Comprehensive Guide to Statistics.......2006-06-13
This excellent book provides a comprehensive look at statistical methods. It's a great reference guide with 19 chapters, each of which build nicely on one another.
Chapters 1 through 11 lay the foundation of statistical study and the reader should benefit greatly from this framework.
Specifically, Chapters 1 through 3 cover Variables and Graphs, Frequency Distributions and Mean-Median-Mode concepts. Chapters 4 through 6 move on to cover Standard Deviation, Skewness and Elementary Probability Theory. Chapter 7 then discusses Binomial, Normal and Poisson Distributions.
Chapters 8, 9, 10 and 11 delve into Sampling, Statistical Estimation, Statistical Decision and Small Sampling Theory.
The remaining chapters offer practical insights into such topics as Chi-Square test, Correlation Theory, Analysis of Variance (ANOVA), Time Series Analysis and Statistical Process Control. Much of this appears in the CFA curriculum and therefore is a worthy supplemental study.
True to its format, this Schaum's book helps readers via its question and answer format in each of its chapters. Going through these problems teaches and reinforces concepts taught in the first pages of each chapter in the book.
Great job authors! I'm hoping others will benefit from this book too.
Schaum's Outline of Statistics.......2006-03-03
This is a handy desk guide to statistics for someone who needs a statistical referemce as part of their job but is not a statistician, ie, performance management, data preparation or other information analysis. I compared this book to many others and found it the most comprehensive.
A Failed Attempt .......2005-01-12
This book is sloppily edited, with numerous typos in the equations. Page 91 has two symbolic errors plus a text error.
Overbar omitted twice on page 19 giving a nonsensical formula Z = (X - X)/s
Book Description
This well-respected introduction to statistics and statistical theory covers data processing, probability and random variables, utility and descriptive statistics, computation of Bayes strategies, models, testing hypotheses and much more. Clearly written, brief, well-indexed and abundantly equipped with exercise material.
Customer Reviews:
The classic.......2004-01-07
This book is a classic in the field of decision theory. In other books I have on the topic the names chernoff and moses crop up repeatedly. Given that it was published first in the 50's and is still around should tell you of its longevitity based on quality and its audience: this is a fairly elementary book, built around learing statistics to learn some decision theory. In the words of the authors: today's statistician will be more likely to say that statistics is concerned with decision making in the face of uncertainty. Hence you get to kill two birds with one stone in essence: some statistics + decision theory.
Chapters of the book include
* Introduction
* Data processing- Representation, histograms, polygons, mean, variance, std dev
* Intro to probability and random variables: probability distributions, probability density functions, ramdom samples, normal populations, sets, review of probabilty
* Utility and descriptive statistics
* Uncertainty due to the uncertainty state of nature: bayes, minimax, regret, convex sets
* The computation of Bayes strategies: Bayes Theorem applied!
* Introduction to classical statistics: hypotheses testing, estimation, confidence intervals, signficance testing
* Models: models of probability and utility, models of a set of available actions, models of a set of possible states of nature, models of regret functions, models of experiements, models of the set of available strategies
* Testing hypotheses
* Estimation and confidence intervals
* Tables- logs, random deviates, normal, chi-square, exponential distribution, t-distribution
* Derivations
*Miscellaneous remarks about game theory and utility theory
Every chapter has a dot point summary too (very cool)!
Personally I find learning something with a purpose is easier then learning something that has no purpose. So this type of book appeals to me: it has reason and application, not just a book of theory that says a lot about nothing useful (other than in and of itself). The language of the text is very accessible (these guys can actually write unlike most of their contemporaries). This book teaches by theory and example: the right way to do it (probably because the book was derived from statistics lectures as Stanford university). The mathematics involved isn't overly difficult: strictly high school for first 6 chapters! But the content is clear and succinct.
So in all, a most excellent book...and look at the price! This is seriously good value. Sure it might be a little old and might have not have the latest and greatest ideas, but those later and greater ideas are built on a basis that comes through understanding the fundementals. And who better to learn them from these Chernoff and Moses (I'll resist the temptation to make a joke about the righteousness of the book based on one author's name). It's hard to fault this book: perhas that not all the answers to all the questions are given, usually about 1 in every two or three. And the solutions aren't worked either. This is a small price to pay: hey, learn it well and you don't need the answers anyway!
So go buy it if your interested in decision theory or just want an intro to stats and some useful application!
Book Description
Here is an overview of modern computational stabilization methods for linear inversion, with applications to a variety of problems in audio processing, medical imaging, seismology, astronomy, and other areas. Rank-deficient problems involve matrices that are exactly or nearly rank deficient. Such problems often arise in connection with noise suppression and other problems where the goal is to suppress unwanted disturbances of given measurements. Discrete ill-posed problems arise in connection with the numerical treatment of inverse problems, where one typically wants to compute information about interior properties using exterior measurements. Examples of inverse problems are image restoration and tomography, where one needs to improve blurred images or reconstruct pictures from raw data. This book describes new and existing numerical methods for the analysis and solution of rank-deficient and discrete ill-posed problems. The emphasis is on insight into the stabilizing properties of the algorithms and the efficiency and reliability of the computations.
Book Description
Explores concept of probability, surveys rules for addition and multiplication of probabilities, conditional probability, total probability, Bayes formula, Bernoulli's scheme, random variables, the Chebychev inequality, distribution curves, and the means by which an event is declared to be in practice impossible.
Customer Reviews:
An illuminating introduction to probability theory........2004-10-26
This brief text, which was written for high school students in the Soviet Union following World War II, is an illuminating introduction to probability theory that does not require a foundation in calculus. The authors develop the theory by generalizing from examples, most of which are taken from military or industrial applications. This gives the reader insight into how mathematicians develop theorems by abstracting from problems arising in the real world. The theorems are proved rigorously except in the final chapter on normal distributions. Formal proofs about normal distributions require advanced mathematics not familiar to the intended audience.
Probability theory is developed in the first section of the text. The authors define probability. They explain the addition rule and how it simplifies when events are mutually exclusive. Likewise, after they obtain the multiplication rule in terms of conditional probabilities, they explain how it simplifies when events are mutually independent. The authors discuss Bayes' formula for the probability of a hypothesis given that a given event has been observed using several examples. They then prove Bernoulli's formula for the most probable number of occurrences of an event when there are a large number of trials.
The second section of the text is on random variables. The authors discuss laws of distribution, mean values, variance and standard of deviation, and how these quantities are used to measure the dispersion of a random variable. Their development culminates in Chebyshev's law of large numbers. In the final chapter on normal distributions, the authors informally discuss their properties and show how they can be used to solve problems.
In a brief conclusion, the authors discuss other developments in probability theory that are beyond the scope of this text.
This text is an excellent introduction to probability theory. I recommend it highly for the insights it offers. However, it does not contain exercises. To learn mathematics, one must solve problems. Therefore, I suggest that you read this text in conjunction with a problem book on probability or a text on probability that does contain exercises such as Samuel Goldberg's Probability: An Introduction.
Old and useful, but get something else.......2001-12-04
Even though the fifth edition of this book was published in 1961, this book still gives a useful and brief introduction to probability. However, if you're going to buy a book to learn statistics or probability, I would suggest a more recent book. They may not be as brief and concise as this, but newer books would be more up to date. One interesting thing about the Gnedenko/Khinchin book is their examples and problems, which involve things such as the production of artillery shells, or hitting targets with cannons. It is a very welcome change from the traditional, but obvious examples that use decks of cards or dice. Also, it says things about the audience for whom this book was originally meant, and the relevant topics of the time of the cold war. Still, I suggest a more recent text.
Book Description
Bayesian analyses have made important inroads in modern clinical research due, in part, to the incorporation of the traditional tools of noninformative priors as well as the modern innovations of adaptive randomization and predictive power. Presenting an introductory perspective to modern Bayesian procedures, Elementary Bayesian Biostatistics explores Bayesian principles and illustrates their application to healthcare research. Building on the basics of classic biostatistics and algebra, this easy-to-read book provides a clear overview of the subject. It focuses on the history and mathematical foundation of Bayesian procedures, before discussing their implementation in healthcare research from first principles. The author also elaborates on the current controversies between Bayesian and frequentist biostatisticians. The book concludes with recommendations for Bayesians to improve their standing in the clinical trials community. Calculus derivations are relegated to the appendices so as not to overly complicate the main text. As Bayesian methods gain more acceptance in healthcare, it is necessary for clinical scientists to understand Bayesian principles. Applying Bayesian analyses to modern healthcare research issues, this lucid introduction helps readers make the correct choices in the development of clinical research programs.
Average customer rating:
- definitely not for the faint of heart...
- An Excellent Intro
|
Elementary Signal Detection Theory
Thomas D. Wickens
Manufacturer: Oxford University Press, USA
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Detection Theory: A User's Guide
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A Primer of Signal Detection Theory
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Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
ASIN: 0195092503 |
Book Description
Signal detection theory, as developed in electrical engineering and based on statistical decision theory, was first applied to human sensory discrimination about 40 years ago. The theory's intent was to explain how humans discriminate and how we might use reliable measures to quantify this ability. An interesting finding of this work is that decisions are involved even in the simplest of discrimination tasks--say, determining whether or not a sound has been heard (a yes-no decision). Detection theory has been applied to a host of varied problems (for example, measuring the accuracy of diagnostic systems, survey research, reliability of lie detection tests) and extends far beyond the detection of signals. This book is a primer on signal detection theory, useful for both undergraduates and graduate students.
Customer Reviews:
definitely not for the faint of heart..........2006-03-04
Although the first chapter is simply written, the rest of the book is not. It doesn't explain the information it presents in an understandable way. Yes, there are problems at the end of the chapters to help facilitate understanding. But there are no answers to the questions! So you cannot check yourself on how you do on the problems.
If you want to learn about SDT, read "Detection Theory: A User's Guide" by Macmillan and Creelman instead. Much easier to understand. Problems at the end of the chapters. AND answers to them!
An Excellent Intro.......2003-09-04
For people who have feared signal detection theory or just don't get why it is so popular among psychophysicists, this book has the answers. It is easy to read and the exercises at the end of each chapter help to solidify the concepts. The reader is left with a good understanding of signal detection and its many applications.
Book Description
This is an introductory textbook on probability theory and its applications. Basic concepts such as probability measure, random variable, distribution, and expectation are fully treated without technical complications. Both the discrete and continuous cases are covered, the elements of calculus being used in the latter case. The emphasis is on essential probabilistic reasoning, amply motivated, explained, and illustrated with a large number of carefully selected examples. Special topics include combinatorial problems, urn schemes, Poisson processes, random walks, genetic models, and Markov chains. Problems with solutions are provided at the end of each chapter. Its easy style and full discussion make this a useful text not only for mathematics and statistics majors, but also for students in engineering and physical, biological, and social sciences. This edition adds two new chapters covering applications to mathematical finance. Elements of modern portfolio and option theories are presented in a detailed and rigorous manner. The approach distinguishes this text from other more mathematically advanced treatises or more technical manuals. Kai Lai Chung is Professor Emeritus of Mathematics at Stanford University. Farid AitSahlia is a Senior Scientist with DemandTec, where he develops econometric and optimization methods for demand-based pricing models. He is also a visiting scholar in the department of statistics at Stanford University, where he obtained his Ph.D.in operations research.
Customer Reviews:
best textbook for elementary probability theory.......2005-04-08
As a professor in computer science, I am teaching a seminar course in which I wanted to cover basic probability theory in a week. I read at least a half dozen textbooks in the university library and found this book to be far better than others for my purpose. In particular, the material I used was the derivation from the binomial distribution (a coin toss) to the normal and the Poisson distributions, which I covered in two classes. Students liked the many interesting, real-life examples in the book. In addition, I taught the two proofs for the law of large numbers. The second one from Chebyshev was more powerful (applies to non-identical distributions), stronger (guaratees the speed of convergence), simpler and shorter (half a page with no need of mathematical analysis). It eclipsed the theories of other mathematicians in the previous 200 years. The Chebyshev's theorem was new to me and to all the people I mentioned this to.
Of the books I know, this is the best entry level textbook for probability theories. I did not read the chapters on mathematical finance.
Great thoughts in every page.......2005-03-18
I just read the review by another reader, I would say unfortunately he was wrong. This book is one of the greatest probability book I have ever seen. If you want high-school level combination problem, this book is not for you. But if you want the essence of probability theory, this will be the perfect book for the entry. Actually I'm annoied by the comments of the other reviewer. I think he needs to review himself if he is not competent enough to take such course.
painful... even by undergraduate math textbook standards.......2003-07-02
I remember this ghastly nightmare from my undergraduate days. It was the only math textbook that I really struggled with. Part of that was probably due to having an inordinately lousy professor, but part of it is because the book reads more like a quick review for people who already know the subject matter than as an actual tool for learning.
As a contrast, check out what people are saying about "A Book of Abstract Algebra" by Pinter -- they're right, THAT is everything a math textbook should be. My class never quite finished it, but I had no trouble reading the later chapters on my own. I still have a copy of Chung's book, but it only has one remotely interesting thing in it that I remember, which was Laplace's calculation of the probability that the sun will rise tomorrow.
Bottom line: if you're unfortunate enough to end up with a professor who is still using Chung's book (I used it in 1997) ... run!
Average customer rating:
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Schaum's Easy Outline: Statistics
Murray R Spiegel , and
David P. Lindstrom
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ASIN: 0070527121 |
Book Description
Boiled-down essentials of the top-selling Schaum's Outline series for the student with limited time
What could be better than the bestselling Schaum's Outline series? For students looking for a quick nuts-and-bolts overview, it would have to be Schaum's Easy Outline series. Every book in this series is a pared-down, simplified, and tightly focused version of its predecessor. With an emphasis on clarity and brevity, each new title features a streamlined and updated format and the absolute essence of the subject, presented in a concise and readily understandable form.
Graphic elements such as sidebars, reader-alert icons, and boxed highlights stress selected points from the text, illuminate keys to learning, and give students quick pointers to the essentials.
- Designed to appeal to underprepared students and readers turned off by dense text
- Cartoons, sidebars, icons, and other graphic pointers get the material across fast
- Concise text focuses on the essence of the subject
- Delivers expert help from teachers who are authorities in their fields
- Perfect for last-minute test preparation
- So small and light that they fit in a backpack!
Customer Reviews:
pathbreaking.......2005-01-12
Nelson develops a new approach to probability theory
that is just as powerful as but much simpler than conventional
"Kolmogorov-style" probability theory used throughout mathematics
for most of the 20th century. This book has two "radical" innovations.
The first innovation is a very simple version of nonstandard analysis,
much simpler than Abraham Robinson's original version, developed in only eight
pages, just powerful enough to define the arithmetic of infinitesimal
and unlimited real numbers.
The second innovation is a very simple version of probability theory
that restricts all sample spaces to be finite (though perhaps unlimited)
and all probabilities of outcomes to be nonzero (though perhaps infinitesimal).
Thus there is no need for measure theory or any other PhD level mathematics.
As Nelson says in his preface, "the mathematical background required is little
more than that which is taught in high school, and it is my hope that it will
make deep results from the modern theory of stochastic processes readily
available to anyone who can add, multiply, and reason." The level
of sophistication required of readers is that of undergraduate math majors.
The reduction of technical difficulty can be seen by his going from nothing to
"a version of the de Moivre-Laplace central limit theorem that
contains Lindeberg's theorem on the sufficiency of his condition, Feller's
theorem on its necessity, Wiener's theorem on the continuity of the
trajectories of his process, the Levy-Doob characterization if it as the
only normalized martingale with continuous trajectories, and the invariance
principle of Erdos and Kac as extended by Donsker and Prohorov" in just 79
pages.
This book will be of no use to anyone who just wants to learn conventional
probability theory. But it is essential for anyone who wants a
perspective from outside conventional theory. Anyone brainwashed by
a PhD level probability course has a hard time seeing that probability
theory can be any other way (although it was very different in the 19th
century and many different alternative approaches were tried in the 20th).
Nelson's book is an eye opener.
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Schaum's Outline of Probability, Random Variables, and Random Processes
Schaum's Outline of Probability, 2nd Edition
Schaum's Outline of Calculus (Fourth Edition)
Schaum's Outline of Statistics and Econometrics
