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Elements of Statistical Computing

statistics. Included are discussions of numerical analysis, numerical integration, and smoothing. The author give special attention to floating;

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Expansions and Asymptotics for Statistics

numerical methods, becomes a powerful way to acquire a deeper understanding of the techniques used in probability and statistics.;

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Numerical Analysis For Statisticians

Numerical analysis is the study of computation and its accuracy, stability and often its implementation on a computer. This book focuses on;

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Numerical and Statistical Methods

Mathematics and statistics with the free software SCILAB (http://www-rocq.inria.fr/scilab/);...

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Numerical Ecology

The book describes and discusses the numerical methods which are successfully being used for analysing ecological data, using a clear and;

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Numerical Methods of Curve Fitting

portions of both statistics and numerical analysis. Originally intended as an introduction to the topic aimed at students and graduates in physics;

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Statistical Computing with R

Computational statistics and statistical computing are two areas that employ computational, graphical, and numerical approaches to solve;

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Statistics and Analysis of Scientific Data

Statistics and Analysis of Scientific Data covers the foundations of probability theory and statistics, and a number of numerical and;

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Statistics in Musicology

analysis, music theory, and performance theory. It explores concrete methods for data generation and numerical encoding of musical data and serves;

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Numerical Methods for Special Functions

provides an up-to-date overview of numerical methods for computing special functions and discusses when to use these methods depending on the;

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Numerical Methods Using Java

Implement numerical algorithms in Java using NM Dev, an object-oriented and high-performance programming library for mathematics.You'll see;

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Numerical Methods Using Java

Implement numerical algorithms in Java using NM Dev, an object-oriented and high-performance programming library for mathematics.Yoüll see;

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Statistics and Analysis of Scientific Data

Statistics and Analysis of Scientific Data covers the foundations of probability theory and statistics, and a number of numerical;

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Introduction to Modeling and Numerical Methods for Biomedical and Chemical Engineers

engineering problems into a numerical representation using scientific fundamentals. Modeling concepts focus on problems that are directly related to;

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Introduction to Numerical Methods

in higher level mechanical engineering courses, Introduction to Numerical Methods incorporates theory and worked-out engineering-related problems;

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Elementary Bayesian Statistics

statistics. It presents methods to assist in the collection, summary and presentation of numerical data.Bayesian statistics are becoming an;

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Numerical Methods of Statistics

the application of mathematical tools to statistical problems. The first half of the book offers a basic background in numerical analysis that;

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Numerical Methods of Statistics

the application of mathematical tools to statistical problems. The first half of the book offers a basic background in numerical analysis that;

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Essentials of Business Statistics (Int'l Ed)

coverage of graphical and numerical methods New graphically based procedures for finding confidence intervals and performing hypothesis tests;

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Transmutations, Singular and Fractional Differential Equations with Applications to Mathematical Physics

applied problems in mathematics, mathematical physics, probability and statistics, applied computer science and numerical methods. In addition to;

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Statistics in Psychology

much-needed introduction is invaluable for any psychology students who 'don't get numbers'. Breaking away from the traditional, numerical;

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Numerical Methods for Chemical Engineering

Suitable for a first year graduate course, this textbook unites the applications of numerical mathematics and scientific computing to the;

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Cambridge Series in Statistical and Probabilistic Mathematics

, medical, and social conclusions from numerical data. The authors draw on many years of experience with practical and research programs and;

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Cambridge Series in Statistical and Probabilistic Mathematics

, medical, and social conclusions from numerical data. The authors draw on many years of experience with practical and research programs and;

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A Dictionary of Statistics for Psychologists

and a numerical example follow a brief written account of a term. In addition, graphical material help make definitions clearer. This unique;

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Resampling Methods for Dependent Data

By giving a detailed account of bootstrap methods and their properties for dependent data, this book provides illustrative numerical;

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Robust Statistics

with fixed and with random predictors; multivariate analysis; generalized linear models; time series; numerical algorithms; and asymptotic;

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