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Handbook Of Fitting Statistical Distributions With R

RRP $411.99

With the development of new fitting methods, their increased use in applications, and improved computer languages, the fitting of statistical distributions to data has come a long way since the introduction of the generalized lambda distribution (GLD) in 1969. Handbook of Fitting Statistical Distributions with R presents the latest and best methods, algorithms, and computations for fitting distributions to data. It also provides in-depth coverage of cutting-edge applications.

The book begins with commentary by three GLD pioneers: John S. Ramberg, Bruce Schmeiser, and Pandu R. Tadikamalla. These leaders of the field give their perspectives on the development of the GLD. The book then covers GLD methodology and Johnson, kappa, and response modeling methodology fitting systems. It also describes recent additions to GLD and generalized bootstrap methods as well as a new approach to goodness-of-fit assessment. The final group of chapters explores real-world applications in agriculture, reliability estimation, hurricanes/typhoons/cyclones, hail storms, water systems, insurance and inventory management, and materials science. The applications in these chapters complement others in the book that deal with competitive bidding, medicine, biology, meteorology, bioassays, economics, quality management, engineering, control, and planning.

New results in the field have generated a rich array of methods for practitioners. Making sense of this extensive growth, this comprehensive and authoritative handbook improves your understanding of the methodology and applications of fitting statistical distributions. The accompanying CD-ROM includes the R programs used for many of the computations.


Fitting And Pattern Alteration 2nd Edition

RRP $201.99

This textbook teaches students how to recognize, evaluate, and correct fit for over 100 figure variations. The book utilizes a multi-method approach that is both logical and easyto- follow, and each procedure is clearly identified and fully-illustrated. The second edition will include new information concerning figure evaluation, methods for working with multi-sized patterns, and instructions for correcting garments that have more than one fit problem. New to this Edition: -- Reorganized table of contents to reflect industry perspective -- Instructions for applying concepts to men's wear and children's wear -- Over 100 fitting variations and solutions to fitting problems -- Covers the elements and principles of design as they relate to fitting -- Multi-method approach to figure evaluation, fitting, and alternation includes both slash and pivot method -- Learning Objectives and important terms listed at the beginning of each chapter -- Review Questions at the end of each chapter Interactive exercises within each chapter -- Double-page format for each figure variation


Numerical Data Fitting In Dynamical Systems

RRP $32.99

Real life phenomena in engineering, natural, or medical sciences are often described by a mathematical model with the goal to analyze numerically the behaviour of the system. Advantages of mathematical models are their cheap availability, the possibility of studying extreme situations that cannot be handled by experiments, or of simulating real systems during the design phase before constructing a first prototype. Moreover, they serve to verify decisions, to avoid expensive and time consuming experimental tests, to analyze, understand, and explain the behaviour of systems, or to optimize design and production. As soon as a mathematical model contains differential dependencies from an additional parameter, typically the time, we call it a dynamical model. There are two key questions always arising in a practical environment: 1 Is the mathematical model correct? 2 How can I quantify model parameters that cannot be measured directly? In principle, both questions are easily answered as soon as some experimental data are available. The idea is to compare measured data with predicted model function values and to minimize the differences over the whole parameter space. We have to reject a model if we are unable to find a reasonably accurate fit. To summarize, parameter estimation or data fitting, respectively, is extremely important in all practical situations, where a mathematical model and corresponding experimental data are available to describe the behaviour of a dynamical system.



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