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Discriminant equations are an important class of Diophantine equations with close ties to algebraic number theory, Diophantine approximation and Diophantine geometry. This book is the first comprehensive account of discriminant equations and their applications. It brings together many aspects, including effective results over number fields, effective results over finitely generated domains, estimates on the number of solutions, applications to algebraic integers of given discriminant, power integral bases, canonical number systems, root separation of polynomials and reduction of hyperelliptic curves. The authors' previous title, Unit Equations in Diophantine Number Theory, laid the groundwork by presenting important results that are used as tools in the present book. This material is briefly summarized in the introductory chapters along with the necessary basic algebra and algebraic number theory, making the book accessible to experts and young researchers alike.

Discriminant equations are an important class of Diophantine equations with close ties to algebraic number theory, Diophantine approximation and Diophantine geometry. This book is the first comprehensive account of discriminant equations and their applications. It brings together many aspects, including effective results over number fields, effective results over finitely generated domains, estimates on the number of solutions, applications to algebraic integers of given discriminant, power integral bases, canonical number systems, root separation of polynomials and reduction of hyperelliptic curves. The authors' previous title, Unit Equations in Diophantine Number Theory, laid the groundwork by presenting important results that are used as tools in the present book. This material is briefly summarized in the introductory chapters along with the necessary basic algebra and algebraic number theory, making the book accessible to experts and young researchers alike.

The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "For both applied and theoretical statisticians as well as investigators working in the many areas in which relevant use can be made of discriminant techniques, this monograph provides a modern, comprehensive, and systematic account of discriminant analysis, with the focus on the more recent advances in the field." –SciTech Book News ". . . a very useful source of information for any researcher working in discriminant analysis and pattern recognition." –Computational Statistics Discriminant Analysis and Statistical Pattern Recognition provides a systematic account of the subject. While the focus is on practical considerations, both theoretical and practical issues are explored. Among the advances covered are regularized discriminant analysis and bootstrap-based assessment of the performance of a sample-based discriminant rule, and extensions of discriminant analysis motivated by problems in statistical image analysis. The accompanying bibliography contains over 1,200 references.

Deals with the important issues of discriminant analysis when observed observations are discrete valued. Provides a unified source of information for the applied statistician, the engineer, and the quantitative research worker in biology, sociology, and marketing research and consumer behavior.

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Rebecca M. Warner′s Applied Statistics: From Bivariate Through Multivariate Techniques, Second Edition provides a clear introduction to widely used topics in bivariate and multivariate statistics, including multiple regression, discriminant analysis, MANOVA, factor analysis, and binary logistic regression. The approach is applied and does not require formal mathematics; equations are accompanied by verbal explanations. Students are asked to think about the meaning of equations. Each chapter presents a complete empirical research example to illustrate the application of a specific method. Although SPSS examples are used throughout the book, the conceptual material will be helpful for users of different programs. Each chapter has a glossary and comprehension questions.

APPLIED REGRESSION ANALYSIS applies regression to real data and examples while employing commercial statistical and spreadsheet software. Covering the core regression topics as well as optional topics including ANOVA, Time Series Forecasting, and Discriminant Analysis, the text emphasizes the importance of understanding the assumptions of the regression model, knowing how to validate a selected model for these assumptions, knowing when and how regression might be useful in a business setting, and understanding and interpreting output from statistical packages and spreadsheets.

This book is a quick an easy introduction to the techniques used in marketing research. Using no mathematics or formulas, this book explains all major analytic techniques used in marketing research. The techniques explained in this book include: * Factor Analysis * Discriminant Analysis * Boosting * Logistic Regression * Turf Analysis * Regression Analysis * Price Sensitivity Meter * Random Forest * MaxDiff * Decision Trees * Perceptual Mapping * Structural Equation (SEM) * Cluster Analysis * Conjoint Analysis * Support Vector Machines * Naive Bayes * Path Analysis * Artificial Neural Network

Applied Statistics: From Bivariate Through Multivariate Techniques provides a clear introduction to widely used topics in bivariate and multivariate statistics including multiple regression, discriminant analysis, MANOVA, factor analysis, and binary logistic regression. Author Rebecca M. Warner presents an applied approach that does not require formal mathematics and equations are accompanied by verbal explanations.