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Advances in Complex Function Theory (Lecture Notes in Mathematics) by W. E. Kirwan (Repost)
Matrix Mathematics - Theory, Facts, and Formulas, Second Edition
Mathematics Probability, Markov Chains, Queues, and Simulation - The Mathematical Basis of Performance Modeling
Algebraic Aspects of Cryptography (Algorithms and Computation in Mathematics) by Neal Koblitz (Repost)
Mathematics Mathematical Foundations of Computer Science 2004 [Repost]
Mathematics Mathematical Logic for Computer Science (3rd edition)
Spaces of Holomorphic Functions in the Unit Ball (Graduate Texts in Mathematics) by Kehe Zhu (Repost)
An Introduction to Ergodic Theory (Graduate Texts in Mathematics) by Peter Walters (Repost)
Mathematics Symmetry Theory in Molecular Physics with Mathematica: A new kind of tutorial book (Repost)
-Mathematics for the Physical Sciences- by Herbert S. Wilf
Mathematics for Elementary Teachers - A Conceptual Approach, 9 edition
Computer-Enabled Mathematics - Integrating Experiment and Theory in Teacher Education
How to Fold It - The Mathematics of Linkages, Origami and Polyhedra
Mathematics Engineering Analysis: Interactive Methods and Programs with FORTRAN, QuickBASIC, MATLAB, and Mathematica [Repost]
Mathematics Maverick Mathematician: The Life and Science of J.E. Moyal
African Mathematics: From Bones to Computers (repost)
Topology (Allyn and Bacon Series in Advanced Mathematics) by James Dugundji
Mathematics Fundamentals of Algebraic Modeling - An Introduction to Mathematical Modeling with Algebra and Statistics, 5 edition
Mathematics LMSST - 24 Lectures on Elliptic Curves (London Mathematical Society Student Texts) by J. W. S. Cassels
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Mathematics R¨¦gression: Th¨¦orie et applications
Posted on 2010-04-12
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More Pierre-Andr¨¦ Cornillon, Eric Matzner-Lober ¡°R¨¦gression: Th¨¦orie et applications" Cet ouvrage expose de mani¨¨re d¨¦taill¨¦e, exemples ¨¤ l'appui, l¡¯une des m¨¦thodes statistiques les plus courantes : la r¨¦gression. Les premiers chapitres sont consacr¨¦s ¨¤ la r¨¦gression lin¨¦aire simple et multiple. Ils expliquent les fondements de la m¨¦thode, tant au niveau des choix op¨¦r¨¦s que des hypoth¨¨ses et de leur utilit¨¦. Ensuite sont d¨¦velopp¨¦s les outils permettant de v¨¦rifier les hypoth¨¨ses de base mises en ?uvre par la r¨¦gression. Une pr¨¦sentation simple des mod¨¨les d'analyse de la covariance et de la variance est effectu¨¦e. Enfin, les derniers chapitres sont consacr¨¦s au choix de mod¨¨les ainsi qu'¨¤ certaines extensions de la r¨¦gression: lasso, PLS, PCR... La pr¨¦sentation t¨¦moigne d'un r¨¦el souci p¨¦dagogique des auteurs qui b¨¦n¨¦ficient d'une exp¨¦rience d'enseignement aupr¨¨s de publics tr¨¨s vari¨¦s. Les r¨¦sultats expos¨¦s sont replac¨¦s dans la perspective de leur utilit¨¦ pratique grace ¨¤ l'analyse d'exemples concrets. Les commandes permettant le traitement des exemples sous le logiciel R figurent dans le corps du texte. Enfin chaque chapitre est compl¨¦t¨¦ par une suite d'exercices corrig¨¦s. Le niveau math¨¦matique requis le rend accessible aux ¨¦tudiants des ¨¦coles d'ing¨¦nieurs, de Masters et aux chercheurs dans les divers domaines des sciences appliqu¨¦es. "
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