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Génie Civil > Mathématiques
Applied regression analysis / Draper, Norman R
Titre : Applied regression analysis Type de document : texte imprimé Auteurs : Draper, Norman R, Auteur ; Harry Smith, Auteur Mention d'édition : 3 éd. Editeur : New York : Wiley Année de publication : 1998 Importance : VI-XV-706 p. Présentation : ill. Format : 25 cm ISBN/ISSN/EAN : 978-0-471-17082-2 Note générale : Bibliogr. 593-603 p. Annexes.Index Langues : Anglais (eng) Mots-clés : Analyse de régression
Regression analysis
Prerequisite knowledge
Regression situationIndex. décimale : 519.22 Théorie statistique. Modèles statistiques. Statistiques mathématiques Résumé : An outstanding introduction to the fundamentals of regression analysis-updated and expanded The methods of regression analysis are the most widely used statistical tools for discovering the relationships among variables. This classic text, with its emphasis on clear, thorough presentation of concepts and applications, offers a complete, easily accessible introduction to the fundamentals of regression analysis. Assuming only a basic knowledge of elementary statistics, Applied Regression Analysis, Third Edition focuses on the fitting and checking of both linear and nonlinear regression models, using small and large data sets, with pocket calculators or computers. This Third Edition features separate chapters on multicollinearity, generalized linear models, mixture ingredients, geometry of regression, robust regression, and resampling procedures. Extensive support materials include sets of carefully designed exercises with full or partial solutions and a series of true/false questions with answers. All data sets used in both the text and the exercises can be found on the companion disk at the back of the book. For analysts, researchers, and students in university, industrial, and government courses on regression, this text is an excellent introduction to the subject and an efficient means of learning how to use a valuable analytical tool. It will also prove an invaluable reference resource for applied scientists and statisticians Note de contenu : Sommaire:
1. Basic prerequisite knowledge
2. Fitting a straight line by leat squares
3. Checking the straight line fit
4. Fitting straight lines: special topics
5. Regression in matrix terms: straight line case
6. Thr general regression situation
7. Extra sums of squares and tests for several parameters being zero
8. Serial correlation in the residual and the durbin-aston test
...Applied regression analysis [texte imprimé] / Draper, Norman R, Auteur ; Harry Smith, Auteur . - 3 éd. . - New York : Wiley, 1998 . - VI-XV-706 p. : ill. ; 25 cm.
ISBN : 978-0-471-17082-2
Bibliogr. 593-603 p. Annexes.Index
Langues : Anglais (eng)
Mots-clés : Analyse de régression
Regression analysis
Prerequisite knowledge
Regression situationIndex. décimale : 519.22 Théorie statistique. Modèles statistiques. Statistiques mathématiques Résumé : An outstanding introduction to the fundamentals of regression analysis-updated and expanded The methods of regression analysis are the most widely used statistical tools for discovering the relationships among variables. This classic text, with its emphasis on clear, thorough presentation of concepts and applications, offers a complete, easily accessible introduction to the fundamentals of regression analysis. Assuming only a basic knowledge of elementary statistics, Applied Regression Analysis, Third Edition focuses on the fitting and checking of both linear and nonlinear regression models, using small and large data sets, with pocket calculators or computers. This Third Edition features separate chapters on multicollinearity, generalized linear models, mixture ingredients, geometry of regression, robust regression, and resampling procedures. Extensive support materials include sets of carefully designed exercises with full or partial solutions and a series of true/false questions with answers. All data sets used in both the text and the exercises can be found on the companion disk at the back of the book. For analysts, researchers, and students in university, industrial, and government courses on regression, this text is an excellent introduction to the subject and an efficient means of learning how to use a valuable analytical tool. It will also prove an invaluable reference resource for applied scientists and statisticians Note de contenu : Sommaire:
1. Basic prerequisite knowledge
2. Fitting a straight line by leat squares
3. Checking the straight line fit
4. Fitting straight lines: special topics
5. Regression in matrix terms: straight line case
6. Thr general regression situation
7. Extra sums of squares and tests for several parameters being zero
8. Serial correlation in the residual and the durbin-aston test
...Exemplaires
Code-barres Cote Support Localisation Section Disponibilité Etat_Exemplaire 048145 519.22 DRA Papier Bibliothèque Centrale Génie Civil Disponible From elementary probability to stochastic differential equations / Sasha Cyganowski ; Peter E. Kloeden ; Jerzy Ombach
Titre : From elementary probability to stochastic differential equations : with MAPLE Type de document : texte imprimé Auteurs : Sasha Cyganowski, Auteur ; Peter E. Kloeden, Auteur ; Jerzy Ombach, Auteur Editeur : Berlin : Springer Année de publication : 2002 Collection : Universitext, ISSN 0172-5939 Importance : XVI-310 p. Format : 24 cm ISBN/ISSN/EAN : 978-3-540-42666-0 Note générale : Bibliogr. p. [305]-306. Index Langues : Anglais (eng) Mots-clés : Maple (Computer file)
Probabilities
Stochastic differential equations
Maple (logiciel)
Équations différentielles stochastiquesIndex. décimale : 519.21 Théorie des probabilités.Processus stochastiques Résumé : The authors provide a fast introduction to probabilistic and statistical concepts necessary to understand the basic ideas and methods of stochastic differential equations. The book is based on measure theory which is introduced as smoothly as possible. It is intended for advanced undergraduate students or graduates, not necessarily in mathematics, providing an overview and intuitive background for more advanced studies as well as some practical skills in the use of MAPLE in the context of probability and its applications. As prerequisites the authors assume a familiarity with basic calculus and linear algebra, as well as with elementary ordinary differential equations and, in the final chapter, simple numerical methods for such ODEs. Although statistics is not systematically treated, they introduce statistical concepts such as sampling, estimators, hypothesis testing, confidence intervals, significance levels and p-values and use them in a large number of examples, problems and simulations. Note de contenu : Contents
*Probality basics
*Measures and Integral
*Randon Variables and Distributions
*Parameters of Probability distributions
*A Tour of Important Distributions
*Numerical Simulations and Statistical Inference
*Stochastic Processes
*Stochastic Calculus
*Stochastic Differential Equations
*Numerical Methods for SDEsFrom elementary probability to stochastic differential equations : with MAPLE [texte imprimé] / Sasha Cyganowski, Auteur ; Peter E. Kloeden, Auteur ; Jerzy Ombach, Auteur . - Springer, 2002 . - XVI-310 p. ; 24 cm. - (Universitext, ISSN 0172-5939) .
ISBN : 978-3-540-42666-0
Bibliogr. p. [305]-306. Index
Langues : Anglais (eng)
Mots-clés : Maple (Computer file)
Probabilities
Stochastic differential equations
Maple (logiciel)
Équations différentielles stochastiquesIndex. décimale : 519.21 Théorie des probabilités.Processus stochastiques Résumé : The authors provide a fast introduction to probabilistic and statistical concepts necessary to understand the basic ideas and methods of stochastic differential equations. The book is based on measure theory which is introduced as smoothly as possible. It is intended for advanced undergraduate students or graduates, not necessarily in mathematics, providing an overview and intuitive background for more advanced studies as well as some practical skills in the use of MAPLE in the context of probability and its applications. As prerequisites the authors assume a familiarity with basic calculus and linear algebra, as well as with elementary ordinary differential equations and, in the final chapter, simple numerical methods for such ODEs. Although statistics is not systematically treated, they introduce statistical concepts such as sampling, estimators, hypothesis testing, confidence intervals, significance levels and p-values and use them in a large number of examples, problems and simulations. Note de contenu : Contents
*Probality basics
*Measures and Integral
*Randon Variables and Distributions
*Parameters of Probability distributions
*A Tour of Important Distributions
*Numerical Simulations and Statistical Inference
*Stochastic Processes
*Stochastic Calculus
*Stochastic Differential Equations
*Numerical Methods for SDEsExemplaires
Code-barres Cote Support Localisation Section Disponibilité Etat_Exemplaire 047731 519.21 CYG Papier Bibliothèque Centrale Génie Civil Disponible