| Titre : |
Applied linear regression |
| Type de document : |
texte imprimé |
| Auteurs : |
Weisberg , Sanford, Auteur |
| Mention d'édition : |
3 éd. |
| Editeur : |
New York : John Wiley & Sons |
| Année de publication : |
2005 |
| Collection : |
Wiley series in probability and statistics |
| Importance : |
XVI-310 p. |
| Présentation : |
ill. |
| Format : |
24 cm |
| ISBN/ISSN/EAN : |
978-0-471-66379-9 |
| Note générale : |
Bibliogr. p. 293-299. Index |
| Langues : |
Anglais (eng) |
| Mots-clés : |
Mathématiques régression -- Linéaire appliquée
Applied linear
Linear Regression |
| Index. décimale : |
519.233.5 Analyse de la corrélation. Analyse de régression |
| Résumé : |
Applied Linear Regression, Third Edition is thoroughly updated to help students master the theory and applications of linear regression modeling. Focusing on model building, assessing fit and reliability, and drawing conclusions, the text demonstrates how to develop estimation, confidence, and testing procedures primarily through the use of least squares regression. To facilitate quick learning, this Third Edition stresses using graphical methods to find appropriate models and to better understand them. In that spirit, most analyses and homework problems use graphs for the discovery of structure as well as for the summarization of results. This text is an excellent tool for learning how to use linear regression analysis techniques to solve and gain insight into real-life problems. |
| Note de contenu : |
Sommaire:
1. Scatterplots and Regression.
2. Simple Linear Regression.
3. Multiple Regression.
4. Drawing Conclusions.
5. Weights, Lack of Fit, and More.
6. Polynomials and Factors.
7. Transformations.
8. Regression Diagnostics: Residuals.
9. Outliers and Influence.
10. Variable Selection.
11. Nonlinear Regression.
12. Logistic Regression.
Appendix A.1. Web Site.
A.2. Means and Variances of Random Variables.
A.3. Least Squares for Simple Regression.
A.4. Means and Variances of Least Squares Estimates.
A.5. Estimating E(Y/X) Using a Smoother.
A.6. A Brief Introduction to Matrices and Vectors.
Appendix A.7. Random Vectors.
A.8. Least Squares Using Matrices.
A.9. The QR Factorization.
A.10. Maximum Likelihood Estimates.
A.11. The Box-Cox Method for Transformations.
A.12. Case Deletion in Linear Regression. |
Applied linear regression [texte imprimé] / Weisberg , Sanford, Auteur . - 3 éd. . - New York : John Wiley & Sons, 2005 . - XVI-310 p. : ill. ; 24 cm. - ( Wiley series in probability and statistics) . ISBN : 978-0-471-66379-9 Bibliogr. p. 293-299. Index Langues : Anglais ( eng)
| Mots-clés : |
Mathématiques régression -- Linéaire appliquée
Applied linear
Linear Regression |
| Index. décimale : |
519.233.5 Analyse de la corrélation. Analyse de régression |
| Résumé : |
Applied Linear Regression, Third Edition is thoroughly updated to help students master the theory and applications of linear regression modeling. Focusing on model building, assessing fit and reliability, and drawing conclusions, the text demonstrates how to develop estimation, confidence, and testing procedures primarily through the use of least squares regression. To facilitate quick learning, this Third Edition stresses using graphical methods to find appropriate models and to better understand them. In that spirit, most analyses and homework problems use graphs for the discovery of structure as well as for the summarization of results. This text is an excellent tool for learning how to use linear regression analysis techniques to solve and gain insight into real-life problems. |
| Note de contenu : |
Sommaire:
1. Scatterplots and Regression.
2. Simple Linear Regression.
3. Multiple Regression.
4. Drawing Conclusions.
5. Weights, Lack of Fit, and More.
6. Polynomials and Factors.
7. Transformations.
8. Regression Diagnostics: Residuals.
9. Outliers and Influence.
10. Variable Selection.
11. Nonlinear Regression.
12. Logistic Regression.
Appendix A.1. Web Site.
A.2. Means and Variances of Random Variables.
A.3. Least Squares for Simple Regression.
A.4. Means and Variances of Least Squares Estimates.
A.5. Estimating E(Y/X) Using a Smoother.
A.6. A Brief Introduction to Matrices and Vectors.
Appendix A.7. Random Vectors.
A.8. Least Squares Using Matrices.
A.9. The QR Factorization.
A.10. Maximum Likelihood Estimates.
A.11. The Box-Cox Method for Transformations.
A.12. Case Deletion in Linear Regression. |
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