Introduction to linear regression analysis
Montgomery, Douglas C.
Introduction to linear regression analysis / Douglas C. Montgomery, Elizabeth A. Peck, G. Geoffrey Vining. - Sixth edition. - Hoboken, NJ : John Wiley & Sons, 2021. - xvi, 673 pages : illustrations, tables, charts (black and white) ; 27 cm. - Wiley series in probability and statistics. .
Includes bibliographical references and index.
1. Introduction — 2. Simple linear regression — 3. Multiple linear regression — 4. Model adequacy checking — 5. Transformations and weighting to correct model inadequacies — 6. Diagnostics for leverage and influence — 7. Polynomial regression models — 8. Indicator variables — 9. Multicollinearity — 10. Variable selection and model building — 11. Validation of regression models — 12. Introduction to nonlinear regression — 13. Generalized linear models — 14. Regression analysis of time series data — 15. Other topics in the use of regression analysis — Appendix — Index.
Introduction to Linear Regression Analysis, 6th Edition is the most comprehensive, fulsome, and current examination of the foundations of linear regression analysis. Fully updated in this new sixth edition, the distinguished authors have included new material on generalized regression techniques and new examples to help the reader understand retain the concepts taught in the book.
The new edition focuses on four key areas of improvement over the fifth edition: New exercises and data sets ; New material on generalized regression techniques ; The inclusion of JMP software in key areas
Carefully condensing the text where possible.
Introduction to Linear Regression Analysis skillfully blends theory and application in both the conventional and less common uses of regression analysis in today’s cutting-edge scientific research. The text equips readers to understand the basic principles needed to apply regression model-building techniques in various fields of study, including engineering, management, and the health sciences.
2020034056
Regression analysis
QA278.2 / .M65 2021
Introduction to linear regression analysis / Douglas C. Montgomery, Elizabeth A. Peck, G. Geoffrey Vining. - Sixth edition. - Hoboken, NJ : John Wiley & Sons, 2021. - xvi, 673 pages : illustrations, tables, charts (black and white) ; 27 cm. - Wiley series in probability and statistics. .
Includes bibliographical references and index.
1. Introduction — 2. Simple linear regression — 3. Multiple linear regression — 4. Model adequacy checking — 5. Transformations and weighting to correct model inadequacies — 6. Diagnostics for leverage and influence — 7. Polynomial regression models — 8. Indicator variables — 9. Multicollinearity — 10. Variable selection and model building — 11. Validation of regression models — 12. Introduction to nonlinear regression — 13. Generalized linear models — 14. Regression analysis of time series data — 15. Other topics in the use of regression analysis — Appendix — Index.
Introduction to Linear Regression Analysis, 6th Edition is the most comprehensive, fulsome, and current examination of the foundations of linear regression analysis. Fully updated in this new sixth edition, the distinguished authors have included new material on generalized regression techniques and new examples to help the reader understand retain the concepts taught in the book.
The new edition focuses on four key areas of improvement over the fifth edition: New exercises and data sets ; New material on generalized regression techniques ; The inclusion of JMP software in key areas
Carefully condensing the text where possible.
Introduction to Linear Regression Analysis skillfully blends theory and application in both the conventional and less common uses of regression analysis in today’s cutting-edge scientific research. The text equips readers to understand the basic principles needed to apply regression model-building techniques in various fields of study, including engineering, management, and the health sciences.
2020034056
Regression analysis
QA278.2 / .M65 2021
