Introduction to regression modeling
| Type de document | Site actuel | Cote | Statut | Date de retour prévue | Code à barres | Réservations |
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| Livre | La bibliothèque des Sciences Exactes et Naturelles | 519.536 ABR (Parcourir l'étagère) | Disponible | 0000000013333 |
Survol La bibliothèque des Sciences Exactes et Naturelles Étagères Fermer l'étagère
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| 519.535 MCL Discriminant analysis and statistical pattern recognition / | 519.535 MON Recent developments on structural equations models | 519.535 NAK Statistique explicative appliquée | 519.536 ABR Introduction to regression modeling | 519.536 DIE Applied regression analysis | 519.536 HAS Generalized additive models / | 519.536 STA Flexible regression and smoothing : using GAMLSS in R / |
Using a data-driven approach, this book is an exciting blend of theory and interesting regression applications. Students learn the theory behind regression while actively applying it. Working with many case studies, projects, and exercises from areas such as engineering, business, social sciences, and the physical sciences, students discover the purpose of regression and learn how, when, and where regression models work. The book covers the analysis of observational data as well as of data that arise from designed experiments. Special emphasis is given to the difficulties when working with observational data, such as problems arising from multicollinearity and "messy" data situations that violate some of the usual regression assumptions. Throughout the text, students learn regression modeling by solving exercises that emphasize theoretical concepts, by analyzing real data sets, and by working on projects that require them to identify a problem of interest and collect data that are relevant to the problem's solution. The book goes beyond linear regression by covering nonlinear models, regression models with time series errors, and logistic and Poisson regression models.


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