| Título : |
Applied Regression Analysis and Generalized Linear Models |
| Tipo de documento: |
Libro |
| Autores: |
John Fox, Autor |
| Mención de edición: |
3rd.ed. |
| Editorial: |
Los Angeles : SAGE |
| Fecha de publicación: |
2016 |
| Número de páginas: |
xxiv, 791 p. |
| Il.: |
il |
| ISBN/ISSN/DL: |
978-1-4522-0566-3 |
| Idioma : |
Inglés (eng) |
| Clasificación: |
[BFA] Ciencias Exactas y Aplicadas:Estadística
|
| Palabras clave: |
Análisis de regresión Modelos lineales |
| Nota de contenido: |
Statistical Models and Social Science.
What Is Regression Analysis?
Examining Data.
Transforming Data.
Linear Least-Squares Regression.
Statistical Inference for Regression.
Dummy-Variable Regression.
Analysis of Variance.
Statistical Theory for Linear Models.
The Vector Geometry of Linear Models.
Unusual and Influential Data.
Diagnosing Non-Normality, Nonconstant Error Variance, and Nonlinearity.
Collinearity and Its Purported Remedies.
Logit and Probit Models for Categorical Response Variables.
Generalized Linear Models.
Time-Series Regression and Generalized Least Squares.
Nonlinear Regression.
Nonparametric Regression.
Robust Regression.
Missing Data in Regression Models.
Bootstrapping Regression Models.
Model Selection, Averaging, and Validation.
Linear Mixed-Effects Models for Hierarchical and Longitudinal Data.
Generalized Linear and Nonlinear Mixed-Effects Models.
|
| Link: |
https://www.bfa.fcnym.unlp.edu.ar/id/47258 |
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