The new Second Edition extends coverage to regression models such as: generalized linear models; limited-dependent-variable-models; mixed models and Cox regression among other methods. INDICE: Preface 1 - Statistical Models and Social Science I - DATA CRAFT 2- What is Regression Analysis? 3 - Examining Data 4 - Transforming Data II - LINEAR MODELS AND LEAST SQUARES 5 - Linear Least-Squares Regression 6 - Statistical Inference for Regression 7 - Dummy-Variable Regression 8 - Analysis of Variance 9 - Statistical Theory for Linear Models 10 - The Vector Geometry of Linear Models III - LINEAR-MODEL DIAGNOSTICS 11 - Unusual and Influential Data 12 - Diagnosing Non-Normality, Nonconstant Error Variance, and Nonlinearity 13- Collinearity and its Purported Remedies IV - GENERALIZED LINEAR MODELS 14 -Logit and Probit Models 15 - Generalized Linear Models V - EXTENDING LINEAR AND GENERALIZED LINEAR MODELS 16 - Time-Series Regression 17 - Nonlinear Regression 18 - Nonparametric Regression 19 - Robust Regression 20 - Missing Data inRegression Models 21 - Bootstrapping Regression Models 22 - Model Selection, Averaging, and Validation A Notation References
- ISBN: 978-0-7619-3042-6
- Editorial: Sage Publications
- Encuadernacion: Cartoné
- Páginas: 600
- Fecha Publicación: 01/05/2008
- Nº Volúmenes: 1
- Idioma: Inglés