“…Ginebra…”

“…Ginebra Descripción y viajes…”

Tabla de Contenidos: “…Nominal subprojections and their word orders -- Toward a restrictive theory of linear order -- Extending the analysis to the clause -- The generalizations that characterize linear order and what they follow from…”

“…La capacidad de influir en el pensamiento y en las emociones de los otros, de “generar su aceptación”, se considera ya el principal atributo del liderazgo…”
Texto completo en Odilo

Tabla de Contenidos: “…Intro -- Foundations of Linear and Generalized Linear Models -- Contents -- Preface -- Purpose of this book -- Use as a textbook -- Acknowledgments -- 1 Introduction to Linear and Generalized Linear Models -- 1.1 Components of a Generalized Linear Model -- 1.1.1 Random Component of a GLM -- 1.1.2 Linear Predictor of a GLM -- 1.1.3 Link Function of a GLM -- 1.1.4 A GLM with Identity Link Function is a "Linear Model" -- 1.1.5 GLMs for Normal, Binomial, and Poisson Responses -- 1.1.6 Advantages of GLMs versus Transforming the Data -- 1.2 Quantitative/Qualitative Explanatory Variables and Interpreting Effects -- 1.2.1 Quantitative and Qualitative Variables in Linear Predictors -- 1.2.2 Interval, Nominal, and Ordinal Variables -- 1.2.3 Interpreting Effects in Linear Models -- 1.3 Model Matrices and Model Vector Spaces -- 1.3.1 Model Matrices Induce Model Vector Spaces -- 1.3.2 Dimension of Model Space Equals Rank of Model Matrix -- 1.3.3 Example: The One-Way Layout -- 1.4 Identifiability and Estimability -- 1.4.1 Identifiability of GLM Model Parameters -- 1.4.2 Estimability in Linear Models -- 1.5 Example: Using Software to Fit a GLM -- 1.5.1 Example: Male Satellites for Female Horseshoe Crabs -- 1.5.2 Linear Model Using Weight to Predict Satellite Counts -- 1.5.3 Comparing Mean Numbers of Satellites by Crab Color -- Chapter Notes -- Exercises -- 2 Linear Models: Least Squares Theory -- 2.1 Least Squares Model Fitting -- 2.1.1 The Normal Equations and Least Squares Solution -- 2.1.2 Hat Matrix and Moments of Estimators -- 2.1.3 Bivariate Linear Model and Regression Toward the Mean -- 2.1.4 Least Squares Solutions When X Does Not Have Full Rank -- 2.1.5 Orthogonal Subspaces and Residuals -- 2.1.6 Alternatives to Least Squares -- 2.2 Projections of Data Onto Model Spaces -- 2.2.1 Projection Matrices -- 2.2.2 Projection Matrices for Linear Model Spaces…”