Applied Linear Regression

Detalles Bibliográficos
Autor principal: Weisberg, Sanford
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated, 2013.
Colección:New York Academy of Sciences Ser.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 2.6 Confidence Intervals and t-Tests
  • 2.7 The Coefficient of Determination, R2
  • 2.8 The Residuals
  • CHAPTER 3: Multiple Regression
  • 3.1 Adding a Regressor to a Simple Linear Regression Model
  • 3.2 The Multiple Linear Regression Model
  • 3.3 Predictors and Regressors
  • 3.4 Ordinary Least Squares
  • 3.5 Predictions, Fitted Values, and Linear Combinations
  • CHAPTER 4: Interpretation of Main Effects
  • 4.1 Understanding Parameter Estimates
  • 4.2 Dropping Regressors
  • 4.3 Experimentation versus Observation
  • 4.4 Sampling from a Normal Population
  • 4.5 More on R2
  • CHAPTER 5: Complex Regressors
  • 5.1 Factors
  • 5.2 Many Factors
  • 5.3 Polynomial Regression
  • 5.4 Splines
  • 5.5 Principal Components
  • 5.6 Missing Data
  • CHAPTER 6: Testing and Analysis of Variance
  • 6.1 F-Tests
  • 6.2 The Analysis of Variance
  • 6.3 Comparisons of Means
  • 6.4 Power and Non-Null Distributions
  • 6.5 Wald Tests
  • 6.6 Interpreting Tests
  • CHAPTER 7: Variances
  • 7.1 Weighted Least Squares
  • 7.2 Misspecified Variances
  • 7.3 General Correlation Structures
  • 7.4 Mixed Models
  • 7.5 Variance Stabilizing Transformations
  • 7.6 The Delta Method
  • 7.7 The Bootstrap
  • CHAPTER 8: Transformations
  • 8.1 Transformation Basics
  • 8.2 A General Approach to Transformations
  • 8.3 Transforming the Response
  • 8.4 Transformations of Nonpositive Variables
  • 8.5 Additive Models
  • CHAPTER 9: Regression Diagnostics
  • 9.1 The Residuals
  • 9.2 Testing for Curvature
  • 9.3 Nonconstant Variance
  • 9.4 Outliers
  • 9.5 Influence of Cases
  • 9.6 Normality Assumption
  • CHAPTER 10: Variable Selection
  • 10.1 Variable Selection and Parameter Assessment
  • 10.2 Variable Selection for Discovery
  • 10.3 Model Selection for Prediction
  • CHAPTER 11: Nonlinear Regression
  • 11.1 Estimation for Nonlinear Mean Functions
  • 11.2 Inference Assuming Large Samples
  • 11.3 Starting Values
  • 11.4 Bootstrap Inference
  • 11.5 Further Reading
  • CHAPTER 12: Binomial and Poisson Regression
  • 12.1 Distributions for Counted Data
  • 12.2 Regression Models For Counts
  • 12.3 Poisson Regression
  • 12.4 Transferring What You Know about Linear Models
  • 12.5 Generalized Linear Models
  • Appendix
  • A.1 Website
  • A.2 Means, Variances, Covariances, and Correlations
  • A.3 Least Squares for Simple Regression
  • A.4 Means and Variances of Least Squares Estimates

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