Regression Analysis by Example

Detalles Bibliográficos
Autor principal: Hadi, Ali S.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated, 2012.
Colección:New York Academy of Sciences Ser.
Temas:
Electronic books.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Intro
  • Half Title page
  • Title page
  • Copyright page
  • Dedication
  • Preface
  • Chapter 1: Introduction
  • 1.1 What Is Regression Analysis?
  • 1.2 Publicly Available Data Sets
  • 1.3 Selected Applications of Regression Analysis
  • 1.4 Steps in Regression Analysis
  • 1.5 Scope And Organization of the Book
  • Exercises
  • Chapter 2: Simple Linear Regression
  • 2.1 Introduction
  • 2.2 Covariance and Correlation Coefficient
  • 2.3 Example: Computer Repair Data
  • 2.4 The Simple Linear Regression Model
  • 2.5 Parameter Estimation
  • 2.6 Tests of Hypotheses
  • 2.7 Confidence Intervals
  • 2.8 Predictions
  • 2.9 Measuring the Quality of Fit
  • 2.10 Regression Line Through the Origin
  • 2.11 Trivial Regression Models
  • 2.12 Bibliographic Notes
  • Exercises
  • Chapter 3: Multiple Linear Regression
  • 3.1 Introduction
  • 3.2 Description of the Data and Model
  • 3.3 Example: Supervisor Performance Data
  • 3.4 Parameter Estimation
  • 3.5 Interpretations of Regression Coefficients
  • 3.6 Centering and Scaling
  • 3.7 Properties of the Least Squares Estimators
  • 3.8 Multiple Correlation Coefficient
  • 3.9 Inference for Individual Regression Coefficients
  • 3.10 Tests of Hypotheses in a Linear Model
  • 3.11 Predictions
  • 3.12 Summary
  • Exercises
  • Appendix: Multiple Regression in Matrix Notation
  • Chapter 4: Regression Diagnostics: Detection of Model Violations
  • 4.1 Introduction
  • 4.2 The Standard Regression Assumptions
  • 4.3 Various Types of Residuals
  • 4.4 Graphical Methods
  • 4.5 Graphs Before Fitting a Model
  • 4.6 Graphs After Fitting a Model
  • 4.7 Checking Linearity and Normality Assumptions
  • 4.8 Leverage, Influence, and Outliers
  • 4.9 Measures of Influence
  • 4.10 The Potential-Residual Plot
  • 4.11 What to Do with the Outliers?
  • 4.12 Role of Variables in a Regression Equation
  • 4.13 Effects of an Additional Predictor
  • 4.14 Robust Regression
  • Exercises
  • Chapter 5: Qualitative Variables as Predictors
  • 5.1 Introduction
  • 5.2 Salary Survey Data
  • 5.3 Interaction Variables
  • 5.4 Systems of Regression Equations: Comparing Two Groups
  • 5.5 Other Applications of Indicator Variables
  • 5.6 Seasonality
  • 5.7 Stability of Regression Parameters Over Time
  • Exercises
  • Chapter 6: Transformation of Variables
  • 6.1 Introduction
  • 6.2 Transformations to Achieve Linearity
  • 6.3 Bacteria Deaths Due to X-Ray Radiation
  • 6.4 Transformations to Stabilize Variance
  • 6.5 Detection of Heteroscedastic Errors
  • 6.6 Removal of Heteroscedasticity
  • 6.7 Weighted Least Squares
  • 6.8 Logarithmic Transformation of Data
  • 6.9 Power Transformation
  • 6.10 Summary
  • Exercises
  • Chapter 7: Weighted Least Squares
  • 7.1 Introduction
  • 7.2 Heteroscedastic Models
  • 7.3 Two-Stage Estimation
  • 7.4 Education Expenditure Data
  • 7.5 Fitting a Dose-Response Relationship Curve
  • Exercises
  • Chapter 8: the Problem of Correlated Errors
  • 8.1 Introduction: Autocorrelation
  • 8.2 Consumer Expenditure and Money Stock

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