Regression analysis by example
The essentials of regression analysis through practical applications Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules,...
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Hoboken, N.J. :
Wiley-Interscience
c2006.
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| Edición: | 4th ed |
| Colección: | Wiley series in probability and statistics.
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009849083006719 |
Tabla de Contenidos:
- Preface; 1 Introduction; 1.1 What Is Regression Analysis?; 1.2 Publicly Available Data Sets; 1.3 Selected Applications of Regression Analysis; 1.3.1 Agricultural Sciences; 1.3.2 Industrial and Labor Relations; 1.3.3 History; 1.3.4 Government; 1.3.5 Environmental Sciences; 1.4 Steps in Regression Analysis; 1.4.1 Statement of the Problem; 1.4.2 Selection of Potentially Relevant Variables; 1.4.3 Data Collection; 1.4.4 Model Specification; 1.4.5 Method of Fitting; 1.4.6 Model Fitting; 1.4.7 Model Criticism and Selection
- 1.4.8 Objectives of Regression Analysis1.5 Scope and Organization of the Book; Exercises; 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; 3 Multiple Linear Regression; 3.1 Introduction; 3.2 Description of the Data and Model
- 3.3 Example: Supervisor Performance Data3.4 Parameter Estimation; 3.5 Interpretations of Regression Coefficients; 3.6 Properties of the Least Squares Estimators; 3.7 Multiple Correlation Coefficient; 3.8 Inference for Individual Regression Coefficients; 3.9 Tests of Hypotheses in a Linear Model; 3.9.1 Testing All Regression Coefficients Equal to Zero; 3.9.2 Testing a Subset of Regression Coefficients Equal to Zero; 3.9.3 Testing the Equality of Regression Coefficients; 3.9.4 Estimating and Testing of Regression Parameters Under Constraints; 3.10 Predictions; 3.11 Summary; Exercises
- Appendix: Multiple Regression in Matrix Notation4 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; 4.5.1 One-Dimensional Graphs; 4.5.2 Two-Dimensional Graphs; 4.5.3 Rotating Plots; 4.5.4 Dynamic Graphs; 4.6 Graphs After Fitting a Model; 4.7 Checking Linearity and Normality Assumptions; 4.8 Leverage, Influence, and Outliers; 4.8.1 Outliers in the Response Variable; 4.8.2 Outliers in the Predictors; 4.8.3 Masking and Swamping Problems
- 4.9 Measures of Influence4.9.1 Cook's Distance; 4.9.2 Welsch and Kuh Measure; 4.9.3 Hadi's Influence Measure; 4.10 The Potential-Residual Plot; 4.11 What to Do with the Outliers?; 4.12 Role of Variables in a Regression Equation; 4.12.1 Added-Variable Plot; 4.12.2 Residual Plus Component Plot; 4.13 Effects of an Additional Predictor; 4.14 Robust Regression; Exercises; 5. Qualitative Variables as Predictors; 5.1 Introduction; 5.2 Salary Survey Data; 5.3 Interaction Variables; 5.4 Systems of Regression Equations; 5.4.1 Models with Different Slopes and Different Intercepts
- 5.4.2 Models with Same Slope and Different Intercepts
