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Handbook of Regression Analysis.
A Comprehensive Account for Data Analysts of the Methods and Applications of Regression Analysis. Written by two established experts in the field, the purpose of the Handbook of Regression Analysis is to provide a practical, one-stop reference on regression analysis. The focus is on the tools that b...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Chicester :
Wiley,
2013.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Title Page; Copyright Page; Dedication; Contents; Preface; Part I The Multiple Linear Regression Model; 1 Multiple Linear Regression; 1.1 Introduction; 1.2 Concepts and Background Material; 1.2.1 The Linear Regression Model; 1.2.2 Estimation Using Least Squares; 1.2.3 Assumptions; 1.3 Methodology; 1.3.1 Interpreting Regression Coefficients; 1.3.2 Measuring the Strength of the Regression Relationship; 1.3.3 Hypothesis Tests and Confidence Intervals for b; 1.3.4 Fitted Values and Predictions; 1.3.5 Checking Assumptions Using Residual Plots; 1.4 Example
- Estimating Home Prices.
- 1.5 Summary2 Model Building; 2.1 Introduction; 2.2 Concepts and Background Material; 2.2.1 Using Hypothesis Tests to Compare Models; 2.2.2 Collinearity; 2.3 Methodology; 2.3.1 Model Selection; 2.3.2 Example
- Estimating Home Prices (continued); 2.4 Indicator Variables and Modeling Interactions; 2.4.1 Example
- Electronic Voting and the 2004 Presidential Election; 2.5 Summary; Part II Addressing Violations of Assumptions; 3 Diagnostics for Unusual Observations; 3.1 Introduction; 3.2 Concepts and Background Material; 3.3 Methodology; 3.3.1 Residuals and Outliers; 3.3.2 Leverage Points.
- 3.3.3 Influential Points and Cook's Distance3.4 Example
- Estimating Home Prices (continued); 3.5 Summary; 4 Transformations and Linearizable Models; 4.1 Introduction; 4.2 Concepts and Background Material: The Log-Log Model; 4.3 Concepts and Background Material: Semilog Models; 4.3.1 Logged Response Variable; 4.3.2 Logged Predictor Variable; 4.4 Example
- Predicting Movie Grosses After One Week; 4.5 Summary; 5 Time Series Data and Autocorrelation; 5.1 Introduction; 5.2 Concepts and Background Material; 5.3 Methodology: Identifying Autocorrelation; 5.3.1 The Durbin-Watson Statistic.
- 5.3.2 The Autocorrelation Function (ACF)5.3.3 Residual Plots and the Runs Test; 5.4 Methodology: Addressing Autocorrelation; 5.4.1 Detrending and Deseasonalizing; 5.4.2 Example
- e-Commerce Retail Sales; 5.4.3 Lagging and Differencing; 5.4.4 Example
- Stock Indexes; 5.4.5 Generalized Least Squares (GLS): The Cochrane-Orcutt Procedure; 5.4.6 Example
- Time Intervals Between Old Faithful Eruptions; 5.5 Summary; Part III Categorical Predictors; 6 Analysis of Variance; 6.1 Introduction; 6.2 Concepts and Background Material; 6.2.1 One-Way ANOVA; 6.2.2 Two-Way ANOVA; 6.3 Methodology.
- 6.3.1 Codings for Categorical Predictors6.3.2 Multiple Comparisons; 6.3.3 Levene's Test and Weighted Least Squares; 6.3.4 Membership in Multiple Groups; 6.4 Example
- DVD Sales of Movies; 6.5 Higher-Way ANOVA; 6.6 Summary; 7 Analysis of Covariance; 7.1 Introduction; 7.2 Methodology; 7.2.1 Constant Shift Models; 7.2.2 Varying Slope Models; 7.3 Example
- International Grosses of Movies; 7.4 Summary; Part IV Other Regression Models; 8 Logistic Regression; 8.1 Introduction; 8.2 Concepts and Background Material; 8.2.1 The Logit Response Function; 8.2.2 Bernoulli and Binomial Random Variables.