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Regression Analysis by Example, Fifth Edition, has been expanded and thoroughly updated to reflect recent advances in the field. The emphasis continues to be on exploratory data analysis rather than statistical theory. The book offers in-depth treatment of regression diagnostics, transformation, multicollinearity, logistic regression, and robust regression. Methods of regression analysis are clearly demonstrated, and examples containing the types of irregularities commonly encountered in the real world are provided. Each example isolates one or two techniques and features detailed discussions of the techniques themselves, the required assumptions, and the evaluated success of each technique. The methods described throughout the book can be carried out with most of the currently available statistical software packages, such as the software packages R, SPSS, SAS, and Minitab. Regression Analysis by Example, Fifth Edition is suitable for anyone with an understanding of elementary statistics. Preface 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 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 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 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 5 Qualitative Variables as Predictors 5.1 Introduction 5.2 Salary Survey Data 5.3 Interaction Variables 5.4 Systems of Regression Equations 5.5 Other Applications of Indicator Variables 5.6 Seasonality 5.7 Stability of Regression Parameters Over Time Exercises 6 Transformation of Variables 6.1 Introduction 6.2 Transformations to Achieve Linear 6.3 Bacteria Deaths Due to XRay 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 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 8 The Problem of Correlated Errors 8.1 Introduction: Autocorrelation 8.2 Consumer Expenditure and Money Stock 8.3 Durbin-Watson Statistic 8.4 Removal of Autocorrelation by Transformation 8.5 Iterative Estimation With Autocorrelated Errors 8.6 Autocorrelation and Missing Variables 8.7 Analysis of Housing Starts 8.8 Limitations of Durbin-Watson Printed Pages: 420. Buchnummer des Verkäufers 75248
Titel: Regression Analysis by Example: Wiley Series...
Verlag: Wiley India Pvt. Ltd
Auflage: 5th or later edition.