Introduction to Regression Modeling

Description

Looking for an easy-to-understand text to guide you through the tough topic of regression modeling? INTRODUCTION TO REGRESSION MODELING (WITH CD-ROM) offers a blend of theory and regression applications and will give you the practice you need to tackle this subject through exercises, case studies. and projects that have you identify a problem of interest and collect data relevant to the problem's solution. The book goes beyond linear regression by covering nonlinear models, regression models with time series errors, and logistic and Poisson regression models. Cover Contents Preface Chapter 1: Introduction to Regression Models 1.1 Introduction 1.2 Examples 1.3 A General Model 1.4 Important Reasons for Modeling 1.5 Data Plots and Empirical Modeling 1.6 An Iterative Model Building Approach 1.7 Some Comments on Data Exercises Chapter 2: Simple Linear Regression 2.1 The Model 2.2 Estimation of Parameters 2.3 Fitted Values, Residuals, and the Estimate of o2 2.4 Properties of Least Squares Estimates 2.5 Inferences about the Regression Parameters 2.6 Prediction 2.7 Analysis of Variance Approach to Regression 2.8 Another Example 2.9 Related Models Appendix: Univariate Distributions Exercises Chapter 3: A Review of Matrix Algebra and Important Results on Random Vectors 3.1 Review of Matrix Algebra 3.2 Matrix Approach to Simple Linear Regression 3.3 Vectors of Random Variables 3.4 The Multivariate Normal Distribution 3.5 Important Results on Quadratic Forms Exercises Chapter 4: Multiple Linear Regression Model 4.1 Introduction 4.2 Estimation of the Model 4.3 Statistical Inference 4.4 The Additional Sum of Squares Principle 4.5 The Analysis of Variance and the Coefficient of Determination, R2 4.6 Generalized Least Squares Appendix: Proofs of Results Exercises Chapter 5: Specification Issues in Regression Models 5.1 Elementary Special Cases 5.2 Systems of Straight Lines 5.3 Comparison of Several "Treatments" 5.4 X Matrices with Nearly Linear-Dependent Columns 5.5 X Matrices with Orthogonal Columns Exercises Chapter 6: Model Checking 6.1 Introduction 6.2 Residual Analysis 6.3 The Effect of Individual Cases 6.4 Assessing the Adequacy of the Functional Form: Testing for Lack of Fit 6.5 Variance-Stabilizing Transformations Appendix Exercises Chapter 7: Model Selection 7.1 Introduction 7.2 All Possible Regressions 7.3 Automatic Methods Exercises Chapter 8: Case Studies in Linear Regression 8.1 Educational Achievement of Iowa Students 8.2 Predicting the Price of Bordeaux Wine 8.3 Factors Influencing the Auction Price of Iowa Cows 8.4 Predicting U.S. Presidential Elections 8.5 Student Projects Leading to Additional Case Studies Exercises Chapter 9: Nonlinear Regression Models 9.1 Introduction 9.2 Overview of Useful Deterministic Models, With Emphasis on Nonlinear Growth Curve Models 9.3 Nonlinear Regression Mode 9.4 Inference in the Nonlinear Regression Model 9.5 Examples Exercises Chapter 10: Regression Models for Time Series Situations 10.1 A Brief Introduction to Time Series Models 10.2 The Effects of Ignoring the Autocorrelation in the Errors 10.3 The Estimation of Combined Regression Time Series Models 10.4 Forecasting with Combined Regression Time Series Models 10.5 Model-Building Strategy and Example 10.6 Cointegration and Regression with Time Series Data: An Example Exercises Chapter 11: Logistic Regression 11.1 The Model 11.2 Interpretation of the Parameters 11.3 Estimation of the Parameters 11.4 Inference 11.5 Model-Building Approach in the Context of Logistic Regression Models 11.6 Examples 11.7 Overdispersion 11.8 Other Models for Binary Responses 11.9 Modeling Responses with More Than Two Categorical Outcomes Exercises Chapter 12: Generalized Linear Models and Poisson Regression 12.1 The Model 12.2 Estimation of the Parameters in the Poisson Regression Model 12.3 Inference in the Poisson Regression Model 12.4 Overdispersion 12.5 Examples Exercises Brief Answers to Selected Exercises Statistical Tables References List of Data Files Index