Introduction to Linear Regression Analysis

As the Solutions Manual, this book is meant to accompany the main title, Introduction to Linear Regression Analysis, Fifth Edition. Clearly balancing theory with applications, this book describes both the conventional and less common uses of linear regression in the practical context of today's mathematical and scientific research. Beginning with a general introduction to regression modeling, including typical applications, the book then outlines a host of technical tools that form the linear regression analytical arsenal, including: basic inference procedures and introductory aspects of model adequacy checking; how transformations and weighted least squares can be used to resolve problems of model inadequacy; how to deal with influential observations; and polynomial regression models and their variations. The book also includes material on regression models with autocorrelated errors, bootstrapping regression estimates, classification and regression trees, and regression model validation.

Cover -- Title Page -- Copyright -- Preface -- Chapter 2: Simple Linear Regression -- Chapter 3: Multiple Linear Regression -- Chapter 4: Model Adequacy Checking -- Chapter 5: Transforrnations and Weighting to Correct Model Inadequacies -- Chapter 6: Diagnostics for Leverage and Influence -- Chapter 7: Polynornial Regression Models -- Chapter 8: Indicator Variables -- Chapter 9: Multicollinearity -- Chapter 10: Variable Selection and Model Building -- Chapter 11: Validation of Regression Models -- Chapter 12: Introduction to Nonlinear Regression -- Chapter 13: Generalized Linear Models -- Chapter 14: Regression Analysis of Time Series Data -- Chapter 15: Other Topics in the Use of Regression Analysis.