Chapter 1: Understanding and Applying Regression Analysis - Theory as Well as PracticeChapter 2: Basic Matrix Algebra for Regression AnalysisChapter 3: Ordinary Least Squares Regression Derived, and Initial Tenets of Estimation Practice IntroducedChapter 4: Moving from Ordinary to Generalized Least Squares, Illustrated through the Problem of HeteroskedasticityChapter 5: Autocorrelated Errors - A Further Look at Generalized Least SquaresChapter 6: Finding Unusual Cases in Your Data Set - They Aren't Just OutliersChapter 7: Collinearity - Finding and Coping with Very High Correlations Among Explanatory VariablesChapter 8: Model Specification - How Can We Know When a Model is Good, or Better than a Competing Model?Chapter 9: Measurement Error in Our Independent and Dependent Variables - How Might This Compromise Your Parameter Estimates, And What Can You Do About It?Chapter 10: Regression Analysis Is the Gateway - Some Directions for Further Study in Data Science.