Inhaltsverzeichnis
Chapter 1: Understanding and Applying Regression Analysis - Theory as Well as Practice
Chapter 2: Basic Matrix Algebra for Regression Analysis
Chapter 3: Ordinary Least Squares Regression Derived, and Initial Tenets of Estimation Practice Introduced
Chapter 4: Moving from Ordinary to Generalized Least Squares, Illustrated through the Problem of Heteroskedasticity
Chapter 5: Autocorrelated Errors - A Further Look at Generalized Least Squares
Chapter 6: Finding Unusual Cases in Your Data Set - They Aren't Just ¿Outliers¿
Chapter 7: Collinearity - Finding and Coping with Very High Correlations Among Explanatory Variables
Chapter 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
Klappentext
This book will introduce you to advanced regression analysis using Matrix Algebra, and will help you prepare to transition to more advanced statistical courses.
