Advanced Regression Analysis

An Introduction

ISBN: 978-1-4739-1215-1
Verlag: SAGE Publications Ltd

Buch, Englisch, 224 Seiten, Format (B × H): 170 mm x 242 mm

Reihe: Research Methods for Social Scientists

Aimed at students who have completed basic statistical training, this book is the perfect gateway to help you move up to more advanced courses. Armed with an understanding of regression analysis, you will be ready to apply regression methods effectively and insightfully any problem.

Benefits of this book:

· Accessible to all students, regardless of their mathematical level

· Rich with examples and problems from a across the social sciences, business and health

· Examples and problem sets on the companion website help you to test your learning and put your skills into practice.

· A guide to Matrix Algebra to help students quickly grasp more advanced procedures

This is the ideal companion into advanced regression analysis for students in the social sciences, business and health.

Autoren/Hrsg.

Pollins, Brian Michael

Cooper, Robert

Fachgebiete

• Sozialwissenschaften Politikwissenschaft Politikwissenschaft Allgemein Politische Theorie, Politische Philosophie
• Sozialwissenschaften Soziologie | Soziale Arbeit Soziologie Allgemein Empirische Sozialforschung, Statistik

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