Description
Book SynopsisThis book is an introduction to regression analysis, focusing on the practicalities of doing regression analysis on real-life data.
Contrary to other textbooks on regression, this book is based on the idea that you do not necessarily need to know much about statistics and mathematics to get a firm grip on regression and perform it to perfection. This non-technical point of departure is complemented by practical examples of real-life data analysis using statistics software such as Stata, R and SPSS. Parts 1 and 2 of the book cover the basics, such as simple linear regression, multiple linear regression, how to interpret the output from statistics programs, significance testing and the key regression assumptions. Part 3 deals with how to practically handle violations of the classical linear regression assumptions, regression modeling for categorical y-variables and instrumental variable (IV) regression. Part 4 puts the various purposes of, or motivations for, regression i
Trade Review
“This book finds a rare balance between applied statistical analysis and theory, giving students the confidence to apply regression analysis in their projects, while being aware of the potential pitfalls.” — Johan A. Elkink, Associate Professor in Social Science Research Methods, University College Dublin, Ireland
“This book provides a short and bright path to understand the meaning and usefulness of regression analysis. If you are a student or policy maker with limited econometrics skills this book equips you with the right and sufficient skills.” — Dr. Maty Konte, United Nations University
Table of ContentsPart 1: The Basics 1. What is regression analysis? 2. Linear regression with a single independent variable 3. Linear regression with several independent variables: Multiple regression Part 2: The Foundations 4. Samples and populations, statistical uncertainty and testing of statistical significance 5. The assumptions of regression analysis Part 3: The Extensions 6. Beyond linear regression: Non-additivity, non-linearity and mediation 7. A categorical dependent variable: Logistic (logit) regression and related methods 8. An ordered (ordinal) dependent variable: Logistic (logit) regression 9. The quest for a causal effect: Instrumental variable (IV) regression Part 4: Regression Purposes, Academic Regression Projects and the Way Ahead 10. Regression purposes in various academic settings and how to perform them 11. The way ahead: Related techniques
