Regression Analysis

This thoroughly practical and engaging textbook conveys the skills needed to responsibly develop, conduct, scrutinize, and interpret statistical analyses without requiring high-level math.

Regression Analysis details the most common sources of statistical biases, including those not covered in other textbooks. Rather than focusing on complicated equations, the book describes these biases visually and with examples of situations in which they could arise. Unique features include an innovative approach to describing the elusive concept of “holding other factors constant,” proper interpretations of the strength of evidence in light of the Bayesian critique of hypothesis testing, and a focus on ethical research practices. This third edition features coverage of the Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) model, Time Series Cross Section models and Multi-level (Hierarchical) models, and has been updated throughout to include more examples.

This is the textbook the author wishes he had learned from, as it would have helped him avoid many research mistakes he made in his career. It is ideal for undergraduate and postgraduate students learning quantitative methods in the social sciences, business, medicine, and data analytics. It will also appeal to researchers and academics looking to better understand regressions.