This chapter evaluates regression analysis, which uses quantitative and sometimes also qualitative independent variables to explain or predict change in a quantitative dependent variable. To attain this goal, it relies on the principles of covariance and correlation. Its most basic form is linear regression, also known as ordinary least squares (OLS) regression. In addition, there are many other varieties of regression methods for different research questions and data characteristics, such as time-oriented questions or data with a limited range of values. Researchers use regression analysis especially to analyse complex patterns of correlation in situations with more than one explanatory variable. Often such patterns are interpreted in the context of causal theories. The concept of regression goes back to Francis Galton’s study on human height.
