Regression is a way to statistically understand the connection between two or more things. It helps us figure out how changes in one thing can predict changes in another thing (Moosbrugger & Kelava, 2012). We often use regression to make conclusions about cause and effect. There are different types of regression, but two of the most common are linear and logistic regression. These both assume that the relationship between the things being studied change together in a consistent way. When using regression, it is also assumed that the data is spread out evenly, error in measurement is normally distributed, and there are no extraneous factors that could affect the results. Regression is an important concept that forms the basis for more complex models in statistics and other fields of study (Little, 2013).
Little, T. D. (2013). Longitudinal structural equation model- ing. New York, NY: Guilford Press.
Moosbrugger, H., & Kelava, A. (2012). Testtheorie und Fragebogenkonstruktion. Heidelberg: Springer.