Regression: Definition, History, and Characteristics
Regression is a statistical technique used to analyze the relationship between a dependent variable and one or more independent variables. It is frequently used in economics, psychology, and other social sciences to measure the strength of the relationship between variables. By applying regression analysis to data, researchers can gain insights into trends and patterns, and even make predictions about future behavior or outcomes.
Definition
Regression is a type of statistical modeling used to identify and quantify the degree of relationship between two or more variables. The dependent variable is the one that is being predicted or explained by the independent variables. For example, if a researcher wants to measure the impact of income on the happiness of individuals, then income would be the independent variable and happiness would be the dependent variable.
History
Regression analysis can be traced back to the work of the 18th century mathematician and scientist, Carl Friedrich Gauss. Gauss developed a linear regression model to determine the relationship between two variables. Gauss’s work was later refined by the 19th century statistician, Adolphe Quetelet. Quetelet developed the concept of least squares regression, which is still used today.
Characteristics
Regression is a versatile tool that can be used to analyze data in a variety of ways. It can be used to identify the strength of the relationship between two variables, to model the behavior of a system over time, or to make predictions about future behavior. Regression analysis can also be used to identify outliers or influential points in a data set, and to test hypotheses about the relationships between variables.
References
Berry, W. D., & Feldman, S. (1985). Multiple regression in practice. Sage.
Gauss, C. F. (1809). Theoria motus corporum coelestium. Königliche Gesellschaft der Wissenschaften.
Quetelet, A. (1835). Recherches sur le calcul des probabilites. Bachelier.
Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Pearson Education.