Description: Statistical regression is a set of statistical processes used to estimate the relationships between variables. This approach allows modeling the relationship between a dependent variable and one or more independent variables, facilitating the understanding of how changes in independent variables affect the dependent variable. Regression can be linear, where a linear relationship between variables is assumed, or nonlinear, where more complex relationships are considered. Additionally, statistical regression provides tools to evaluate the quality of the model through metrics such as the coefficient of determination (R²) and standard errors. Its relevance lies in its ability to make predictions and inform decisions based on data, making it a fundamental tool in various disciplines, from economics to biology. In summary, statistical regression is a powerful method that allows researchers and analysts to unravel patterns and relationships in data sets, facilitating the interpretation and prediction of complex phenomena.
History: Statistical regression was introduced by British statistician Francis Galton in the 19th century, who studied the relationship between the height of parents and that of their children. Galton used the term ‘regression’ to describe the phenomenon where children tend to be closer to the average height than their parents, hence the term ‘regression to the mean.’ Later, Karl Pearson formalized the concept and developed correlation and linear regression. Throughout the 20th century, regression expanded and diversified, incorporating more complex methods and applications in various fields such as economics, biology, and engineering.
Uses: Statistical regression is used in a wide variety of fields. In economics, it is applied to predict market behavior and analyze the relationship between economic variables. In medicine, it is used to identify risk factors and predict treatment outcomes. In social sciences, it helps understand the relationships between demographic variables and behaviors. It is also employed in marketing to analyze the impact of advertising campaigns on sales.
Examples: An example of statistical regression is the analysis of the relationship between income and advertising expenditure of a company, where linear regression can be used to predict how an increase in advertising spending might influence revenues. Another example is the use of logistic regression in medical studies to predict the likelihood of a patient developing a disease based on risk factors such as age, weight, and family history.
