Description: The regression model is a statistical process that allows estimating the relationships between variables, being fundamental in data analysis. Its main objective is to identify how a dependent variable is affected by one or more independent variables. This type of model is based on the premise that there is a functional relationship between the variables, which can be linear or non-linear. Regression models are powerful tools in predictive modeling, as they allow making inferences about future data based on patterns observed in historical data. Moreover, they are widely used in various disciplines, from economics to social sciences, and are essential for informed decision-making. The interpretation of the results of a regression model includes evaluating the statistical significance of the variables, as well as the model’s ability to explain the variability of the dependent variable. In summary, the regression model is a key technique in data analysis that helps unravel the complex relationships between different factors.
History: The concept of regression dates back to 1809 when British statistician Francis Galton introduced the term ‘regression to the mean’ while studying the relationship between the height of parents and their children. Over time, the regression model has evolved, incorporating more complex and diverse techniques, such as multiple regression and logistic regression, which were developed in the 20th century. The mathematical formalization of these models has allowed their application in a wide range of fields, from economics to healthcare.
Uses: Regression models are used in various fields, such as economics to predict market trends, in medicine to assess the effectiveness of treatments, and in engineering to optimize processes. They are also fundamental in survey analysis and market studies, where the aim is to understand the relationship between different variables.
Examples: A practical example of a regression model is analyzing the relationship between income and a company’s advertising spending, 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.
