Description: A regression model is a statistical tool used to estimate the relationships between variables. Through regression analysis, the aim is to understand how a dependent variable is affected by one or more independent variables. This model allows for predicting the value of the dependent variable based on known values of the independent variables. There are different types of regression models, such as linear regression, which assumes a linear relationship between variables, and logistic regression, which is used for categorical outcomes. Regression models are fundamental in supervised learning, where a model is trained with labeled data to make predictions about new data. Their ability to identify patterns and relationships in large datasets makes them an essential tool across various disciplines, from economics to biology, engineering, and social sciences.
History: The concept of regression was introduced by Francis Galton in 1886, who observed that the characteristics of children tend to ‘regress’ towards the mean of their parents’ population. Later, Karl Pearson formalized regression and correlation analysis, establishing the mathematical foundations still used today. Throughout the 20th century, the development of statistics and computing allowed for the evolution of more complex models, such as multiple regression and nonlinear regression.
Uses: Regression models are used in various fields, such as economics to predict economic growth, in medicine to assess the relationship between treatments and health outcomes, and in marketing to analyze the impact of different variables on sales. They are also common in scientific research to establish relationships between variables and in engineering to optimize processes.
Examples: A practical example of a regression model is the use of linear regression to predict the price of a house based on features such as size, location, and number of rooms. Another example is logistic regression, which is used in medical studies to predict the likelihood of a patient developing a disease based on risk factors.
