Description: Regression analysis is a statistical method used to understand the relationship between variables. This approach allows modeling the relationship between a dependent variable and one or more independent variables, facilitating prediction and trend analysis. Through techniques like linear regression, patterns and correlations can be identified, which is essential in various disciplines, from economics to biology. Regression analysis not only helps to understand how one variable affects another but also provides tools for informed decision-making based on data. Its ability to handle large volumes of information and its integration with machine learning techniques make it a key component in data science and artificial intelligence, where the goal is to optimize models and make accurate inferences.
History: Regression analysis was introduced by the statistician Francis Galton in the 19th century, who used the term ‘regression’ to describe the tendency of children to have characteristics closer to the mean than their parents. Subsequently, Karl Pearson developed linear regression in 1896, establishing the mathematical foundations for regression analysis. Throughout the 20th century, the method expanded and refined, integrating into various fields such as economics, biology, and engineering, becoming a fundamental tool in modern statistics.
Uses: Regression analysis 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 helps identify risk factors and evaluate treatment effectiveness. In marketing, it is used to analyze the impact of different strategies on sales. Additionally, in data science, it is fundamental for creating predictive models and optimizing processes.
Examples: A practical example of regression analysis is using linear regression to predict the price of a house based on characteristics such as size, location, and number of rooms. Another case is logistic regression analysis in medical studies to determine the likelihood of a patient developing a disease based on risk factors such as age, weight, and family history.
