Regression is a statistical method used to model relationships between variables and predict the values of one variable based on the values of other variables. It is one of the most common methods used in statistics and machine learning to analyze and predict data.
In regression analysis, usually, one variable, called the dependent variable, is predicted based on the values of one or more independent variables, called explanatory variables. The goal is to find a mathematical relationship that best describes the relationship between these variables and allows predicting the value of the dependent variable for new input values of the independent variable.
Regression can be applied to various types of data, including continuous and categorical variables. There are multiple types of regression analysis such as linear regression, logistic regression, polynomial regression, multivariate regression, and more. The selection of the appropriate type of regression takes into account the characteristics of the data and the goal of the analysis.
Statistical methods are used in regression analysis to estimate model parameters and evaluate their accuracy and relevance. The result of regression analysis is usually a mathematical model in the form of an equation that describes the relationship between variables.
Regression finds application in various fields such as economics, finance, marketing, medicine, social sciences, and others. Its uses include predicting future values, identifying influencing factors, testing hypotheses, and comparing groups.
It is important to remember that regression is a statistical model, so we should interpret its results with caution and take into account all relevant factors and constraints.