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Regression models are statistical tools used to estimate the relationship between a dependent variable and one or more independent variables. The goal of a regression model is to predict the value of the dependent variable based on the known values of the independent variables. These models are widely used in various fields, including economics, finance, social sciences, and engineering, for forecasting, understanding relationships, and making informed decisions.

There are several types of regression models, with linear regression being the most common. In simple linear regression, a straight line is fitted to the data points, representing the linear relationship between a single independent variable and the dependent variable. Multiple linear regression extends this concept to include multiple independent variables. Other types of regression models, such as polynomial regression, logistic regression, and time series regression, are used for different types of data and relationships.

The process of building a regression model typically involves selecting relevant variables, fitting the model to the data, and evaluating its performance. The goodness-of-fit is assessed using various statistical measures, such as R-squared, which indicates the proportion of variance in the dependent variable that is explained by the independent variables. Residual analysis is also crucial to check for assumptions and identify potential issues with the model. Regression models are powerful analytical instruments, but their effectiveness depends on the quality of the data and the appropriate selection of the model type.