Definition of Regression

Regression is a statistical analysis technique used to model and assess the relationship between a dependent variable and one or more independent variables. It aims to find the best-fitting line or curve that represents the pattern of the data.

Dependent Variable

The dependent variable, also known as the outcome or response variable, is the variable that is being predicted or estimated in regression analysis. Its values depend on the values of the independent variables.

Independent Variables

Independent variables, also known as predictor or explanatory variables, are the variables that are used to predict or explain the values of the dependent variable. These variables can be continuous or categorical.

Regression Line or Curve

The regression line or curve represents the relationship between the dependent variable and the independent variables. It is fitted to the data points using various statistical techniques, such as ordinary least squares (OLS) regression, to minimize the difference between the predicted and actual values.

Best-Fitting Line or Curve

The best-fitting line or curve is the one that minimizes the sum of the squared differences between the observed and predicted values. It is chosen based on the principle of least squares, which aims to find the line or curve that provides the best approximation of the relationship between the variables.

Predictive Modeling

Regression analysis is often used for predictive modeling, where the relationship between the variables is used to predict the values of the dependent variable for new or future observations.