Linear regression is a machine learning technique that is used to establish a relationship between a scalar response and one or more explanatory variables. The first scaler response is called a target or dependent variable while the explanatory variables are known as a response or independent variables. When more than one independent variable is used in the modeling technique we call it multiple linear regression.
Independent variables are known as explanatory variables as they can explain the factors that control the dependent variable along with the degree of the impact. This can also be calculated using ‘parameter estimates’ or ‘coefficients’.