Regression is a statistical method used to examine and measure the relationship between a dependent variable and one or more independent variables. It helps a researcher determine how changes in one variable are associated with changes in another variable and also helps in predicting the value of the dependent variable. In simple linear regression, the relationship between two variables is represented by an equation such as (Y=a+bX+e), where (Y) is the dependent variable, (X) is the independent variable, (a) is the intercept, (b) is the regression coefficient, and (e) represents the error term. Multiple regression extends this analysis to two or more independent variables. The classical linear regression model is based on several important assumptions. First, the model is assumed to be linear in its parameters. The dependent variable is expressed as a linear function of the explanatory variables and an error term. Second, the explanatory variables are assumed to be non-stochastic or, in practical applications, independent of the error term. This means that the explanatory variables should not be systematically related to factors included in the