Linear Regression studies linear, additive relationships between the response variable and explanatory variable(s). These variables must be continuous.
Let Y denote the “dependent” variable whose values you wish to predict, and let X1, …,Xk denote the “independent” variables from which you wish to predict it, with the value of variable Xi in period t (or in row t of the data set) denoted by Xit. Then the equation for computing the predicted value of Yt is:
Assumptions
- linearity and additivity of the relationship between dependent and independent variables
- statistical independence of the errors
- homoscedasticity (constant variance) of the errors
- normality of the error distribution