CLASSICAL LINEAR REGRESSION (CLR) MODEL

(Statistics) The standard for the Ordinary Least Squares (OLS), or Regression Analysis model. The CLR Model has five basic assumptions: (1) Linearity: The dependent variable, or the variable to be explained or forecasted, can be calculated as a linear function of a specific set of independent, or explanatory variables; (2) Randomness of Disturbance Terms: The expected value of the disturbance term, that is the term showing the differences between the model's estimated values and the actual observed values, is zero; (3) Uncorrelated Disturbance Terms: The disturbance terms all have the same variance and are not correlated with each other (see Serial Correlation); (4) Data Conformity: The observations on the independent variable can be considered fixed in repeated samples, i.e., it is possible to repeat the sample with the same independent variables; (5) Sample Size and Selection: The number of observations is greater than the number of independent variables and that there are no linear relationships, i.e., no significant correlations, between the independent variables (see Multicollinearity).