Simple Linear Model are models in which the behavior of a variable, Y, can be explained by a variable X: Y = f(X). If we consider that the relation f(), which connects Y with X, is linear, then it can be written as: y = β 0 + β 1x + ε. Since relations of the above type are seldom exact, but rather are approximations in which many variables of secondary importance have been omitted, we must include a random perturbation term, which reflects all factors - other than X - that influence the endogenous variable, but none of them is individually relevant. The parameters β 0 and β 1 determine the intercept and the slope of the line respectively. The intercept β 0 represents the predicted value of y when x = 0 . The slope β 1 represents the predicted increase in Y resulting from a one unit increase in X. The above expression reflects a linear relationship, and only one single explanatory variable, receiving the name of simple linear relation. H...