Regression, as we know, is the description of the dependent variable, Y, as a function of a bunch of independent variables, X1, X2, X3, etc.
Y = A0 + A1 X1 + A2 X2 + A3 X3 + …
A1, A2, etc., are coefficients representing the marginal effect of the respective X variable on the impact of Y while keeping all the other Xs constant.
But what happens when the X variables are correlated? – that is multicollinearity.
