The tolerance of a variable is defined as 1 minus the squared multiple correlation of this variable with all other independent variables in the regression equation. Therefore, the smaller the tolerance of a variable, the more redundant is its contribution to the regression (i.e., it is redundant with the contribution of other independent variables). If the tolerance of any of the variables in the regression equation is equal to zero (or very close to zero), the regression equation cannot be evaluated (the matrix is said to be ill-conditioned, and it cannot be inverted). For more information, see the Multiple Regression Model Definition dialog box topic.