1. A method of statistically modeling integrated circuits on a computer system using model parameters that are directly measurable and not directly measurable, the method comprising: wherein for the case that the model parameter is directly measurable, the method further comprises: determining a variance-covariance matrix for data to be modeled; conducting principal component analysis on the variance-covariance matrix; and creating a statistical model with an independent distribution for each principal component, allowing calculation of each individual model parameter as a weighted sum; and wherein for the case that the model parameter is not directly measurable, the method further comprises: measuring a variance and covariance of multiple electrical parameters for a plurality of different size transistors; conducting a principal component analysis for each size transistor; analyzing a sensitivity of each model electrical parameter to a plurality of model parameters for each size transistor and recording model parameter distributions; determining a set of partially correlated model parameter distributions that reproduce the recorded model parameter distributions; and conducting a linear regression to determine a geometric dependency of the model parameter distributions to generate the statistical model.