An important step in today's integrated circuit (IC) manufacturing is optical proximity correction (OPC). In model-based OPC, masks are systematically modified to compensate for the nonideal optical and process effects of an optical lithography system. The polygons in the layout are fragmented, and simulations are performed to determine the image intensity pattern on the wafer. If the simulated pattern on the wafer does not match the desired one, the mask is perturbed by moving the fragments. This iterative process continues until the pattern on the wafer matches the desired one. Although OPC increases the fidelity of pattern transfer to the wafer, it is quite CPU intensive due to the simulations performed at each iteration. In this paper, linear regression techniques from statistical learning are used to predict the fragment movements. The goal is to reduce the number of iterations required in model-based OPC by using a fast, computationally efficient linear regression solution as the initial guess to model-based OPC. Experimental results show that fragment movement predictions via linear regression model significantly decrease the number of iterations required in model-based OPC, thereby decreasing the product development time in IC design and manufacturing.