In this chapter, we provide a review of statistical methods that are useful in conducting computer experiments. Our focus is primarily on the task of metamodeling, which is driven by the goal of optimizing a complex system via a deterministic simulation model. However, we also mention the case of a stochastic simulation, and examples of both cases are discussed. The organization of our review separates the two primary tasks for metamodeling: (1) select an experimental design; (2) fit a statistical model. We provide an overview of the general strategy and discuss applications in electrical engineering, chemical engineering, mechanical engineering, and dynamic programming. Then, we dedicate a section to statistical modeling methods followed by a section on experimental designs. Designs are discussed in two paradigms, model-dependent and model-independent, to emphasize their different objectives. Both classical and modern methods are discussed.