Design of experiments (DoE) is one of the most important tools in the Six Sigma methodology. According to [2] it is the “jewel of quality engineering.” It is the essence of the Improve phase and the basis for the design of robust processes. However, DoE is not improvement itself. In fact, some authors include DoE within the Analyze phase of the design, measure, analyze, improve, and control (DMAIC) cycle; see, for instance,[82]. An adequate use of DoE will lead to the improvement of a process, but a bad design can result in wrong conclusions and engender the opposite effect: inefficiencies, higher costs, and less competitiveness. In this chapter, we present the foundations of DoE and introduce the essential functions in R to perform it and analyze its results. We will describe 2k factorial designs using a variation of the example described in [10]. This example is representative of how DoE should be used to achieve the improvement of a process in a Six Sigma way. The chapter is not intended as a thorough review of DoE. The idea is to introduce a simple model in an intuitive way. For more technical or advanced training, a number of references are given at the end of the chapter.