A well-designed experiment is an efficient method of learning about the world. Because experiments in the field and in the laboratory cannot avoid random error, statistical methods are essential for their efficient design and analysis. In this book, the fundamentals of optimum experimental design theory are presented. In the first part of the part of the book, the advantages of a statistical approach to the design of experiments are discussed, and the ideas of models, least squares fitting, and optimum experimental designs are introduced. The second part presents a more detailed discussion of the general theory of optimum design and an evaluation of various criteria that may be appropriate for designing experiments. Specific experiments are detailed and algorithms for the construction of designs are given. Each chapter is a self-contained topic, illustrated with examples drawn from science and engineering. Little previous statistical knowledge is assumed, and the derivation of mathematical results has been avoided. This book should be of interest to everyone concerned with designing efficient experiments in the laboratory or in the industry.