The theory of the most common tools for systematic planning of experiments and methods for the analysis of experimental results, is covered. ANOVA models are introduced as special cases of general linear models. Special emphasis is put on complete and fractional two level factorial designs. Response surface methods and their designs, and strategies for sequential design of experiments are included. Furthermore, more advanced models for the analysis of variance, with random and mixed effects are treated. Finally robust designs are introduced. In the later part of the course elementary linear and nonlinear regression analysis is addressed, including least square error and likelihood methods for estimating the parameters in the models. General Linear Models are introduced and methods for fitting, validating and testing in such models are discussed. Further, the fundamentals of one dimensional smoothing including splines and their use in construction of General Additive Models, are introduced. Some criteria for choosing such model parameters as well as practical aspects of analysis are discussed. Element 2 As support for choosing experimental design and analyzing data, throughout the course suitable statistical software is used.