Least Squares Estimate and Projectivity

This lecture covers the least squares estimate for a system, including examples and solutions. It also delves into the concept of projectivity, discussing different factors affecting performance optimization, such as diet, saddle height, and pressure. The instructor explains the Hadamard plan, aliasing effects, and full foldover in experimental design, providing insights into de-aliasing techniques and inference of effects. The lecture concludes with an effects diagram using a full plan to identify interactions and non-aliased factors.