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Filter design research in the continuous-time domain is discussed with the goal of designing filters in the discrete-time domain. We look at some of the fundamentals of statistical filter design techniques including auto-regressive and auto-regressive-moving-average models. This book reflects a teaching paradigm called layering in which a concept is developed one step at a time by making gradual progressions from the simple to the complex. Rather than starting with a presentation of the final destination and then attempting to explain and justify its meaning, a concept is allowed to evolve with figures and equations by intentionally repeating material so that an incremental change becomes obvious. It is the intentional rewriting rather than erasing and writing that is at the core of layering. An additional benefit to this approach is that blocks in learning may be identified and clarified more easily.