ECE 695E Data Analysis, Design of Experiment, ML Lecture 8: Statistical Design of Experiments

Table of Contents:
00:00 Lecture 8. Statistical Design of Experiments
00:24 The story so far …
04:32 Design of Experiments
06:40 Philosophical shift with DOE
07:33 Problem definition
09:25 Definition of terms
12:13 Puzzle Analogy: Many factors, 2 levels
19:12 Outline
19:58 7 Factor, 2 level: One factor at a time
23:13 7 Factor, 2 Level: Full factorial analysis
26:44 The problem with one-at-a-time approach
29:30 Uncorrelated main effect (forward/backward)
36:04 Taguchi orthogonal array (L8 array)
40:16 Orthogonal measurements (uncorrelated)
42:03 Outline
42:11 Correlated effect & level factor
44:25 Correlated effect & level factor
46:29 Correlated effect & level factor
47:04 How to fix for correlation
48:09 Aside: correlation linear graph
49:06 Main effect and interactions

This course will provide the conceptual foundation so that a student can use modern statistical concepts and tools to analyze data generated by experiments or numerical simulation. We will also discuss principles of design of experiments so that the data generated by experiments/simulation are statistically relevant and useful. We will conclude with a discussion of analytical tools for machine learning and principle component analysis. At the end of the course, a student will be able to use a broad range of tools embedded in MATLAB and Excel to analyze and interpret their data.

Topics Covered:
Review of Basic Statistical Concepts
Where do data come from: Big vs. Small Data
Collecting and Plotting Data: Principles of Robust Data Analysis
Physical vs. Empirical Distribution
Model Selection and Goodness of Fit
Scaling Theory of Design of Experiments
Statistical Theory of Design of Experiments
Machine Learning vs. Physics-based Machine Learning