ECE 695E Data Analysis, Design of Experiment, ML Lecture 9A: DOE and Taguchi Experiments

Table of Contents:
00:00 Lecture 9A. DOE and Taguchi Experiments
01:46 Three representations of full factorial design
05:36 Uncorrelated linear graph (Field and Run views)
08:24 Taguchi table: How to determine n (Run view)
14:50 Aside: Taguchi Orthogonal Columns
16:48 Generating Taguchi (orthogonal) Arrays
19:55 Full Factorial to Taguchi Table
23:36 Main effect assuming no interactions (Run view)
24:43 Main effect with interactions (Run view)
27:45 The effect of B is now understood
29:17 Web Design: 4 Factor, 5 level
31:32 Conclusions
34:35 Review Questions

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