ECE 69500 - Primer on Analysis of Experimental Data and Design of Experiments

Note:

This course will run the second 5 weeks of the semester in fall semesters. It is available through PEO, or online for WL students only. Students will have access to the instructor through a discussion forum and by office hours. If there is sufficient enrollment, weekly help sessions will be scheduled.

Course Details

Lecture Hours: 1 Credits: 1

Counts as:

Experimental Course Offered:

Fall 2018

Requisites by Topic:

Undergraduate physics, chemistry and mathematics

Catalog Description:

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.

Required Text(s):

None.

Recommended Text(s):

  1. Applied Statistics and Probability for Engineers , 3rd Edition , Montomery and Runger , Wiley , 2003
  2. Understanding Robust and Exploratory Data Analysis , D. C. Hoaglen, F. Mosteller and J.W. Tukey , Wiley Interscience , 1983
  3. Video lectures by Stuart Hunter (Available on Youtube)

Lecture Outline:

Hours Topics
.5 Course introduction
1 Review of Basic Statistical Concepts
1 Where do data come from: Big vs. Small Data
2 Collecting and Plotting Data: Principles of Robust Data Analysis
1 Physical vs. Empirical Distribution
2 Model Selection and Goodness of Fit
1 Scaling Theory of Design of Experiments
2 Statistical Theory of Design of Experiments
2 Machine Learning vs. Physics-based Machine Learning
.5 Course Summary
2 Homework and Solutions