Data Measurement & Analysis

Instructor: Dr. Phillip Kreth

Textbook: References:

​• Random Data: Analysis and Measurement Procedures, Fourth Edition, by J. S. Bendat and A. G. Piersol, John Wiley & Sons, 2010.
• Academic or standard version of MATLAB ​

Course Content: This course will cover various tools used in the analysis of random data, including classifications of random data, statistics principles, probability density & distribution functions, moments & characteristic functions, and spectral & correlation analyses. Additional topics include data acquisition fundamentals, bias & random error estimations, input-output system models, measurement examples, and advanced decomposition & analysis techniques (time permitting).

The objective of this course is to explore the application of time- and frequency-domain analysis techniques to time series data. The course builds upon the fundamental principles introduced in statistics and numerical & experimental methods and is intended for graduate students working in a research environment. The course emphasizes the understanding, application, and interpretation of various methods. Statistical functions (e.g., probability density functions, moments, etc.), time- domain (e.g., auto- and cross-correlation, covariance) functions, and frequency-domain (e.g., auto- and cross-spectra, coherence) methods are derived and applied to practical engineering problems. The application of the methods presented in the course will be illustrated via homework problems that utilize MATLAB for data reduction, analysis, and presentation. Exams will also be used to reinforce important aspects of these methods.

You can view a sample video of the course below. You must purchase the course to unlock all of the lecture sessions.