## Courses

## nanoHUB-U: The Science, Art, and Practice of Analyzing Experimental Data and Designing Experiments

**Lecture 1: Collecting and Plotting Data**

- Origin of data, Field Acceleration vs. Statistical Inference
- Nonparametric information
- Preparing data for projection: Hazen formula
- Preparing data for projection: Kaplan formula

**Lecture 2: Physical vs Empirical Distribution**

- Physical vs. empirical distribution
- Properties of classical distribution function
- Moment-based fitting of data

**Lecture 3: Model Selection/Goodness of Fit**

- The problem of matching data with theoretical distribution
- Parameter extractions: Moments, linear regression, maximum likelihood
- Goodness of fit: Residual, Pearson, Cox, Akika

**Lecture 4: Scaling Theory of Design of Experiments**

- Buckingham PI Theorem
- An Illustrative Example
- Recall the scaling theory of HCI, NBTI, and TDDB

**Lecture 5: Design of Experiments**

- Single factor and full factorial method
- Orthogonal vector analysis: Taguchi/Fisher model
- Correlation in dependent parameters