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