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


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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