Short Courses

Summary

Statistical Engineering Division staff members offer short courses at NIST, at conferences open to the public, and at other government agencies. The courses include uncertainty analysis, design of experiments, and other topics in statistical metrology. The audiences for these courses have broad experience and knowledge in statistical metrology. The courses are designed to provide practical experience with the featured statistical methods and software tools.

Description

Topics, along with a description, for previously-taught classes are below. New topics are being considered. If you have a suggestion for a course, or would like to request that a course be taught, please contact Julia Sharp (julia.sharp [at] nist.gov (julia[dot]sharp[at]nist[dot]gov)).

Fundamentals of uncertainty analysis

This short course covers many aspects of the propagation of uncertainty using the methods outlined in the JCGM Guide to the Expression of Uncertainty in Measurement. Exercises and hands-on applications will use functions for uncertainty analysis from the free software package, metRology, written for the open-source R statistical computing environment. The functions will be accessed via an Excel graphical user interface that is available as a free add-in.

• Topics Covered:
  • Importance of uncertainty analysis
  • Different statistical approaches for uncertainty analysis
  • Essentials of the GUM approach
  • Measurement functions
  • Type A and Type B methods for evaluating standard uncertainties
  • Degrees of freedom
  • Sensitivity coefficients
  • Propagation of standard uncertainties
  • Effective degrees of freedom
  • Expanded uncertainties
  • Software for propagation of uncertainty
  • Interpretation of results

Experimental design

Experiment design is a systematic, rigorous, data-based approach to scientific/engineering problem-solving. The goal of experiment design is to generate valid, unambiguous, and reproducible conclusions about the scientific/engineering process of interest--and to do so in a time- and cost-efficient fashion. Statistically designed experiments--especially "orthogonal" designed experiments--markedly enhance scientific insight, rigor, and robustness, while saving both time and money. Such designs have already benefited a variety of NIST projects across the variety of data types: 1) within-laboratory, 2) across-laboratory (interlab), and 3) the increasingly important virtual/computational.

• Topics covered:
  • Problems
  • Problem-Solving Framework
  • Problem Organization & Classification
  • Experiment Design Principles
  • Comparative/Robust Designs
  • Screening/Sensitivity Designs—Full Factorial
  • Screening/Sensitivity Designs—Fractional Factorial
  • Optimization and Regression Designs

Bayesian Methods for Quantifying Measurement Uncertainty

This course covers the basic tool set necessary for applying Bayesian methods, from probability distributions and computation to their use with measurement equations. The short course will provide participants with a working knowledge of these tools, examples of NIST projects where they were used successfully, and scientific and statistical insight into the interpretation of results. Computational tools will be demonstrated throughout the class. The tools we will use are R (https://www.r-project.org/), JAGS (http://mcmc-jags.sourceforge.net/), Rstudio (https://www.rstudio.com/), and the R package rjags (https://cran.r-project.org/web/packages/rjags/index.html).

• Topics covered
  • Introduction to probability
  • Simple models
  • Prior distributions
  • How many samples?
  • Hierarchical models
  • Computation 

Stat Methods for Forensic Sciences

For details, contact julia.sharp [at] nist.gov (julia[dot]sharp[at]nist[dot]gov) 

Introductory statistics

For details, contact julia.sharp [at] nist.gov (julia[dot]sharp[at]nist[dot]gov)