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Case Studies

NIST/SEMATECH Section 1.4

10 worked EDA case studies from the NIST/SEMATECH Engineering Statistics Handbook. Each applies the full 4-plot diagnostic and complementary techniques to real-world datasets, demonstrating how to assess location, variation, randomness, and distributional assumptions.

Beam Deflections Case Study

Section 1.4.2

EDA case study analyzing NIST LEW.DAT dataset of 200 beam deflection measurements to demonstrate detection of periodic structure and randomness failure

Ceramic Strength Case Study

Section 1.4.2

EDA case study analyzing NIST JAHANMI2.DAT ceramic strength data to demonstrate analysis of a designed experiment with batch, lab, and machining factor effects

Fatigue Life of Aluminum Alloy Specimens

Section 1.4.2

EDA case study analyzing NIST BIRNSAUN.DAT fatigue life data to demonstrate probabilistic model selection for reliability engineering

Josephson Junction Cryothermometry

Section 1.4.2

EDA case study analyzing NIST SOULEN.DAT voltage count data to demonstrate univariate analysis with discrete integer measurements and mild assumption violations

Filter Transmittance Case Study

Section 1.4.2

EDA case study analyzing NIST filter transmittance data to demonstrate detection of non-randomness caused by a too-fast sampling rate in data acquisition

Heat Flow Meter 1 Case Study

Section 1.4.2

EDA case study analyzing NIST ZARR13.DAT heat flow meter calibration data to demonstrate a well-behaved univariate measurement process

Normal Random Numbers Case Study

Section 1.4.2

EDA case study analyzing 500 normal random numbers from a Rand Corporation publication to demonstrate assumption verification techniques

Random Walk Case Study

Section 1.4.2

EDA case study analyzing NIST RANDWALK.DAT dataset — a cumulative sum of uniform random numbers — to demonstrate detection of non-stationary location and violation of the fixed-location assumption

Standard Resistor Case Study

Section 1.4.2

EDA case study analyzing NIST standard resistor data to demonstrate detection of drift in location, non-constant variation, and non-randomness caused by seasonal humidity effects on measurement equipment

Uniform Random Numbers Case Study

Section 1.4.2

EDA case study analyzing NIST RANDU.DAT dataset to demonstrate detection of non-normal underlying distributions