Two-Level Parallel Flats Split-Plot Designs for Nonregular Experiments

Chunyan Wang authored a dataset describing a class of clear split-plot designs constructed via a parallel flats structure. The dataset, last updated on 2026-05-12, includes files in PDF, DS_STORE, and M formats totaling 448.5 KB. These designs are proposed for experiments where some factors are hard to change, dividing factorial effects into orthogonal subspaces to simplify model selection.

Use Cases

• Designing efficient experiments based on the described split-plot structure where some factors are difficult to change.
• Comparing model selection performance based on the orthogonal subspaces created by the parallel flats design.
• Generating flexible run-size designs based on the straightforward construction method described.
• Analyzing confounding patterns in nonregular fractional factorial treatment structures.

Strengths

• The design class is described as flexible in run sizes and straightforward to construct.
• The designs are shown to divide factorial effects into two orthogonal subspaces, which simplifies model selection.
• The dataset is licensed under CC-BY-4.0, allowing for open use and modification.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset's small size (448.5 KB) suggests limited scope, likely containing design specifications rather than extensive experimental data.

Provenance

Source

Chunyan Wang via figshare

Collection Method

Likely contains theoretical design specifications and construction methods.

Freshness

Last updated 2026-05-12 13:14:51; freshness should be verified.