Statistics for Experimenters¶
Box, G. E. P., Hunter, & Hunter, J. S. (1978). Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building. John Wiley & Sons.
Cited by¶
4 citations across 4 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Blocking (In Experimental Design)
- 4. The generalization-versus-precision trade-off — blocking improves power and precision for the blocking-factor-at-hand but does so at cost of reduced generalizability across levels of the blocking variable unless treatment-by-block interactions are negligible (homogeneity of treatment effects)
This sourceStandard treatment of factorial and blocked designs, including treatment-by-block interaction and the precision-vs-generalizability trade-off.
- 4. The generalization-versus-precision trade-off — blocking improves power and precision for the blocking-factor-at-hand but does so at cost of reduced generalizability across levels of the blocking variable unless treatment-by-block interactions are negligible (homogeneity of treatment effects)
- Factorial Design
- 3. The main-effect efficiency via shared replication — a full 2×2×2 factorial with 8 runs estimates three main effects as precisely as three separate two-level OFAT experiments using 12 runs (one per factor), gaining the interaction estimates as a bonus from the same runs and resource investment
This sourceClassic introduction documenting the main-effect efficiency of factorials (e.g., a 2×2×2 with 8 runs estimating three main effects with interaction estimates as a bonus) and their role in managing multi-factor complexity.
- 3. The main-effect efficiency via shared replication — a full 2×2×2 factorial with 8 runs estimates three main effects as precisely as three separate two-level OFAT experiments using 12 runs (one per factor), gaining the interaction estimates as a bonus from the same runs and resource investment
- Randomization
- T6 — Sample-size adequacy versus achieving pragmatic scale. Randomized experiments require sufficient sample size to detect effects of policy-relevant or scientifically meaningful magnitude with adequate statistical power
This sourceBox Hunter Statistics Experimenters factorial randomization industrial DOE.
- T6 — Sample-size adequacy versus achieving pragmatic scale. Randomized experiments require sufficient sample size to detect effects of policy-relevant or scientifically meaningful magnitude with adequate statistical power
- Variability
- Performance variability in a service organization's case-resolution illustrates how variability thinking drives operational improvement
This sourceBox Hunter Statistics Experimenters factorial randomization industrial DOE.
- Performance variability in a service organization's case-resolution illustrates how variability thinking drives operational improvement
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Links previously used in the corpus¶
Before the registry existed this work was also linked 2 other ways.
- https://books.google.com/books/about/Statistics_for_Experimenters.html?id=QaFqAAAAMAAJ ×1
- https://www.google.com/books/edition/Statistics_for_Experimenters/QaFqAAAAMAAJ ×1
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