Statistics for Experimenters

Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery. Wiley.

Type

Book

Intellectual base

Textbook

Year

2005

Link

https://www.wiley-vch.de/en/areas-interest/mathematics-statistics/statistics-16st/applied-probability-statistics-16st2/experimental-design-16st22/statistics-for-experimenters-978-0-471-71813-0

Cited from

data_science, statistics_experimental_design

Cited by

3 citations across 3 artifacts.

Each citation links to the sentence it supports in the citing article.

Primes

• Curse Of Dimensionality
  • In experimental design, factorial designs with many factors require exponentially many runs, which is why fractional factorial and response-surface methods exist.

    This sourceFactorial designs require exponentially many runs in factor count, motivating fractional-factorial and response-surface methods.

• Experimental Design
  • It separates what we want to know (the causal estimand) from how we will know it (the identification strategy and data collection mechanism), a structure Box, Hunter, and Hunter (2005) make explicit in their classic treatment of statistics for experimenters.

    This sourceclassic treatment articulating the experimental cycle from causal question through treatment assignment, measurement protocol, and statistical analysis, separating what we want to know from how we will know it.

Mechanisms

• Controlled Surface Swatch Series
  • Its failure mode is the limit of one-factor-at-a-time experimentation

    This sourceExplains that one-factor-at-a-time experimentation can miss interactions that factorial designs detect.

Verification

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Links previously used in the corpus

Before the registry existed this work was also linked 1 other way.

• https://www.wiley.com/en-us/Statistics+for+Experimenters%3A+Design%2C+Innovation%2C+and+Discovery%2C+2nd+Edition-p-9780471718130 ×2

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