Statistics for experimenters : design, innovation, and discovery

By G. Box, J. Hunter, William G. Hunter. 2005

96 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/72a9aa40014cdffb2dd3df6447c3d62ca237adaa

Citations in the corpus

• Effects of different corn silage: Alfalfa silage ratios and full fat extruded soybeans on milk composition, conjugated linoleic acids content in milk fat and performance of dairy cows (2010, unknown): “Response surface methodology (RSM) is a combination of mathematical and statistical techniques that is commonly employed for designing and evaluating the effects of experimental factors for optimizing the conditions of these factors for desirable responses (Box et al., 2005; Kammoun et al., 2008).”
• In Pursuit of Balance: Randomization in Practice in Development Field Experiments (2008, unknown): “By eliminating particular sources of differences between groups, stratification (aka blocking) can increase the sensitivity of the experiment, allowing it to detect smaller treatment differences than would otherwise be possible (Box et al, 2005).”
• Online controlled experiments at large scale (2013, unknown): “Multivariate Tests (MVT) evaluate the impact of multiple variables that could interact, and are the subject of a rich statistical literature [20] and many buzzword-compliant brochures of product vendors.”
• Comparison of response surface methodology (RSM) and artificial neural networks (ANN) towards efficient extraction of artemisinin from Artemisia annua (2014, unknown): “…design (CCD) was selected to determine he experimental conditions as the inclusion of axial experimenal points allow for a larger spread of conditions to be examined, hich is beneficial when the required complexity of model is not nown for accurate predictions to be made (Box et al., 2005).”