The Mathematics of Experimental Design: Incomplete Designs and Latin Squares

1970 ◽  
Vol 7 (1) ◽  
pp. 127
Author(s):  
Bertram Schoner ◽  
S. Vajda
Keyword(s):  
Experimental Design ◽  
Latin Squares ◽  
Incomplete Designs

The intake, digestibility and rate of passage of whole crop wheat and grass silage by growing heifers

1993 ◽  
Vol 1993 ◽  
pp. 98-98 ◽  
Author(s):  
J. Hill ◽  
J.D. Leaver
Keyword(s):  
Chemical Composition ◽  
Experimental Design ◽  
Latin Squares ◽  
Brown Swiss ◽  
Grass Silage ◽  
Holstein Friesian ◽  
Voluntary Intake ◽  
Kinetics Of ◽  
Selection Of

The aims of the experiment were to determine the intake, digestibility and particle passage kinetics of urea treated (40 g urea / Kg DM) whole crop wheat (UW), grass silage (GS) and a 0.5:0.5 UW : GS mixture (UG) on a DM basis.The chemical composition of GS and UW were respectively, TDM (gTDM/Kg) 227, 648; NDF (g/Kg DM) 528,426; ADF (g/Kg DM) 346, 285; Ash (g/Kg DM) 74, 62; Starch (g/Kg DM) 3, 236; Nitrogen (g/Kg DM) 24.2, 30.2; Ammonia N (g/Kg TN) 91, 382; pH 3.9, 8.5; NCD (g/Kg DM) 681, 642 respectively.The UG was mixed daily, in an attempt to reduce selection of individual components by the animals.The experimental design was based on two 3 x 3 latin squares with three animals per square and three periods of three weeks duration. Six Holstein Friesian x Brown Swiss pregnant heifers (mean liveweight 400 Kg) were used to assess (during the last 7 days of each period) the voluntary intake, digestibility and particle passage kinetics of the three forages.

Identifying suboptimalities with factorial model comparison

2018 ◽  
Vol 41 ◽  
Author(s):  
Wei Ji Ma
Keyword(s):  
Experimental Design ◽  
Model Comparison ◽  
Process Models ◽  
Multiple Forms ◽  
Factorial Experimental Design ◽  
Factorial Model ◽  
Multiple Components ◽  
The Many

AbstractGiven the many types of suboptimality in perception, I ask how one should test for multiple forms of suboptimality at the same time – or, more generally, how one should compare process models that can differ in any or all of the multiple components. In analogy to factorial experimental design, I advocate for factorial model comparison.

Combining integrated systems-biology approaches with intervention-based experimental design provides a higher-resolution path forward for microbiome research

2019 ◽  
Vol 42 ◽  
Author(s):  
J. Alfredo Blakeley-Ruiz ◽  
Carlee S. McClintock ◽  
Ralph Lydic ◽  
Helen A. Baghdoyan ◽  
James J. Choo ◽  
...  
Keyword(s):  
Systems Biology ◽  
High Resolution ◽  
Experimental Design ◽  
Integrated Systems ◽  
Microbiome Research ◽  
Constructive Criticism

Abstract The Hooks et al. review of microbiota-gut-brain (MGB) literature provides a constructive criticism of the general approaches encompassing MGB research. This commentary extends their review by: (a) highlighting capabilities of advanced systems-biology “-omics” techniques for microbiome research and (b) recommending that combining these high-resolution techniques with intervention-based experimental design may be the path forward for future MGB research.

Statistics and parallaxes

1978 ◽  
Vol 48 ◽  
pp. 7-29
Author(s):  
T. E. Lutz
Keyword(s):  
Experimental Design ◽  
Statistical Methods ◽  
Review Paper ◽  
Systematic Errors ◽  
Past Experience ◽  
Random Errors ◽  
Trigonometric Parallaxes ◽  
Systematic And Random Errors

This review paper deals with the use of statistical methods to evaluate systematic and random errors associated with trigonometric parallaxes. First, systematic errors which arise when using trigonometric parallaxes to calibrate luminosity systems are discussed. Next, determination of the external errors of parallax measurement are reviewed. Observatory corrections are discussed. Schilt’s point, that as the causes of these systematic differences between observatories are not known the computed corrections can not be applied appropriately, is emphasized. However, modern parallax work is sufficiently accurate that it is necessary to determine observatory corrections if full use is to be made of the potential precision of the data. To this end, it is suggested that a prior experimental design is required. Past experience has shown that accidental overlap of observing programs will not suffice to determine observatory corrections which are meaningful.

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