A novel derivation of the control law enables the link to minimum variance benchmarking to be explored and exploited.
This work is concerned with the minimum variance (MV) benchmark estimation without a prior knowledge of time-delay.
This research is part of a larger study titled: 'Paramedicine clinical placement duration and quality variance: An international benchmarking study' [ 5].
Inter-examiner reliability was evaluated using fixed-effects analysis of variance in comparison with benchmark values (GC).
In 2010, 79% of Canadians in need of this procedure obtained surgery within the benchmark period, with inter-provincial variance ranging from 42-89% [ 24].
The factor analysis also identified five factors within the integration subscale, which accounted for 53.3% of the variance: Hour Replacement, Intensive Integration, Leveling, Preparation, and Benchmarking.
This requires a Monte Carlo code capable for full sensitivity and uncertainty calculations along with high-quality, co-variance data as well as suitable integral benchmark experiments for qualification and validation purposes.
An interpretation of effect size in the context of analysis of variance has been provided in terms of benchmarks where 0.1 is a small effect, 0.25 is a medium effect and 0.4 is a large effect [ 31].
Both growth and value managers show minimal variance from each other and from their relevant benchmark indexes.
Co-linearity diagnostics calculated with Stata indicated no major concerns with co-linearity, with no variance inflation factor scores above three, far off the benchmark of 10 at which co-linearity warrants further investigation (Chen et al., 2009).
The use of the generalised minimum variance control law for control loop performance assessment and benchmarking is considered.
