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The hypothesis-testing procedure involves using sample data to determine whether or not H0 can be rejected.
We've also provided colleagues at Cornell with sample data to help with their development of a linked data editor.
Design-based inference requires the sample data to be obtained via a probability sampling design.
The procedures use the pilot sample data to estimate an "optimal" group size and then proceed to determine the number of groups by a stopping rule.
Simulation was done by using sequential indicator simulation (SIS), which generated 1000 realizations of spatial distributions of the heavy metals, based on existing sample data, to represent the real distributions of heavy metals.
This study highlights the remarkable effectiveness of process based, spatially lumped modelling with commonly available monthly stream sample data, to elucidate high resolution catchment function, when appropriate calibration methods are used that correctly handle the inherent uncertainties.
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These simulations used sampled data to predict phylogenetic diversity for farmland (yellow; Figure 2) and the neighbouring forest (green; Figure 2) to create a thought experiment.
The use of sampled data to estimate properties of a network is a common practice; however, current network generation methods do not account for the uncertainty in the estimates due to sampling.
We are in the early phases of planning a curation strategy, but our general objectives for the dataset are to digitize it, add metadata, convert sampling data to actionable formats, and make it all public.
A dynamic event-triggered communication scheme is introduced to detect whether or not transmit the newly sampled data to the quantizer for different jumping modes.
The proposed approach provides an efficient way to use noisy, non-uniformly sampled data to determine a reliable, low-order continuous time model.
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