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Similar to any software system, the efficient development of a decision support system depends on the quality of its models (specification and design, in particular).
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We provide system architecture, model specification, and result analysis.
Topics include: probability, statistics, and sampling; selection, causation and causal inference; regression and model specification; and machine learning and big data.
Results are robust to different model specification, and consistent among different socioeconomic sub-populations.
These methods involve several steps: data acquisition, model specification and estimation.
Resource and metadata management in Museums, Archives and Research collections, with an emphasis on tools and practices for information and resource modeling, specification, and metadata migration.
In addition to handling multiple response models, several features have been integrated into PFIM 3.0 for model specification and optimisation.
The results are sensitive to several research design parameters, such as the type of data, model specification and econometric technique.
These tools focus on model specification and verification.
Section 3 presents the model specification and the estimation results.
See Section 2 for the model specification and Section 3.2 for the definitions of leisure.
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