Validating quantitative data model Richtig flirten schule

It can lead to multi-perspective meta-interpretations. Notwithstanding validation and verification which are secondary, triangulation presents diverse perspectives on a specific issue using different investigative means to provide and enriched approach and outcome to the subject matter.

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In addition to physical observations, information may come from the literature or expert judgment that may incorporate historical data or known physical behavior.

Estimating prediction uncertainty requires the combination of computational models, physical observations, and possibly other information sources.

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The basic process includes identifying and representing key sources of uncertainty; identifying physical observations; experiments, or other information sources for the assessment; assessing prediction uncertainty; assessing the reliability or quality of the prediction; supplying information on how to improve the assessment; and communicating results.Exactly how this estimation is carried out can range from very direct, as in the weather forecasting example in Figure 5.1, to quite complicated, as described in the case studies in this chapter.In these examples, some physical observations are used to refine or constrain uncertainties that contribute to prediction uncertainty.However, FIGURE 5.1 Daily maximum temperatures for Norman, Oklahoma (left), and histograms of next-day prediction errors (right) using two prediction models.The top histogram shows residuals from the persistence model, predicting tomorrow’s high temperature with today’s high temperature.

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Triangulation facilitates validation of data through cross verification from more than two sources.

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