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Explorative data analysis
Time cycle analyses
Missing data analysis
Plausibility checks
Prognosis models

Statistical analyses

We have been carrying out statistical analyses for many years with S+, IMSL, RATS and TSP, among others. Examples include:

Explorative data analysis
Explorative data analysis (EDA) has gained in importance in recent years as a result of the increasing availability of powerful computers and the graphic capabilities of computers at the work stations of data analysts. EDA can be used to track down interrelated structures in multivariant data sets, to investigate the distribution of variables and the type of relationship that may exist between them.

Analyses of air pollutant concentration time cycles
The various cycles (e.g. annual, weekly, daily) in the time cycles can be separated from the stochastical elements and modelled using statistical methods. Time cycle analytical models can be used, for example, as prognosis models or for analysing causes.

Missing data analysis - MDA
Time cycle analysis and regression analysis methods allow the flow of dataarriving at an air pollution measurement node, for example, to be interpreted and modelled as the output of a spatial/temporal stochastical process. An optimal strategy for replacing missing values can then be derived from this spatial/temporal model.

Multivariant spatial/temporal plausibility checks
Multivariant spatial/temporal plausibility checks also involve the idea behind the spatial/temporal stochastical process. Here, ex-post prognoses are made, based on the statial/temporal model. If the value obtained deviates from the predicted value by a defined amount, the value is marked out as a possible error.

Statistical prognosis model
Prognosis models can be developed on the basis of statistical model structures. These models can involve transfer functions, binary decision trees or regression models. Statistical models are advantageous because one can easily define the uncertainty range of the predictions.

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