An Examining from Data Sourced Deviations in Modelling by use of Variance Analysis Method
In this study, outliers which can cause bias on models in a survey stage using observation values which constitute basement for conducting modelling have been
investigated. The processes important for estimation observation, transform to data, modelling from data, and to gain information from models so important by oneself. One of the most important risks we encountered is transformation on data by naturel randomness or derivation by people. If transformation on data is derived by naturel randomness, solution is easy and has to compulsory ultimately. Causes for bias are appeared by time series (ARIMA-Auto Regressive Integrated Moving Average) models, detection, and detection causes on them are modelled by multi factored experimental design with and significance levels have been investigated.
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