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To a first approximation, the transformation applied to model data corrects the model distribution for bias and spread with respect to observation distributions.
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Update: Corrected the model name.
The above four methods were used to predict the balance model error and correct the model with it.
The estimated parameters are then used to correct the model of the faulty system and to reconfigure the controller.
To correct the model for confounding, changes in the estimates were checked when removing variables.
We confirmed some of these discrepancies through a literature search and subsequently corrected the model.
If a Durbin-Watson test result was significant, we corrected the model for autocorrelated errors.
We examined each inconsistency between experimental results and model predictions, and corrected the model when sufficient justifying evidence could be collected.
We corrected the models for the serially autocorrelated nature of the observations.
Flipping the hand corrects the handedness of the model.
This participant said he got confused when he had to correct the reuse model.
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