Validating input output transformation Nude reall fecam
To perform the test a number n statistically independent runs of the model are conducted and an average or expected value, E(Y), for the variable of interest is produced.
Then the test statistic, t, the model needs adjustment.
One approach that is commonly used is to have the model builders determine validity of the model through a series of tests. For example, the number of servers in a fast food drive through lane and if there is more than one how are they utilized?
Do the servers work in parallel where a customer completes a transaction by visiting a single server or does one server take orders and handle payment while the other prepares and serves the order.
These include, but are not limited to, having the model checked by an expert, making logic flow diagrams that include each logically possible action, examining the model output for reasonableness under a variety of settings of the input parameters, and using an interactive debugger.
Due to that, a model should be verified and validated to the degree needed for the models intended purpose or application.
In the context of computer simulation, verification of a model is the process of confirming that it is correctly implemented with respect to the conceptual model (it matches specifications and assumptions deemed acceptable for the given purpose of application).
The validation test consists of comparing outputs from the system under consideration to model outputs for the same set of input conditions.
Data recorded while observing the system must be available in order to perform this test.
There are many approaches that can be used to validate a computer model. Assumptions made about a model generally fall into two categories: structural assumptions about how system works and data assumptions.