Accuracy (bias)
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is the difference between your measurement (mean value) and the truth. A good analyses should include bias estimates as well. Important: accuracy is not the same as uncertainty! e.g. can have an accurate measurement with a large uncertainty
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Bias (systematic) and Random Errors
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Systematic/bias errors are consistent and repeatable. Random errors arise from random fluctuations in the measurements
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To differentiate between the two:
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Random errors are reduced when experiment is repeated many times, get a mean value. It can be studied through statistical analysis of repeated measurements e.g. mean, standard deviation, and variance are often used.
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The systematic error (bias)
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will not change. It’s a very common type of error. It could be can be studied through intercomparisons, calibrations, and error propagation.
(source http://apollo.lsc.vsc.edu/classes/remote/)
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The overall quality of a model can reasonably be described through a single parameter, called the bias.
In qsaR you can just type the command bias()
, to get all the things automatically done for the all six models. The final results is showed in the figure bellow saved under the name “bias.png”.
* to produce the graph just type the command bias()