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@ -16,7 +16,7 @@ There are three parts to a model:
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data, and `a_1` and `a_2` are parameters that can vary to capture
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different patterns.
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1. Next, you generate a __fitted model__ by finding the the model from the
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1. Next, you generate a __fitted model__ by finding the model from the
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family that is the closest to your data. This takes the generic model
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family and makes it specific, like `y = 3 * x + 7` or `y = 9 * x ^ 2`.
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