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Supplement

This supplement discusses the concept of fitting quadratic models to data for the best fit. It emphasizes the importance of minimizing errors by defining E (Error) as the difference between actual and predicted values, SSE (Sum of Square of Errors), and n (Number of Data Points), leading to the average error calculation.

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Supplement

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Presentation Transcript

  1. Supplement Fitting Quadratic Models to Data

  2. Best Fit • We have learned from previous sections that the best fitting curve for any given data, is the curve that gives the smallest average error.

  3. Where: • E = Error = Actual Given Value – Predicted Value from the model • SSE = Sum of the Square of the Errors • n = Number of Data Points

  4. Therefore, the average error is:

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