nonlinear fitting lecture
TRANSCRIPT
![Page 1: Nonlinear Fitting Lecture](https://reader035.vdocuments.us/reader035/viewer/2022071817/55adbbcf1a28ab96798b4586/html5/thumbnails/1.jpg)
The importance of using the nonlinear equation rather than the linearly transformed equation.
![Page 2: Nonlinear Fitting Lecture](https://reader035.vdocuments.us/reader035/viewer/2022071817/55adbbcf1a28ab96798b4586/html5/thumbnails/2.jpg)
Visualizing how the algorithm arrives at the minimizedsum of squared error terms by trying different combinationsof the parameters (A and B in this case)
![Page 3: Nonlinear Fitting Lecture](https://reader035.vdocuments.us/reader035/viewer/2022071817/55adbbcf1a28ab96798b4586/html5/thumbnails/3.jpg)
How should the data in a “residuals” plot appear?
![Page 4: Nonlinear Fitting Lecture](https://reader035.vdocuments.us/reader035/viewer/2022071817/55adbbcf1a28ab96798b4586/html5/thumbnails/4.jpg)
The importance of providing reasonable initial “guesses”of the parameters of interest
![Page 5: Nonlinear Fitting Lecture](https://reader035.vdocuments.us/reader035/viewer/2022071817/55adbbcf1a28ab96798b4586/html5/thumbnails/5.jpg)
The error estimates of the parameters are helpful, but theyshould be regarded as underestimates of the true error. TheSolver algorithm in Excel does not provide error estimates
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An “outlier” has more influence in determining the best-fit line when there are relatively fewer data points
![Page 7: Nonlinear Fitting Lecture](https://reader035.vdocuments.us/reader035/viewer/2022071817/55adbbcf1a28ab96798b4586/html5/thumbnails/7.jpg)
An “outlier” has more influence in determining the best-fit line when there are relatively fewer data points