Nonlinear Fitting Lecture

1. The importance of using the nonlinear equation rather
than the linearly transformed equation.

2. Visualizing how the algorithm arrives at the minimized
sum of squared error terms by trying different combinations
of the parameters (A and B in this case)

3. How should the data in a “residuals” plot appear?

4. The importance of providing reasonable initial “guesses”
of the parameters of interest

5. The error estimates of the parameters are helpful, but they
should be regarded as underestimates of the true error. The
Solver algorithm in Excel does not provide error estimates

6. An “outlier” has more influence in determining the best-fit
line when there are relatively fewer data points

7. An “outlier” has more influence in determining the best-fit
line when there are relatively fewer data points