I am fitting curves to my data to extract one parameter. However, I am unsure what the certainty of that parameter is and how I would calculate / express its $95$% confidence interval.
Say for a dataset containing data that exponentially decays, I fit a curve to each dataset. Then the information I want to extract is the exponent $b$. I know the values of $t$ and the value of $a$ I am not interested in (thats a variable that comes from the population, not the process Im trying to model).
I use non-linear regression to fit these parameters. However I dont know how to compute the $95$% confidence interval for any method, so broader answers are welcome as well.
$$f= a\cdot e^{-bt}$$
Once I have my value for $b$, how do I calculate its $95$% confidence interval? Thanks in advance!
Best Answer
The problem with linearizing and then using linear regression is that the assumption of a Gaussian distribution of residuals is not likely to be true for the transformed data.
It is usually better to use nonlinear regression. Most nonlinear regression programs report the standard error and confidence interval of the best-fit parameters. If yours doesn't, these equations may help.
Each standard error is computed using this equation:
SE(Pi) = sqrt[ (SS/DF) * Cov(i,i)]And here is the equation to compute the confidence interval for each parameter from the best-fit value, its standard error, and the number of degrees of freedom.
DF is degrees of freedom.
Example with Excel for 95% confidence (so alpha = 0.05) and 23 degrees of freedom: = TINV(0.05,23) DF equals degrees of freedom (the number of data points minus number of parameters fit by regression)