One more update on this subject. I have been looking through some of the
papers on this topic, and I have finally found exactly what I need in this
paper:
Hussein, T., Dal Maso, M., Petaja, T., Koponen, I. K., Paatero, P., Aalto,
P. P., Hameri, K., and Kulmala, M.: Evaluation of an automatic algor
On Thu, Nov 19, 2009 at 9:12 PM, Ian Mallett wrote:
> Hello,
>
> My analysis shows that the exponential regression gives the best result
> (r^2=87%)--power regression gives worse results (r^2=77%). Untransformed
> data gives r^2=76%.
>
> I don't think you want lognorm. If I'm not mistaken, that
Hello,
My analysis shows that the exponential regression gives the best result
(r^2=87%)--power regression gives worse results (r^2=77%). Untransformed
data gives r^2=76%.
I don't think you want lognorm. If I'm not mistaken, that fits the data to
a log(normal distribution random variable).
So,
On Tue, Nov 17, 2009 at 12:13 AM, Ian Mallett wrote:
> Theory wise:
> -Do a linear regression on your data.
> -Apply a logrithmic transform to your data's dependent variable, and do
> another linear regression.
> -Apply a logrithmic transform to your data's independent variable, and do
> another
Theory wise:
-Do a linear regression on your data.
-Apply a logrithmic transform to your data's dependent variable, and do
another linear regression.
-Apply a logrithmic transform to your data's independent variable, and do
another linear regression.
-Take the best regression (highest r^2 value) an
Hello,
I have a data which represents aerosol size distribution in between 0.1 to
3.0 micrometer ranges. I would like extrapolate the lower size down to 10
nm. The data in this context is log-normally distributed. Therefore I am
looking a way to fit a log-normal curve onto my data. Could you pleas
