Timeline for Why Do I Have Large Error Margin Using NonLinearModelFit function
Current License: CC BY-SA 3.0
7 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jul 27, 2013 at 0:31 | vote | accept | Afloz | ||
| Jul 27, 2013 at 0:06 | vote | accept | Afloz | ||
| Jul 27, 2013 at 0:08 | |||||
| Jul 26, 2013 at 15:55 | comment | added | Afloz | Ahaha. That makes sense. I'll call it the "Fudge Fit Factor". Thanks a lot B. You've done more that fit my data ;) | |
| Jul 26, 2013 at 12:21 | comment | added | bobthechemist | @Methyl yes. I chose the more 'technical' term :-). There are cases when you may not want to normalize the data and this is one option in such cases. | |
| Jul 26, 2013 at 8:49 | comment | added | Afloz | Oh by "fudge" you mean something like an "enabler" or a"heuristic" a "weighting factor"? is that what you mean? | |
| Jul 26, 2013 at 8:31 | comment | added | Afloz | Yes, this is the best I came up with as well using Rician distr. but I had to normalize the data with data/Total[data] before I got this. Would you please explain the fudge parameter means? It appears this is what @Chuy was talking about. | |
| Jul 20, 2013 at 3:22 | history | answered | bobthechemist | CC BY-SA 3.0 |