Timeline for Standard error of linear fit parameters
Current License: CC BY-SA 3.0
12 events
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| Mar 31, 2016 at 13:29 | history | edited | mrz | CC BY-SA 3.0 |
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| Mar 31, 2016 at 13:24 | history | edited | mrz | CC BY-SA 3.0 |
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| Mar 31, 2016 at 12:46 | vote | accept | mrz | ||
| Mar 31, 2016 at 12:46 | comment | added | mrz | somebody dislikes this question: can he/she explain why - or is this just for fun? | |
| Mar 31, 2016 at 12:29 | answer | added | Chris Degnen | timeline score: 4 | |
| Mar 31, 2016 at 12:15 | comment | added | J. M.'s missing motivation♦ |
...and if you do use NonlinearModelFit[], you can use the "ParameterErrors" property of the resulting FittedModel[] object.
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| Mar 31, 2016 at 11:59 | comment | added | Jason B. |
@mrz - the easiest way would be to use LinearModelFit or NonlinearModelFit, but I take it you want the formula used to calculate these for academic purposes. I'd look at this page and this page
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| Mar 31, 2016 at 11:59 | comment | added | Szabolcs |
Can you explain why you want to avoid LinearModelFit/NonlinearModelFit in favour of FindFit? What's wrong with NonlinearModelFit? It also calls FindFit internally, but it also does additional calculations to get the errors. You'll need to do these manually if you don't want NonlinearModelFit.
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| Mar 31, 2016 at 11:54 | comment | added | mrz | Thank you for your comment ... how can I obtain the errors of the fit parameters (a,b)? | |
| Mar 31, 2016 at 11:36 | comment | added | J. M.'s missing motivation♦ |
Why linearize when nonlinear regression capabilities are available? FindFit[data, a x^b, {a, b}, x]
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| Mar 31, 2016 at 11:18 | history | edited | mrz | CC BY-SA 3.0 |
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| Mar 31, 2016 at 11:10 | history | asked | mrz | CC BY-SA 3.0 |