Nice work, but C is not the FWHM. They are related by FWHM = 2 * C * SQRT( 2 * LN (2)). Do another take please and also fix the part where you entered the fit formula wrong.
Very well explained. Thank you very much. I used this method for fitting my experimental values. But unfortunately, the final values for the variables depend a lot on initial values (that we first assume). Did you notice anything like that in your calculation?
Hi, thanks for your video. Very useful. one question. to calculate chi sq (Column D) why you don't use ((Y - Fit)^2)/Fit. ? Just the difference btw them.
Because he is using the Goodness-of-Fit given by the Chi distribution. Formula for that is (Observed Value - Expected Value)^2 / Expected Value... Like we do to test the association between two variables in a contingency table (chi-square test of independence, etc.)
Thank you so much, this worked wonderfully for my medical equipment evaluations!
Brother you have no idea how much you helped me.
You can also calculate the Sum chi sq using the formula = sumxmy2(Fit,y) - this way you dont need the "Chi sq" column
Nice work, but C is not the FWHM. They are related by FWHM = 2 * C * SQRT( 2 * LN (2)). Do another take please and also fix the part where you entered the fit formula wrong.
Thank you very much, finally i can finish my homework
You are welcome!
Very well explained. Thank you very much. I used this method for fitting my experimental values. But unfortunately, the final values for the variables depend a lot on initial values (that we first assume). Did you notice anything like that in your calculation?
It was very much well explained..I understood a lot...can you please tell me what is concept of offset in curve fitting and how to calculated it?
But is for limited cells. it doesn't hold on more than 300 cells of a column.
Hi, thanks for your video. Very useful. one question. to calculate chi sq (Column D) why you don't use ((Y - Fit)^2)/Fit. ? Just the difference btw them.
Great suggestion!
Because he is using the Goodness-of-Fit given by the Chi distribution. Formula for that is (Observed Value - Expected Value)^2 / Expected Value... Like we do to test the association between two variables in a contingency table (chi-square test of independence, etc.)
Awesome video, very useful thanks. Helped me get my lab report done on time :)
Fine job. Really excellent. Thanks!
Many thanks!
Thank you, excellent video, I need to know that how to work with Modified Gaussian model (MGM). Could you please help on this?
Thanks. Great video.
Excellent work!
Excellent! Thank you!
Very useful! Thanks!
Glad it was helpful!
You're the best
Thanks
Legend
This doesn't appear to be working for me. It repeatedly just gives me a straight line when I'm trying to fit it to a Gaussian.
How can you find the error in the fit parameters? That's the real deal.
what is the purpose behind all this practice when we already have a curve of same dimensions.?
Please check my website www.edmerls.com for complete course and download notes for free.
@@Edmerls Thank you so much, which course on your website explains my question.
The equation used in excel for Gaussian fitting is incorrect. There is no exponential term.
If you just slow down a little that would be great. You say and do everything so fast it is difficult to follow.