Gaussian Fitting: Finding Error with MATLAB Output

In summary, Gaussian fitting is a statistical method used to determine the best-fit parameters of a Gaussian distribution to a set of data points. It is important because Gaussian distributions are commonly found in nature and can be used to describe many different phenomena. MATLAB output helps with Gaussian fitting by providing the results of the fit, including the best-fit parameters and error calculations. Gaussian fitting can be used for non-Gaussian data, but there are limitations, such as the assumption of a Gaussian distribution and potential inaccuracies with outliers or skewed data.
  • #1
natugnaro
64
1
Hi
I have fitted Gaussian function to some data in matlab.
Now MATLAB gives me this:

General model Gauss1:
f(x) = a1*exp(-((x-b1)/c1)^2)
Coefficients (with 95% confidence bounds):
a1 = 2633 (2628, 2637)
b1 = 1824 (1824, 1824)
c1 = 4.979 (4.97, 4.989)

Goodness of fit:
SSE: 3.694e+004
R-square: 0.9991
Adjusted R-square: 0.9991
RMSE: 4.599

I would like to write my result for c1 like c1[tex]\pm[/tex]M where M is error in measurement,
Since it's been a while I have done any measurement analysis, is it possible to get this error from output above ?
 
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Related to Gaussian Fitting: Finding Error with MATLAB Output

1. What is Gaussian fitting and why is it important?

Gaussian fitting is a statistical method used to determine the best-fit parameters of a Gaussian distribution to a set of data points. It is important because Gaussian distributions are commonly found in nature and can be used to describe many different phenomena, such as physical measurements, atmospheric data, and financial data.

2. How does MATLAB output help with Gaussian fitting?

MATLAB output provides the results of the Gaussian fitting, including the best-fit parameters such as the mean, standard deviation, and amplitude of the Gaussian distribution. This can be used to evaluate the accuracy of the fit and determine the error in the measurements.

3. What is the error in Gaussian fitting and how is it calculated?

The error in Gaussian fitting is the difference between the actual data points and the predicted values from the Gaussian distribution. This error can be calculated using various methods, such as the root mean square error or the chi-squared test.

4. Can Gaussian fitting be used for non-Gaussian data?

Yes, Gaussian fitting can be used for non-Gaussian data, as long as the data can be approximated by a Gaussian distribution. In some cases, the data may need to be transformed or preprocessed before fitting to a Gaussian distribution.

5. Are there any limitations to using Gaussian fitting?

Yes, there are some limitations to using Gaussian fitting. One limitation is that it assumes the data follows a Gaussian distribution, which may not always be the case. Additionally, if the data has significant outliers or is highly skewed, Gaussian fitting may not provide an accurate representation of the data.

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