How Do I Correctly Fit a Gaussian to My Absorption Spectra Data?

In summary, the speaker is discussing their process for fitting a gaussian curve to a plot of the first derivative of an absorption spectra. They calculate the mean and variance and use them in the equation, but the amplitude is too large and they are unsure if this is the correct method. They ask for assistance and the responder suggests checking their calculations and providing more details.
  • #1
pazmush
32
0
I have a set of data for an absorption spectra that I plot the firtst derivative of and it gives me a gaussianish plot, I then want to fit it with a gaussian and what I've been doing so far is calculating the mean and variace of the spread and then using them in the equation. Although this does give me a gaussian the amplitude is too large and I'm unsure weather this is the correct way to be doing the data analysis

Can anyone help, thanks
 
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  • #2
What you describe sounds right.

The maximum likelihood estimators for the mean and the variance of a normal distribution are simply the population mean and variance. Maybe you made a mistake in your calculations.

If not so, it would be helpful if you provide a some more details on what you are doing, maybe a picture:smile:
 

FAQ: How Do I Correctly Fit a Gaussian to My Absorption Spectra Data?

What is Gaussian fitting?

Gaussian fitting is a statistical method used to model and analyze data that follows a Gaussian or normal distribution. It involves finding the best fit curve that represents the data and estimating the parameters of the distribution.

When is Gaussian fitting used?

Gaussian fitting is commonly used in scientific research and data analysis to model various phenomena that follow a normal distribution, such as measurements of physical quantities, experimental data, and biological data.

How is Gaussian fitting performed?

Gaussian fitting is typically performed using a mathematical algorithm called the least squares method, which minimizes the sum of the squared errors between the data and the fitted curve. It can also be done manually by adjusting the parameters of the Gaussian curve until it best fits the data.

What are the benefits of Gaussian fitting?

Gaussian fitting allows for a better understanding of the underlying distribution of data and can provide more accurate predictions and estimations of future data points. It also allows for the identification of outliers and the detection of any deviations from the expected normal distribution.

What are some common challenges with Gaussian fitting?

One common challenge with Gaussian fitting is the selection of an appropriate initial guess for the parameters of the Gaussian curve. Additionally, if the data deviates significantly from a normal distribution, Gaussian fitting may not be the most suitable method for analysis.

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