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.
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.
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.
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.
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.