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Within the specification I can glean from the question - here is what I would do. (i) Find the best fit Gaussian - which I am assuming is what you have done. (ii) Your best fit should return a chi-squared value You should compare the chi-squared value with critical values of the chi-squared distribution for the appropriate number of degrees of freedom of ...


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Here is how I interpret what happened: You used Excel to compute the coefficients of the Gaussian that best describe the data: mean $\mu$, standard deviation $\sigma$, and magnitude $A$ for a curve $$Y=Ae^{-(x-\mu)^2/2\sigma^2}$$ Then you evaluated that function at a number of X values. Since the X values are not symmetrical about the calculated mean, you ...


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A gaussian fit is symmetrical by definition, because it is a gaussian. Your orange fit doesnt look like a gaussian, it is not even smooth. I do no think excel had a gaussian fit function (but I dont use excell so cannot tell for sure. You can use other software such as matlab, or likely free ones on the web. Or, just use that data to calculate the parameters ...


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I found it very difficult to understand your question, but here is an attempt at answering it: The whole point of embedding is to uncover the phase-space structure or, with other words, something that is topologically equivalent to the attractor. For any interesting system, this is not describable by your usual distributions (and also it is usually not ...


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The two most common conventions I know are Report the standard deviation of the result: this tells you that a further measurement has 68% probability of falling inside the interval Report the standard error of the measurement (standard deviation / $\sqrt{n}$). This tell you there is a 68% chance that the actual value (the mean of the underlying population) ...


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While I am not familiar with the details of your experiment, I am quite familiar with the methods of maximum likelihood estimation. In the field in which I work, we model the system response as part of our model - so that when we compare the predicted measurements with the actual ones, we find our how close our model is to the underlying truth. It goes ...



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