Negative values in covariance matrix

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Negative values in the covariance matrix can occur, indicating that higher-than-average results in one variable correspond with lower-than-average results in another. However, the issue arises when negative values appear in the diagonal elements, which represent the variance of the function coefficients. This leads to the conclusion that the errors of the function coefficients are imaginary, which is problematic for fitting the luminescence decay profile. Understanding that covariance can be negative is crucial, but the presence of negative variances suggests a deeper issue in the data or fitting process. Addressing this anomaly is essential for accurate modeling and interpretation of the experimental results.
latvietis
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Hello

I had measured luminescence decay profile. Then I want to fit a function which would approximate my experimental date. For that I make a simple program in LabWiev. The problem is that, that program give me out a negative values in covariance matrix. Why that?


P.S.
Sorry for my English
 
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Negative covariance is OK. It means that higher-than-average results from one variable will happen at the same time as lower-than-average results from the other variable.

For example, the covariance between how cold it is out and much people get sunburned is probably negative.

If you have more intuition for correlation, this may help: the covariance between 2 variables is just the correlation between the variables, scaled by the standard deviations.
 
Ok

But problem is that the negative values is in diagonal elements. Diagonals elements of covariance matrix is \sigma^2. So I get that errors of functions coefficients \sigma is imaginary.
 

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