What is the correct formula for the reduced Chi square?

In summary, the conversation discusses the calculation of reduced Chi square and root mean square deviation (RMSD) for a set of data points. There is confusion regarding which formula is the correct one, as some sources refer to it as RMSD while others call it reduced chi square. The conversation also delves into the concept of minimizing reduced chi square for the best fit, with some confusion about the optimum value being 1 or 1-reduced chi square. The elements of the formulae are carefully defined, with ##y## representing the measured data, ##\tilde{y}## being the calculated data from a specific model, ##\delta y_i## being the error in measuring ##y##, and ##m## being
  • #1
patric44
308
40
Homework Statement
what is the correct formula of reduced Chi square
Relevant Equations
\Chi^2
Hi all
I want to calculate the reduced Chi square and root mean square deviation RMSD of some data points that i have, but I am confused about the correct formula for each of them, which one is the correct one. I found this formula in a paper where they referred to it as the RMSD :
$$
\chi=\sqrt{\frac{1}{N}\sum_{i}^{N}\left(\frac{(y_{i}-\tilde{y}_{i})}{\delta y_{i}}\right)^{2}}
$$
and in some books the same formula with little modification (instead of ##N## they put the degrees of freedom) as :
$$
\chi=\sqrt{\frac{1}{N-m}\sum_{i}^{N}\left(\frac{(y_{i}-\tilde{y}_{i})}{\delta y_{i}}\right)^{2}}
$$
which one is reduced ##\chi^{2}## and which is RMSD if any of them?!
another question why i read that we need to minimize the value of reduced ##\chi^{2}## to get the best fit, isn't the optimum value is 1 ?! , shouldn't we minimize 1-##\chi^{2}## or what?
I will appreciate any help, thanks in advance
 
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  • #2
Please carefully define the elements in these formulae, particularly ##\tilde{y}_{i}## and
##\delta y_i ## and what is m?)
 
  • #3
hutchphd said:
Please carefully define the elements in these formulae, particularly ##\tilde{y}_{i}## and
##\delta y_i ## and what is m?)
##y## is the measured data
##\tilde{y}## is the calculated data from a specific model
##\delta y_i ## is the error in measuring ##y##
##m## the number of parameters of the model
I am not talking about the so called category chi2. I mean the other one
 
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  • #4
I think the formula with the m is appropriate. Very often m=1 when the the mean value is taken as a "fitted" parameter from the data. I have no idea about the names and categories of these things sorry.
 
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FAQ: What is the correct formula for the reduced Chi square?

What is the correct formula for the reduced Chi square?

The correct formula for the reduced Chi square (χ²_red) is given by:

χ²_red = χ² / ν

where χ² is the Chi square value and ν (nu) is the number of degrees of freedom.

How do you calculate the Chi square value (χ²) for the reduced Chi square formula?

The Chi square value (χ²) is calculated using the formula:

χ² = Σ[(O_i - E_i)² / E_i]

where O_i represents the observed values, E_i represents the expected values, and the summation is over all data points.

What are degrees of freedom (ν) in the context of the reduced Chi square formula?

The degrees of freedom (ν) in the context of the reduced Chi square formula are calculated as:

ν = N - p

where N is the number of data points and p is the number of fitted parameters in the model.

Why is the reduced Chi square used instead of the Chi square value?

The reduced Chi square is used instead of the Chi square value to account for the number of degrees of freedom in the data. It provides a normalized measure of how well the model fits the data, making it easier to compare fits across different datasets or models with varying numbers of parameters.

What is a good value for the reduced Chi square?

A good value for the reduced Chi square is typically around 1. If χ²_red is much greater than 1, it suggests that the model does not fit the data well. If χ²_red is much less than 1, it may indicate overfitting or that the error estimates are too large.

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