What is the correct formula for the reduced Chi square?

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Homework Help Overview

The discussion revolves around the calculation of the reduced Chi square and root mean square deviation (RMSD) for a set of data points. The original poster expresses confusion regarding the correct formulas for each, noting variations found in literature and questioning their definitions and applications.

Discussion Character

  • Conceptual clarification, Assumption checking

Approaches and Questions Raised

  • The original poster presents two formulas for the reduced Chi square and RMSD, questioning which is appropriate for each. They also inquire about the minimization of reduced Chi square in relation to best fit criteria.

Discussion Status

Some participants seek clarification on the definitions of variables within the formulas. There is an indication that one participant supports the formula that includes the number of parameters, while expressing uncertainty about the terminology used in this context. The discussion appears to be exploring different interpretations without reaching a consensus.

Contextual Notes

Participants are discussing specific elements of the formulas, such as the meaning of the variables involved and the implications of the number of parameters in the model. There is a focus on understanding the distinctions between the formulas rather than resolving the confusion outright.

patric44
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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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Please carefully define the elements in these formulae, particularly ##\tilde{y}_{i}## and
##\delta y_i ## and what is m?)
 
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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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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