What is a Chi-Squared Test Against Weighted Mean?

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In summary, the paper discusses the use of a chi-square test against the weighted mean in analyzing the flux variability of a gamma-ray source. The results show no evidence for variability and a possible contribution from extended radio features cannot be ruled out. The authors suggest contacting the corresponding author for further clarification.
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majormuss
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Hi all,
I was reading a paper and I wasn't sure how they performed their statistic analysis. What do they mean by a chi-square test against the weighted mean? Is it a chi-square test between the actual data points and the weighted mean? If so, why do they us the function P(X^2,v)? I thought that was a function for Poisson distribution.

This is the paragraph describing the statistics and the graph below is figure 3:
"Lightcurves were produced in 10-day (Figure 3) and 28-day (not shown) bins over the 10-month LAT dataset. Considering the limited statistics, it was necessary to fix the photon index to the (average) fitted value in order to usefully gauge variability in the flux. Considering only statistical errors of all the binned data points with T S ≥ 1 (1σ), a χ 2 test against the weighted mean fluxes of the 10-day and 28-day lightcurves resulted in probabilities, P(χ 2 ,ν) = 22% and 70%, respectively, indicating plausible fits to the tested hypothesis. We conclude that there is no evidence for variability over the period of observations. A radial profile of the γ-ray source counts (not shown) was extracted for the total energy range (>200 MeV). The profile is consistent with that of a point source simulated at energies 0.2 − 200 GeV using the fitted spectral parameters above with a reduced χ 2 = 1.04 for 20 degrees of freedom. The total ∼0 ◦ .2 extent of the 10’s kpc-scale radio lobes of M87 (Figure 1; Owen et al. 2000) is comparable to the LAT angular resolution, θ68 ≃ 0 ◦ .8 E −0.8 GeV (Atwood et al. 2009). Therefore, from the presently available data, we can not disentangle (or exclude) a possible contribution of the extended radio features to the total γ-ray flux. "
upload_2015-7-23_9-21-22.png
Figure 3.
 

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  • #2
That is a bit tricky to unravel.

Have you consdered writing the corresponing author?
 
  • #3
majormuss said:
Hi all,
I was reading a paper and I wasn't sure how they performed their statistic analysis. What do they mean by a chi-square test against the weighted mean? Is it a chi-square test between the actual data points and the weighted mean? If so, why do they us the function P(X^2,v)? I thought that was a function for Poisson distribution.

This is the paragraph describing the statistics and the graph below is figure 3:
"Lightcurves were produced in 10-day (Figure 3) and 28-day (not shown) bins over the 10-month LAT dataset. Considering the limited statistics, it was necessary to fix the photon index to the (average) fitted value in order to usefully gauge variability in the flux. Considering only statistical errors of all the binned data points with T S ≥ 1 (1σ), a χ 2 test against the weighted mean fluxes of the 10-day and 28-day lightcurves resulted in probabilities, P(χ 2 ,ν) = 22% and 70%, respectively, indicating plausible fits to the tested hypothesis. We conclude that there is no evidence for variability over the period of observations. A radial profile of the γ-ray source counts (not shown) was extracted for the total energy range (>200 MeV). The profile is consistent with that of a point source simulated at energies 0.2 − 200 GeV using the fitted spectral parameters above with a reduced χ 2 = 1.04 for 20 degrees of freedom. The total ∼0 ◦ .2 extent of the 10’s kpc-scale radio lobes of M87 (Figure 1; Owen et al. 2000) is comparable to the LAT angular resolution, θ68 ≃ 0 ◦ .8 E −0.8 GeV (Atwood et al. 2009). Therefore, from the presently available data, we can not disentangle (or exclude) a possible contribution of the extended radio features to the total γ-ray flux. "
View attachment 86285Figure 3.

I think the author is referring to a ## (\chi^2, v) ## goodness-of-fit test, where v is the number of degrees of freedom. http://stattrek.com/chi-square-test/goodness-of-fit.aspx?Tutorial=AP , where 22%, 70% are pretty far from most choices of significance level.
 

1. What is a Chi Squared Test?

A Chi Squared Test is a statistical test used to analyze categorical data and determine if there is a significant difference between the observed frequencies and the expected frequencies.

2. When should I use a Chi Squared Test?

A Chi Squared Test should be used when you have two or more categorical variables and want to determine if there is a relationship between them. It can also be used to compare observed and expected frequencies in a single categorical variable.

3. How do I interpret the results of a Chi Squared Test?

The results of a Chi Squared Test will provide a p-value, which indicates the probability of obtaining the observed results by chance. If the p-value is less than the chosen significance level (usually 0.05), then there is a significant difference between the variables. Additionally, the Chi Squared statistic can be compared to a critical value to determine significance.

4. What are the assumptions of a Chi Squared Test?

The assumptions of a Chi Squared Test include:

  • Independence: the observations are independent of each other.
  • Randomness: the data is collected randomly.
  • Expected frequencies: the expected frequencies in each category are at least 5.
If these assumptions are violated, the results of the test may not be accurate.

5. Can a Chi Squared Test be used with continuous data?

No, a Chi Squared Test is specifically designed for categorical data. For continuous data, other tests such as the t-test or ANOVA should be used.

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