Measuring averages (Chi squared)

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In summary, the conversation discusses a section of a course involving chi squared and least square fitting. The question involves calculating the average of 3 measurements of a poisson distributed quantity using two different methods: setting up a chi squared function and using the mean of the poisson distribution. The question asks to investigate biased and unbiased estimators. The conversation suggests trying the concept with a Gaussian distribution to better understand the idea.
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skiboka33
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Really confused with this entire section of my course involving chi squared and least square fitting.

I have an assignment question in which 3 measurements of a poisson distributed quantity of counts over a fixed period of time, say N1, N2, N3.The variances associated with these measurements are also N1, N2, N3 respectively. The problem shows two ways of calculating the average. Both involve setting up a Chi Squared function.

The first gives:

K^2 = SUM[((t-Ni)^2)/Ni)] from i=1->3

The 2nd uses the fact that the mean of the poisson dist'n is (N1+N2+N3)/3 which is also equal to the variance. This gives:

(3/N1+N2+N3) * SUM[(t-Ni)^2]from i=1->3

The question asks which would be more appropriate to use to find the average given t is our estimate of true poisson mean. (eg which is more biased? in which direction?)

I know that may have seemed a little confusing, but anything would help, thanks!
 
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The question asks to investigate the phenomenon of biased and unbiased estimators. Its late and I haven't the inclination to write the equations out, however; a quick read of these concepts should lead to a quick solution. Try it first with a Gaussian distribution with the mean estimators
1/m sum(qi,i,1,n) and 1/(m+1)sum(qi,i,1,n) to see the idea.
 

1. What is Chi squared and why is it used in measuring averages?

Chi squared is a statistical test used to determine if there is a significant difference between observed data and expected data. It is commonly used in measuring averages because it allows us to compare the observed frequencies of a categorical variable to the expected frequencies, helping us to determine if there is a relationship between the variables being studied.

2. How is Chi squared calculated?

Chi squared is calculated by taking the sum of the squared differences between observed and expected frequencies and dividing it by the expected frequency for each category. This calculation results in a single value that can be compared to a critical value to determine if there is a significant difference between the observed and expected frequencies.

3. What is the purpose of determining the average with Chi squared?

The purpose of determining the average with Chi squared is to determine if there is a significant difference between the observed and expected frequencies. This can help us to understand the relationship between the variables being studied and make conclusions about the data.

4. How is Chi squared used in hypothesis testing?

Chi squared is used in hypothesis testing by comparing the calculated Chi squared value to a critical value. If the calculated value is greater than the critical value, it indicates that there is a significant difference between the observed and expected frequencies, and therefore, we reject the null hypothesis. If the calculated value is less than the critical value, it indicates that there is not a significant difference and we fail to reject the null hypothesis.

5. Can Chi squared be used in all types of data?

No, Chi squared is most commonly used in categorical data where the variables being studied are discrete and can be placed into categories. It is not appropriate for continuous data, as the categories are not clearly defined. In these cases, other statistical tests such as t-tests or ANOVA may be more suitable.

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