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I have a statistics doubt. Consider I have the following values.

Base Model      Model 1     Model 2
5               7           4
10              12          8
15              17          12
20              23          21
25              30          27
30              31          31
35              34          37
40              43          43
45              45          44
50              51          50

I want a better way to represent Model1 and Model2 by comparing against the Base Model. I used an error metric:

$$\frac{|\text{My_Model} - \text{Base_Model}|}{\text{Base_Model}} \times 100$$

What other methods can I use to represent these. I want to plot these as a graph and see how good Model 1 and Model 2 are w.r.t Base Model. Can I use any form of distributions?

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What is your research question? – Roman Luštrik Aug 24 '12 at 19:54
up vote 1 down vote accepted

In the chi square goodness of fit test the measure is ∑(observed-expected)$^2/expected. The change to your formula would be to square the numerator and not express it as a percentage.

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