What can we say about QQPlot of this data ?

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The QQPlot analysis reveals that the first dataset deviates from normality, showing heavier tails, while the second dataset appears approximately normal but is discrete. The Jarque-Bera tests indicate that the first dataset fails to meet normality, whereas several datasets similar to the second one pass the test. Despite the QQPlots being nearly similar, only a few datasets qualify for normality based on their p-values. The discussion raises questions about the underlying reasons for the observed differences in normality between the datasets.
paawansharmas
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This is the QQPlot of a data. What can be inferred from this plot ?
(Tested against Normal Distribution)
QQPLOT 1 :
attachment.php?attachmentid=51211&stc=1&d=1348650222.png


QQPLOT 2:
attachment.php?attachmentid=51212&stc=1&d=1348650379.png
 

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The first one is not normal - there is more data on the tails than there would be in a normal distribution.

The second one looks approximately normal, except for the fact that it is discrete instead of continuous.
 
thnks mxscnt

Jaque-Bera tests failed for first dataset.
while for data sets similar to second one, few were passing JB test for normality.

these are the p-values for JB test for 14 data sets similar to second graph.

P-VALUE
0.005
0.039
0.003
0
0.00287214
0.595792
0.190489
0.0947931
0.782434
0
0.12
0.257
0.656
0.246

you can see almost 3 sets qualify for normality.

Though there QQPlot are almost similar.

What can be the reason for difference ?
 
I was reading documentation about the soundness and completeness of logic formal systems. Consider the following $$\vdash_S \phi$$ where ##S## is the proof-system making part the formal system and ##\phi## is a wff (well formed formula) of the formal language. Note the blank on left of the turnstile symbol ##\vdash_S##, as far as I can tell it actually represents the empty set. So what does it mean ? I guess it actually means ##\phi## is a theorem of the formal system, i.e. there is a...

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