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Interpretation of normal probability plots is not always straight-forward for the inexperienced data analyst. In the finance literature a plot of empirical and fitted normal densities on the log scale is frequently preferred as a graphical diagnostic for normality. This article describes the construction of this type of plot, and suggests a refinement that can facilitate its interpretability with small samples. A Monte Carlo test for normality arises naturally as a by-product of this methodology.
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Martin L. Hazelton (2003) studied this question.
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