The best analysis is the simplest analysis that provides the needed insight. Of course this requires enough knowledge to strike a balance between the needed simplicity and unnecessary complexity. In parts one and two of this series we looked at the properties of Weibull and gamma probability models and discovered some unexpected characteristics for each of these families. Here we shall examine some basic properties of the family of lognormal models.

As we have seen, Weibull and gamma distributions have an elongated tail, with the elongation increasing with the skewness. Yet contrary to expectation, this elongation does not increase the area in the tail. In fact just the opposite, it is the area in the central portion, the area within one standard deviation of the mean, that increases with increasing skewness. Here we consider if this property of Weibulls and gammas also holds true for lognormal probability models.

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