Glossary of statistical terms
Kurtosis measures the "heaviness of the tails" of a distribution (in compared to a normal distribution). Kurtosis is positive if the tails are "heavier" then for a normal distribution, and negative if the tails are "lighter" than for a normal distribution. The normal distribution has kurtosis of zero.
Kurtosis characterizes the shape of a distribution - that is, its value does not depend on an arbitrary change of the scale and location of the distribution. For example, kurtosis of a sample (or population) of temperature values in Fahrenheit will not change if you transform the values to Celsius (the mean and the variance will, however, change).
To calculate the kurtosis of a sample:
i) subtract the mean from each value to get a set of deviations from the mean;
ii) divide each deviation by the standard deviation of all the deviations;
iii) average the 4th power of the deviations and subtract 3 from the result.
Want to learn more about this topic?
Statistics.com offers over 100 courses in statistics from introductory to advanced level. Most are 4 weeks long and take place online in series of weekly lessons and assignments, requiring about 15 hours/week. Participate at your convenience; there are no set times when you must to be online. Ask questions and exchange comments with the instructor and other students on a private discussion board throughout the course.
This course covers modeling technique making decisions in the presence of risk or uncertainty. Specific topics include risk analysis using Monte Carlo simulation for risk simulation, queuing theory for problems involving waiting lines, and decision trees for analyzing problems with multiple discrete decision alternatives.