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# Variance and Higher Moments - Comprehensive Introduction

Course Topic(s): Probability | Expected Value, variance and higher moments

This page contains the definition of variance and higher moments using the expected value. It contains properties, with proofs, of variance, skew, and kurtosis. It also contains Chebyshevâ€™s inequality. It also has discussions about the variance of special distributions, some of these are given, some the reader is asked to find (answers are given through a small link at the end of the page). It contains links to simulators and directions for the student to use the simulator to observe various properties. Finally, there is a discussion of vector space concepts leading to Minkowskiâ€™s inequality, Lyapunovâ€™s inequality, and convergence.

Resource URL: http://www.math.uah.edu/stat/expect/Variance.html

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Subject classification(s): Statistics and Probability | Probability | Univariate Distributions

Creator(s): Kyle Siegrist

Contributor(s): Kyle Siegrist

This resource was cataloged by Lisa Green

Publisher:
Virtual Laboratories in Probability and Statistics

This review was published on October 10, 2012