Statistical Omnibus

Statistics is much more than the boring course you hated in college.

For example,

Did you know that "probability" and "statistics" are not synonymous terms?
Did you know that probability has two different definitions, both part of mainstream statistical thought, yet fundamentally in conflict?
Did you know what the likelihood function is, and where it comes from, and why you should care?
Ever heard of the Fisher Information Matrix? Or the Cram�r-Rao inequality? Can you put them to practical use?
Do you know what the Central Limit Theorem says and why it is central to successful Engineering Probabilistics?
Were you aware that two variables can have a perfect functional relationship and yet have zero correlation?
Do you know the difference between a conditional distribution and a marginal distribution? Or a joint distribution? Or when you can get from one to another - and when you cannot?

If you have an analytically predicted stress of 50 KSI and a strain gage measurement that's different, which should you believe? How would you resolve the difference? (The common practice of adding the difference to the analytical result as a "correction" is dangerous. Do you know why?)

This short course will describe and discuss these and other interesting, important, and especially useful, results from Math Stats as they apply to Probabilistic Engineering Analysis. With luck it may even un-do some of the damage done to your psyche by STAT 101.