Revision as of 17:06, 18 November 2008 by Nbrowdue (Talk)

Covariance

  • $ COV(X,Y)=E[(X-E[X])(Y-E[Y])]\! $
  • $ COV(X,Y)=E[XY]-E[X]E[Y]\! $

Correlation Coefficient

$ \rho(X,Y)= \frac {cov(X,Y)}{\sqrt{var(X)} \sqrt{var(Y)}} \, $

Markov Inequality

Loosely speaking: In a nonnegative RV has a small mean, then the probability that it takes a large value must also be small.

  • $ P(X \geq a) \leq E[X]/a\! $

for all a > 0

Chebyshev Inequality

$ \Pr(\left|X-\mu\right|\geq \alpha)\leq\frac{\sigma^2}{\alpha^2}. $

ML Estimation Rule

MAP Estimation Rule

Bias of an Estimator, and Unbiased estimators

Confidence Intervals, and how to get them via Chebyshev

Alumni Liaison

Abstract algebra continues the conceptual developments of linear algebra, on an even grander scale.

Dr. Paul Garrett