Statistics 110 Lecture 9: Expectation, Indicator Random Variables, Linearity
by Joseph K. Blitzstein · Harvard University (Statistics 110)
Joe Blitzstein's Harvard lecture defines expectation and builds the core computational toolkit: indicator random variables, linearity of expectation, and symmetry arguments, worked through examples including the geometric and binomial distributions. The single best hour anywhere on this exact concept: it does not merely define E[X] but teaches the two techniques that make expectation usable (indicators plus linearity without independence).
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