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Session 2, Reading 9 (Part 2): This video reviews portfolio variance and covariance, where covariance is the expected cross-product. We look at correlation, which is given by the covariance divided by the product of standard deviations, and therefore standardizes the covariance into a unitless, intuitive measure of *linear* relationship. Because covariance and correlation are linear, it is important to realize that although independence implies zero correlation, the converse is not true; i.e., zero correlation does not imply independence. Special topics include Bayes Formula (aka, Bayes Theorem), multiplication rule, multinomial formula for labeling, and finally combinations versus permutations.