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- https://stats.libretexts.org/Courses/Saint_Mary's_College_Notre_Dame/BFE_1201_Statistical_Methods_for_Finance_(Kuter)/03%3A_Probability_Topics/3.07%3A_Independent_EventsConsider the context of Exercise 2.2.1, where we randomly draw a card from a standard deck of 52 and \(C\) denotes the event of drawing a club, \(K\) the event of drawing a King, and \(B\) the event o...Consider the context of Exercise 2.2.1, where we randomly draw a card from a standard deck of 52 and \(C\) denotes the event of drawing a club, \(K\) the event of drawing a King, and \(B\) the event of drawing a black card. Alternately, knowing that the card drawn is black increases the probability that the card is a club, since the proportion of clubs in the entire deck is \(1/4\), but the proportion of clubs in the collection of black cards is \(1/2\).
- https://stats.libretexts.org/Courses/Saint_Mary's_College_Notre_Dame/DSCI_500B_Essential_Probability_Theory_for_Data_Science_(Kuter)/02%3A_Conditional_Probability/2.02%3A_Independent_EventsConsider the context of Exercise 2.2.1, where we randomly draw a card from a standard deck of 52 and \(C\) denotes the event of drawing a club, \(K\) the event of drawing a King, and \(B\) the event o...Consider the context of Exercise 2.2.1, where we randomly draw a card from a standard deck of 52 and \(C\) denotes the event of drawing a club, \(K\) the event of drawing a King, and \(B\) the event of drawing a black card. Alternately, knowing that the card drawn is black increases the probability that the card is a club, since the proportion of clubs in the entire deck is \(1/4\), but the proportion of clubs in the collection of black cards is \(1/2\).
- https://stats.libretexts.org/Bookshelves/Applied_Statistics/Business_Statistics_(OpenStax)/03%3A_Probability_Topics/3.07%3A_Independent_EventsThis page explains independence in conditional probability for pairs and larger collections of events. Two events, A and B, are independent if knowing one does not change the other's probability, mean...This page explains independence in conditional probability for pairs and larger collections of events. Two events, A and B, are independent if knowing one does not change the other's probability, meaning P(A|B) = P(A) and P(B|A) = P(B). This leads to P(A∩B) = P(A)P(B). For three or more events, independence can be pairwise (each pair is independent) or mutually independent (all combinations satisfy independence). While mutual independence guarantees pairwise independence, the reverse is not true