Multiply the possible outcomes of the events in the disjoint events
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Two sets are considered disjoint if they have no elements in common.
Two sets are disjoint if there are elements that belong to both. Two sets are overlapping if there is at least one elements that belongs to both.
Two sets are said to be "disjoint" if they have no common element - their intersection is the empty set. As far as I know, "joint" is NOT used in the sense of the opposite of disjoint, i.e., "not disjoint".
Multiply the possible outcomes of the events in the disjoint events
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In probability theory, disjoint events are two (or more) events where more than one cannot occur in the same trial. It is possible that none of them occur in a particular trial.
Two sets are considered disjoint if they have no elements in common.
If two events are disjoint, they cannot occur at the same time. For example, if you flip a coin, you cannot get heads AND tails. Since A and B are disjoint, P(A and B) = 0 If A and B were independent, then P(A and B) = 0.4*0.5=0.2. For example, the chances you throw a dice and it lands on 1 AND the chances you flip a coin and it land on heads. These events are independent...the outcome of one event does not affect the outcome of the other.
Yes,Because not all disjoint no equivalent other have disjoint and equivalent
Two sets are disjoint if there are elements that belong to both. Two sets are overlapping if there is at least one elements that belongs to both.
Not necessarily. For a counterexample, A and C could be the same set.
Two sets are said to be "disjoint" if they have no common element - their intersection is the empty set. As far as I know, "joint" is NOT used in the sense of the opposite of disjoint, i.e., "not disjoint".
A disjoint event is an event that can not happen at the same time
Two sets are said to be "disjoint" if they have no common element - their intersection is the empty set. As far as I know, "joint" is NOT used in the sense of the opposite of disjoint, i.e., "not disjoint".
If they're disjoint events: P(A and B) = P(A) + P(B) Generally: P(A and B) = P(A) + P(B) - P(A|B)