P(A'/B)=P(A'nB)/P(B)
P(A given B')=[P(A)-P(AnB)]/[1-P(B)].In words: Probability of A given B compliment is equal to the Probability of A minus the Probability of A intersect B, divided by 1 minus the probability of B.
The complement (not compliment) of the probability of event A is 1 minus the probability of A: that is, it is the probability of A not happening or "not-A" happening.The complement (not compliment) of the probability of event A is 1 minus the probability of A: that is, it is the probability of A not happening or "not-A" happening.The complement (not compliment) of the probability of event A is 1 minus the probability of A: that is, it is the probability of A not happening or "not-A" happening.The complement (not compliment) of the probability of event A is 1 minus the probability of A: that is, it is the probability of A not happening or "not-A" happening.
Prob(A given B) = Prob(A and B)/Prob(B)
The probability of event A occurring given event B has occurred is an example of conditional probability.
With probability ratios the value you get to describe the strength of the relationship when you compare (A given B) to (A given not B) is not the same as what you get when you compare (not A given B) to (not A given not B). This is, IMHO, a big problem. There is no such problem with odds ratios.
P(A given B')=[P(A)-P(AnB)]/[1-P(B)].In words: Probability of A given B compliment is equal to the Probability of A minus the Probability of A intersect B, divided by 1 minus the probability of B.
The complement (not compliment) of the probability of event A is 1 minus the probability of A: that is, it is the probability of A not happening or "not-A" happening.The complement (not compliment) of the probability of event A is 1 minus the probability of A: that is, it is the probability of A not happening or "not-A" happening.The complement (not compliment) of the probability of event A is 1 minus the probability of A: that is, it is the probability of A not happening or "not-A" happening.The complement (not compliment) of the probability of event A is 1 minus the probability of A: that is, it is the probability of A not happening or "not-A" happening.
Prob(A given B) = Prob(A and B)/Prob(B)
The probability of event A occurring given event B has occurred is an example of conditional probability.
If A and B are independent, then you can multiply the two probabilities
With probability ratios the value you get to describe the strength of the relationship when you compare (A given B) to (A given not B) is not the same as what you get when you compare (not A given B) to (not A given not B). This is, IMHO, a big problem. There is no such problem with odds ratios.
Given two events, A and B, the conditional probability rule states that P(A and B) = P(A given that B has occurred)*P(B) If A and B are independent, then the occurrence (or not) of B makes no difference to the probability of A happening. So that P(A given that B has occurred) = P(A) and therefore, you get P(A and B) = P(A)*P(B)
Given two events, A and B, the probability of A or B is the probability of occurrence of only A, or only B or both. In mathematical terms: Prob(A or B) = Prob(A) + Prob(B) - Prob(A and B).
Define your event as [A occurs and B does not occur] or as [A occurs and B' occurs] where B' is the complement of B. Equivalently, this is the event that [A and B' both occur].
Pr(A | B)
compliment- it's a word for the probability minus one, so it something has a .6 probability, the probability(compliment) it woun't occur is .4 The answer for the Statistic Crossword Puzzle is "odds"
Yes, the complement rule can be applied to mutually exclusive events. For example, if you have two mutually exclusive events, A and B, the probability of either event occurring is given by P(A or B) = P(A) + P(B). The complement rule states that the probability of the complement of an event, such as neither A nor B occurring, is 1 minus the probability of A or B, or P(not A and not B) = 1 - P(A or B). Thus, the complement rule effectively helps calculate the probabilities related to mutually exclusive events.