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)
Addition Theorem The addition rule is a result used to determine the probability that event A or event B occurs or both occur. ; The result is often written as follows, using set notation: : ; where: : P(A) = probability that event A occurs : P(B) = probability that event B occurs : = probability that event A or event B occurs : = probability that event A and event B both occur ; For mutually exclusive events, that is events which cannot occur together: : = 0 ; The addition rule therefore reduces to : = P(A) + P(B) ; For independent events, that is events which have no influence on each other: : ; The addition rule therefore reduces to : ; Example ; Suppose we wish to find the probability of drawing either a king or a spade in a single draw from a pack of 52 playing cards. ; We define the events A = 'draw a king' and B = 'draw a spade' ; Since there are 4 kings in the pack and 13 spades, but 1 card is both a king and a spade, we have: : = 4/52 + 13/52 - 1/52 = 16/52 ; So, the probability of drawing either a king or a spade is 16/52 (= 4/13).MultiplicationTheorem The multiplication rule is a result used to determine the probability that two events, A and B, both occur. The multiplication rule follows from the definition of conditional probability. ; The result is often written as follows, using set notation: : ; where: : P(A) = probability that event A occurs : P(B) = probability that event B occurs : = probability that event A and event B occur : P(A | B) = the conditional probability that event A occurs given that event B has occurred already : P(B | A) = the conditional probability that event B occurs given that event A has occurred already ; For independent events, that is events which have no influence on one another, the rule simplifies to: : ; That is, the probability of the joint events A and B is equal to the product of the individual probabilities for the two events.
Since each event is independent (heads in one coin does not affect the probability of the other two coin flips), the multiplication rule applies: 1/2 x 1/2 x 1/2 = 1/8 or 0.125. So we can say the probability is 12.5%.
Yes, except that if you know that the distribution is uniform there is little point in using the empirical rule.
The addition rule of probability states that the probability that one or the other will happen is the probability of one plus the probability of the other. This rule only applies to mutually exclusive events. For example, the probability that a dice roll will be a 3 is 1/6. The probability that the dice roll will be even is 1/2. These are mutually exclusive events as the dice cannot be both 3 and even. Thus the probability of the dice roll coming up either a 3, or even, is 1/2 + 1/6 = 2/3.
If the probability of A is p1 and probability of B is p2 where A and B are independent events or outcomes, then the probability of both A and B occurring is p1 x p2. See related link for examples.
States that to determine a probability, we multiply the probability of one event by the probability of the other event. Ex: Probability that two coins will land face heads up is 1/2 x 1/2 = 1/4 .
(3/20) * (2/19) = 6/380 = 0.01579 [General Rule of Multiplication]. B) What is the probability that the first two electric toothbrushes sold will not be defective? (17/20) * (16/19) = 272/380 = 0.7158 [General Rule of Multiplication].
Math Confusion Differences: One rule is special, another rule is general. Relationship: Both are part of the multiplication family.Know it..?!? THEY HAVE DIFFERENT RULES!!!answered by : REYMIAN
Addition Theorem The addition rule is a result used to determine the probability that event A or event B occurs or both occur. ; The result is often written as follows, using set notation: : ; where: : P(A) = probability that event A occurs : P(B) = probability that event B occurs : = probability that event A or event B occurs : = probability that event A and event B both occur ; For mutually exclusive events, that is events which cannot occur together: : = 0 ; The addition rule therefore reduces to : = P(A) + P(B) ; For independent events, that is events which have no influence on each other: : ; The addition rule therefore reduces to : ; Example ; Suppose we wish to find the probability of drawing either a king or a spade in a single draw from a pack of 52 playing cards. ; We define the events A = 'draw a king' and B = 'draw a spade' ; Since there are 4 kings in the pack and 13 spades, but 1 card is both a king and a spade, we have: : = 4/52 + 13/52 - 1/52 = 16/52 ; So, the probability of drawing either a king or a spade is 16/52 (= 4/13).MultiplicationTheorem The multiplication rule is a result used to determine the probability that two events, A and B, both occur. The multiplication rule follows from the definition of conditional probability. ; The result is often written as follows, using set notation: : ; where: : P(A) = probability that event A occurs : P(B) = probability that event B occurs : = probability that event A and event B occur : P(A | B) = the conditional probability that event A occurs given that event B has occurred already : P(B | A) = the conditional probability that event B occurs given that event A has occurred already ; For independent events, that is events which have no influence on one another, the rule simplifies to: : ; That is, the probability of the joint events A and B is equal to the product of the individual probabilities for the two events.
Since each event is independent (heads in one coin does not affect the probability of the other two coin flips), the multiplication rule applies: 1/2 x 1/2 x 1/2 = 1/8 or 0.125. So we can say the probability is 12.5%.
If it were not true, it would not have become the rule!
Yes, except that if you know that the distribution is uniform there is little point in using the empirical rule.
The addition rule of probability states that the probability that one or the other will happen is the probability of one plus the probability of the other. This rule only applies to mutually exclusive events. For example, the probability that a dice roll will be a 3 is 1/6. The probability that the dice roll will be even is 1/2. These are mutually exclusive events as the dice cannot be both 3 and even. Thus the probability of the dice roll coming up either a 3, or even, is 1/2 + 1/6 = 2/3.
1=1
"and" means multiplication "or" means addition
The rule says that you can change the order of multiplication: a x b = b x a. For example, 3 x 5 is the same as 5 x 3.
There is no rule other than that the order is unimportant for multiplication. xyz is the same as zyx and so forth. For example, 1 * 2 * 3 = 3 * 2 * 1 = 6.