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).
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.
Sum Rule: P(A) = \sum_{B} P(A,B) Product Rule: P(A , B) = P(A) P(B|A) or P(A, B)=P(B) P(A|B) [P(A|B) means probability of A given that B has occurred] P(A, B) = P(A) P(B) , if A and B are independent events.
The answer depends on what "this less than 5 percent rule" is, in contrast to some other 5 percent rule!
Yes, except that if you know that the distribution is uniform there is little point in using the empirical rule.
The complementary rule is a principle in probability theory stating that the probability of an event not occurring is equal to one minus the probability of the event occurring. Mathematically, it can be expressed as P(A') = 1 - P(A), where P(A') is the probability of the complement of event A, and P(A) is the probability of event A. This rule is useful for calculating probabilities when it's easier to determine the likelihood of an event not happening rather than the event itself.
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.
An empirical rule indicates a probability distribution function for a variable which is based on repeated trials.
The product rule states that the probability of two independent events occurring together is equal to the product of their individual probabilities. In genetics, the product rule is used to calculate the probability of inheriting multiple independent traits or alleles simultaneously from different parents.
The four basic rules of probability are: Non-negativity: The probability of any event is always between 0 and 1, inclusive. Normalization: The total probability of all possible outcomes in a sample space sums to 1. Additive Rule: For mutually exclusive events, the probability of either event occurring is the sum of their individual probabilities. Multiplicative Rule: For independent events, the probability of both events occurring is the product of their individual probabilities.
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 .
The addition rule is used when calculating the probability of two mutually exclusive events occurring together. For example, when calculating the probability of rolling a 2 or a 6 on a six-sided die, you would use the addition rule.
Sum Rule: P(A) = \sum_{B} P(A,B) Product Rule: P(A , B) = P(A) P(B|A) or P(A, B)=P(B) P(A|B) [P(A|B) means probability of A given that B has occurred] P(A, B) = P(A) P(B) , if A and B are independent events.
The answer depends on what "this less than 5 percent rule" is, in contrast to some other 5 percent rule!
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)