Add the probabilities of the two events. If they're not mutually exclusive, then you need to subtract the probability that they both occur together.
To find the experimental probability of an event you carry out an experiment or trial a very large number of times. The experimental probability is the proportion of these in which the event occurs.
experimental probability
Odds of A to B in favour of an event states that for every A times an event occurs, the event does not occur B times. So, out of (A+B) trials, A are favourable to the event. that is, the probability of A is A/(A+B).
Two events complementary when one event occurs if and only if the other does not. Simple event do not depend on other events, it consists of on and only one outcome Doctor Chuck aka mathdoc Two events complementary when one event occurs if and only if the other does not. Simple event do not depend on other events, it consists of on and only one outcome Doctor Chuck aka mathdoc
For the first 5 to appear on the tenth roll, the first nine rolls must not be a 5 and the tenth must. Therefore: probability = (5/6)9 x 1/6 ≈ 0.0323 = 3.23 %
No, if two events are mutually exclusive, they cannot both occur. If one occurs, it means the second can not occur.
Mutually exclusive events are occurrences where, say, a couple of propositions are possible, but if one occurs, the other cannot. A coin toss might be a good example. A coin lands heads or it lands tails. It cannot land on both in the same toss. A coin toss, therefore, can be said to be a mutually exclusive event.
Mutually exclusive events are occurrences where, say, a couple of propositions are possible, but if one occurs, the other cannot. A coin toss might be a good example. A coin lands heads or it lands tails. It cannot land on both in the same toss. A coin toss, therefore, can be said to be a mutually exclusive event.
If A and B are mutually exclusive, P(A or B)=P(A) + P(B) They both cannot occur together. For example: A die is rolled. A = an odd number; B= number is divisible by 2. P(A or B) = 1/3 + 1/3 = 2/3
Two events are mutually exclusive if the occurrence of one event implies that the other cannot occur. There is no need for either to occur.For example, if you roll a die and the two outcomes of interest are:A - you roll a primeB - you roll a compositethen A and B cannot occur together. Of course, you could roll a 1, so that neither A nor B occurs.An example of events that are not mutually exclusive is:A - you roll a primeC - you roll an even numberIf you roll a 2 then both A and C occur.
Basic Rules of Probability:1) The probability of an event (E) is a number (fraction or decimal) between and including 0 and 1. (0≤P(E)≤1)2) If an event (E) cannot occur its probability is 0.3) If an event (E) is certain to occur, then the probability if E is 1. This means that there is a 100% chance that something will occur.4) The sum of probabilities of all the outcomes in the sample space is 1.Addition Rules/Formulas:When two events (A and B) are mutually exclusive, meaning that they can't occur at the same time or they have no outcomes in common, the probability that A or B will occur is:P(A or B)= P(A)+P(B)If A and B are not mutually exclusive, then:P(A or B)= P(A)+P(B)-P(A and B)Multiplication Rules/Formulas:When two events (A and B) are independent events, meaning the fact that A occurs does not affect the probability of B occurring (for example flipping a coin, rolling a die, or picking a card), the probability of both occurring is:P(A and B)= P(A)P(B)Conditional Probability-When two events are dependent (not independent), the probability of both occurring is:P(A or B)= P(A)P(B|A)Note: P(B|A) does not mean B divided by A but the probability of B after A.
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.
That depends on your definition of "depends." Mutually exclusive events are events that cannot occur at the same time. If you knew that Independent events most certainly can happen at the same time, you could easily deduce that mutually exclusive events are always dependent events. And while it's true dependent events affect the outcome of one another, that's not so easy to see when your dealing with events that don't occur in succession.It can be said that if a mutually exclusive event occurs, the other events that are mutually exclusive in relation to it have not taken place, i.e. the complement of that event has not taken place. When you look at only two events that are mutually exclusive and jointly exhaustive (i.e. all the possible events) like flipping a coin once and getting either a head or a tails (where the probability of the coin landing on it's side is 0), you can say that one event, flipping a head, is dependent on the other event, flipping a tail, not happening. Therefore the events are mutually exclusive.Now imagine two events which are still mutually exclusive but not jointly exhaustive, e.g. rolling a 2 or a 3 with a six sided die. Lets assume the die is not weighted so the probability of each is 1/6. A roll of two does not only depend on not rolling a three. To roll a 2 means not rolling a 1,3,4,5 or 6. To say that rolling a 2 and rolling a 3 are mutually exclusive if the occurrence one depends on the occurrence of the other is ambiguous at best, if not wrong. Rolling a 2 and rolling a 3 are mutually exclusive only because its impossible for both to happen at the same time with one roll, or you can say that P(2and3)=0.It's fair to say that two events are mutually exclusive if the occurrence of one depends on the other not happening. But if you thought that two events are mutually exclusive because the occurrence of one relays on the occurrence of the other then you were wrong. That just describes dependent events in succession.If one event's occurence depends upon the occurence of another, and the events cannot occur with a certain outcome otherwise, they are said to be dependent events. Mutually exclusive events are events that cannot occur together, as the occurence of one prohibits the occurence of the other. An example of a mutually exclusive event is this: two dice are rolled; what is the possibility of rolling both a nine and a double? One cannot roll both a nine and a double simultaneously; therefore, the events are mutually exclusive because one outcome excludes the other. An example of a dependent event is this: Susan is baking cookies. She has enough batter for two dozen chocolate chip cookies and one dozen oatmeal cookies. Therefore, the ratio of chocolate chip to oatmeal is 1.5:1. If Susan's little brother eats half of the chocolate chip cookies, the ratio changes to become 1:1. The possibility of the ratio being 1:1 is dependent upon Susan's brother eating half of the chocolate chip cookies. Thus, it is a dependent event. If one event's occurence depends upon the occurence of another, and the events cannot occur with a certain outcome otherwise, they are said to be dependent events. Mutually exclusive events are events that cannotoccur together, as the occurence of one prohibits the occurence of the other. An example of a mutually exclusive event is this: two dice are rolled; what is the possibility of rolling both a nine and a double? One cannot roll both a nine and a double simultaneously; therefore, the events are mutually exclusive because one outcome excludes the other. An example of a dependent event is this: Susan is baking cookies. She has enough batter for two dozen chocolate chip cookies and one dozen oatmeal cookies. Therefore, the ratio of chocolate chip to oatmeal is 1.5:1. If Susan's little brother eats half of the chocolate chip cookies, the ratio changes to become 1:1. The possibility of the ratio being 1:1 is dependent upon Susan's brother eating half of the chocolate chip cookies. Thus, it is a dependent event. If one event's occurence depends upon the occurence of another, and the events cannot occur with a certain outcome otherwise, they are said to be dependent events. Mutually exclusive events are events that cannotoccur together, as the occurence of one prohibits the occurence of the other. An example of a mutually exclusive event is this: two dice are rolled; what is the possibility of rolling both a nine and a double? One cannot roll both a nine and a double simultaneously; therefore, the events are mutually exclusive because one outcome excludes the other. An example of a dependent event is this: Susan is baking cookies. She has enough batter for two dozen chocolate chip cookies and one dozen oatmeal cookies. Therefore, the ratio of chocolate chip to oatmeal is 1.5:1. If Susan's little brother eats half of the chocolate chip cookies, the ratio changes to become 1:1. The possibility of the ratio being 1:1 is dependent upon Susan's brother eating half of the chocolate chip cookies. Thus, it is a dependent event.Mutually exclusive events refers to the events that cannot occur at the same time.
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True