Q: Events that have no effect on each others probability?

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Two events are equally unlikely if the probability that they do not happen is the same for each event. And, since the probability of an event happening and not happening must add to 1, equally unlikely events are also equally likely,

These would be independent events; therefore, we can multiply the probabilities of each of the two events. Probability of flipping a head: 1/2 Probability of rolling an odd number with a single die: 1/6 Required probability : 1/2 x 1/6 = 1/12

I believe you mean to say, equally probable. By stating they are separate events, I assume that they are independent and that there is a single unique outcome to each event that can be identified. Ok, then the chance of each event or outcome is 1/10.

The probability of getting heads on three tosses of a coin is 0.125. Each head has a probability of 0.5. Since the events are sequentially unrelated, simply raise 0.5 to the power of the number of tosses (3) and get 0.125, or 1 in 8.

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.

Related questions

No, the combined probability is the product of the probability of their separate occurrances.

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Two events are equally unlikely if the probability that they do not happen is the same for each event. And, since the probability of an event happening and not happening must add to 1, equally unlikely events are also equally likely,

Coin tosses are what we call Independent Events, meaning that the results of one toss have no effect on the next toss or any thereafter. Therefore the probability of each toss is 1/2. If, however, you want to know the probability of tossing two coins, and each coin landing heads-up, you simply multiply their probabilities together, resulting in 1/4.

The probability of getting a heads on the first flip is 1/2. Similarly, the probability on each subsequent flip is 1/2, since they are independent events. The probability of several independent events happening together is the product of their individual probabilities.

Dickens gave the ghosts a theme. Ech was to take on a timeline of Scrooges life and in each time line was to opens Scrooges eyes to the events of his life and the effect those events had on him and others

There are ten possible events: that the spinner shows one of the number from 1 to 10. The probability of each of these events is the same and equals 1/10, 0.1 or 10%

A probability distribution describes the likelihood of different outcomes in a random experiment. It shows the possible values of a random variable along with the probability of each value occurring. Different probability distributions (such as uniform, normal, and binomial) are used to model various types of random events.

The probability of winning two games with the same probability of 0.8 can be calculated by multiplying the probability of winning the first game (0.8) by the probability of winning the second game (0.8). Therefore, the probability is 0.8 * 0.8 = 0.64, or 64%.

These would be independent events; therefore, we can multiply the probabilities of each of the two events. Probability of flipping a head: 1/2 Probability of rolling an odd number with a single die: 1/6 Required probability : 1/2 x 1/6 = 1/12

Discrete probability is probability in the context of random variables that can only take a discrete number of values. In the work environment there are some events that can have discrete outcomes and others that are continuous. For example, the number of customers in a day (or month) is a discrete variable. The amount that each one spends on your products is discrete. But the time interval between them is not. So the role that any kind of probability could play depends on what you wish to study.

Let's say there are 52 cards in a deckThere's one chance out of 52 to get the two of clubs from the first deckThe same chance with the second deckThe probability that the two events occur at the same time is the probability of the first multiplied by the probability of the second as the events are independentSo P = 1/52 x 1/52 = 1/2074