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There are two main methods: theoretical and empirical.

The first of these is used when there is a relatively simple model for the possible outcomes of a trial. For example, if you roll a fair die, laws of physics suggest that each of the six faces is equally likely to end up on the top face. The probability for each of the six numbers is, therefore, 1/6.

The second method is used when there is no satisfactory model based purely on theory: if you have a loaded die, for example. It may, just about, be possibly to analyse the physical properties of the mass distribution within the coin and develop an appropriate model for the outcome of a throw. However, it is simpler to use the second method. Given the chance, roll the die again and again (and again and again and ... ) and record the outcomes. The probability of any particular outcome is the proportion of the trials that result in that particular event. Thus, if a loaded die comes up 6 fifty times out of 200 throws, then the probability of throwing a 6 is 50/200 = 0.25.

Q: How would you calculate the probability of an event occurring?

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Multiply together the probability that each event would have of occurring by itself. For example, the probability of rolling a "3" on a single die is 1/6 ,because there are 6 different possibilities. And the probability of flipping a "heads" on a coin is 1/2 , because there are two possibilities. Then the probability of rolling a "3" AND flipping a "heads" is ; 1/6 x 1/2 = 1/12 .

Joint probability is the probability that two or more specific outcomes will occur in an event. An example of joint probability would be rolling a 2 and a 5 using two different dice.

uummm!!!! The probability would maybe close to 5 or 4. * * * * * The answer is clearly incorrect because the probability of an event cannot be greater than 1. The actual probability, assuming the dice are fair, is 4/36 = 1/9 = 11.11...%

Yes, probability can be expressed as a percent. It is common to express probabilities as a percentage, which is calculated by multiplying the probability by 100. For example, if the probability of an event is 0.25, it can also be expressed as 25%.

The answer depends on the nature of the event. If the event is composed of sub-events then a tree diagram may help but if not, it is irrelevant.

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When an event is repeated, the probability of it occurring is squared. For instance, if an outcome had the probability of 1/4, then the outcome happening twice would have a probability of 1/16. Note, however, that this does not mean that the second event has different probabilities. That particular outcome will always be 1/4, regardless of anything that happened before it.

Multiply together the probability that each event would have of occurring by itself. For example, the probability of rolling a "3" on a single die is 1/6 ,because there are 6 different possibilities. And the probability of flipping a "heads" on a coin is 1/2 , because there are two possibilities. Then the probability of rolling a "3" AND flipping a "heads" is ; 1/6 x 1/2 = 1/12 .

Yes. It is a certain event. If a coin is tossed, a 'head or tail would roll' is a certain event and has probability 1.

The likelihood that something will happen refers to the probability or chance of that event occurring. It is often quantified on a scale from 0 (impossible) to 1 (certain). The higher the likelihood, the greater the probability of the event occurring.

I haven't heard of a component with regards to statistics. If, by chance, you are referring to the complement, it is the probability that the event does not occur. In this case, the complement would be 0.58.

The probability of a threat is 1. The threat exists. What is important is not the threat but the probability that the threatened event happens.

The two factors that determine risk level are the likelihood of a specific threat occurring and the impact that threat would have if it occurs. By assessing both the probability of an event happening and the consequences of that event, organizations can better understand and manage risks.

Probability concerns with estimating a likelyhood for an event to either yet to happen or would have happened.

Complementary events are events that are the complete opposite. The compliment of event A is everything that is not event A. For example, the complementary event of flipping heads on a coin would be flipping tails. The complementary event of rolling a 1 or a 2 on a six-sided die would be rolling a 3, 4, 5, or 6. (The probability of A compliment is equal to 1 minus the probability of A.)

The time for the event has passed so there is no need for a probability. It either did snow so the event was a certainty, or it did not and so it was not.

Joint probability is the probability that two or more specific outcomes will occur in an event. An example of joint probability would be rolling a 2 and a 5 using two different dice.

This would be a probability factor.