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Q: Is in the normal distribution the total area beneath the curve represent the probability for all possible outcomes for a given event?

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It is always non-negative. The sum (or integral) over all possible outcomes is 1.

A probability distribution must have a well defined domain - that is, the set of possible outcomes.For each possible outcome, there must be a non-negative value associated - the probability of that outcome.The sum of the probabilities, over all possible outcomes, must be 1.

Your question is not clear, but I will attempt to interpret it as best I can. When you first learn about probability, you are taught to list out the possible outcomes. If all outcomes are equally probable, then the probability is easy to calculate. Probability distributions are functions which provide probabilities of events or outcomes. A probability distribution may be discrete or continuous. The range of both must cover all possible outcomes. In the discrete distribution, the sum of probabilities must add to 1 and in the continuous distribtion, the area under the curve must sum to 1. In both the discrete and continuous distributions, a range (or domain) can be described without a listing of all possible outcomes. For example, the domain of the normal distribution (a continuous distribution is minus infinity to positive infinity. The domain for the Poisson distribution (a discrete distribution) is 0 to infinity. You will learn in math that certain series can have infinite number of terms, yet have finite results. Thus, a probability distribution can have an infinite number of events and sum to 1. For a continuous distribution, the probability of an event are stated as a range, for example, the probability of a phone call is between 4 to 10 minutes is 10% or probability of a phone call greater than 10 minutes is 60%, rather than as a single event.

A probability assignment.

It is the outcome space.

Related questions

Find all the possible outcomes and the probabilities associated with each. That information comprises the probability distribution.Find all the possible outcomes and the probabilities associated with each. That information comprises the probability distribution.Find all the possible outcomes and the probabilities associated with each. That information comprises the probability distribution.Find all the possible outcomes and the probabilities associated with each. That information comprises the probability distribution.

A probability is fair if there is no bias in any of the possible outcomes. Said another way, all of the possible outcomes in a fair distribution have an equal probability.

It is the probability distribution.

The binomial distribution is a discrete probability distribution. The number of possible outcomes depends on the number of possible successes in a given trial. For the Poisson distribution there are Infinitely many.

It is the theoretical probability of the event.

The probability of an event occurring can be found by dividing the number of favorable outcomes (what you want to happen) by the number of possible outcomes number of favorable outcomes probability = _________________________ number of possible outcomes

It is always non-negative. The sum (or integral) over all possible outcomes is 1.

you ether use a graph tree diagram or web diagram to answer the possible outcomes of the question possible outcomes meaning the number of outcomes the person will have in the probability or divide the number of favourable outcomes by the number of possible outcomes favorible outcomes meaning the number of outcomes all together

Probability distribution is when all the possible outcomes of a random variation are gathered together and the probability of each outcome is figured out. There are several ethical issues with this one being that it is not always accurate information that is gathered.

A probability distribution must have a well defined domain - that is, the set of possible outcomes.For each possible outcome, there must be a non-negative value associated - the probability of that outcome.The sum of the probabilities, over all possible outcomes, must be 1.

Your question is not clear, but I will attempt to interpret it as best I can. When you first learn about probability, you are taught to list out the possible outcomes. If all outcomes are equally probable, then the probability is easy to calculate. Probability distributions are functions which provide probabilities of events or outcomes. A probability distribution may be discrete or continuous. The range of both must cover all possible outcomes. In the discrete distribution, the sum of probabilities must add to 1 and in the continuous distribtion, the area under the curve must sum to 1. In both the discrete and continuous distributions, a range (or domain) can be described without a listing of all possible outcomes. For example, the domain of the normal distribution (a continuous distribution is minus infinity to positive infinity. The domain for the Poisson distribution (a discrete distribution) is 0 to infinity. You will learn in math that certain series can have infinite number of terms, yet have finite results. Thus, a probability distribution can have an infinite number of events and sum to 1. For a continuous distribution, the probability of an event are stated as a range, for example, the probability of a phone call is between 4 to 10 minutes is 10% or probability of a phone call greater than 10 minutes is 60%, rather than as a single event.

Calculating Probabilities for Equally Likely OutcomesStep 1: Count the total number of possible outcomes of an eventStep 2: Count the number of outcomes that represent a success - that is, the number of outcomes that represent the desired result.Step 3: Determine the probability of success by dividing the number of successes by the total number of possible outcomes:

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