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What is the Probability distribution when all possible samples of size n are repeatedly drawn from a population?
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Explain probability distribution?
The probability distribution of an experiment is a function that maps the probability of each possible outcome of the experiment to that outcome.
How do you obtain a 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.Find all the possible outcomes and the probabilities associated with each. That information comprises the probability distribution.
What is a listing of all possible outcomes of an experiment and their corresponding probability of occurrence is called?
It is the probability distribution.
What differentiates a probability from a probability distribution?
A probability indicates the likely-hood that a particular event occurs out of a set number of observations or measurements. A probability distribution allows relative comparison of probability of an event with any other possible event.
Is in the normal distribution the total area beneath the curve represent the probability for all possible outcomes for a given event?
Yes. The total area under any probability distribution curve is always the probability of all possible outcomes - which is 1.
What does it mean for a probability to be fair?
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.
What re the three criteria needed for something to be a probability distribution?
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.
How many experimental outcomes are possible for the binomial and the Poisson distributions?
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.
What the ethical issues in probability distribution?
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.
What is of discrete uniform distribution?
A discrete uniform distribution assigns the same probability to two or more possible events. For example, there is a discrete uniform distribution associated with flipping a coin: 'heads' is assigned a probability of 1/2 as is the event 'tails'. (Note that the probabilities are equal or 'uniform'.) There is also a discrete uniform distribution associated with tossing a die in that there is a 1/6 probability for seeing each possible side of the die.
What are the 2 conditions that determine a probability distribution?
The value of the distribution for any value of the random variable must be in the range [0, 1]. The sum (or integral) of the probability distribution function over all possible values of the random variable must be 1.
A complete probability distribution is always an objective listing of all possible events Since it is impossible to list all the possible outcomes from a single event probability distributions are o?
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.
