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A probability distribution is a function that describes the probability of obtaining a certain outcome where the outcomes are not equally likely. There is a fixed probability of getting each outcome, but the probabilities are not necessarily equal. For example, roll 2 dice, there are 36 equally likely outcomes with a probability of each occurring being 1/36. However if we look at the sum of the numbers, there is only one outcome that gives a sum of 2 (1&1) , so P(sum 2) = 1/36, but six outcomes that give the sum of 7 (1&6, 2&5, 3&4, 4&3, 5&2, 6&1), so P(sum 7) = 6/36 = 1/6.
Probability distributions can be tabulated, or there are functions that can be used to calculate the probabilities of getting each outcome.
A probability distribution is a function that describes the probability of obtaining a certain outcome where the outcomes are not equally likely. There is a fixed probability of getting each outcome, but the probabilities are not necessarily equal. For example, roll 2 dice, there are 36 equally likely outcomes with a probability of each occurring being 1/36. However if we look at the sum of the numbers, there is only one outcome that gives a sum of 2 (1&1) , so P(sum 2) = 1/36, but six outcomes that give the sum of 7 (1&6, 2&5, 3&4, 4&3, 5&2, 6&1), so P(sum 7) = 6/36 = 1/6.
Probability distributions can be tabulated, or there are functions that can be used to calculate the probabilities of getting each outcome.
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What are the Similarities of discrete probability distribution and continuous probability distribution?
They are probability distributions!
Why if a probability distribution curve is bell shaped why is this a normal distribution?
A bell shaped probability distribution curve is NOT necessarily a normal distribution.
How do you convert a beta probability density function to a beta frequency distribution?
probability density distribution
The total area of a normal probability distribution is?
The total area of any probability distribution is 1
Is the uniform probability distribution is symmetric about the mode?
Yes, the uniform probability distribution is symmetric about the mode. Draw the sketch of the uniform probability distribution. If we say that the distribution is uniform, then we obtain the same constant for the continuous variable. * * * * * The uniform probability distribution is one in which the probability is the same throughout its domain, as stated above. By definition, then, there can be no value (or sub-domain) for which the probability is greater than elsewhere. In other words, a uniform probability distribution has no mode. The mode does not exist. The distribution cannot, therefore, be symmetric about something that does not exist.
