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It is a probability distribution where when all of the values of a random variable occur with equal probability.
Say X is the random variable, such as what number shows up when we roll a die.
There are 6 possible outcomes, each with a 1/6 probability of showing up.
If we create a probability distribution where X= 1,2,3,4,5, or 6, we note
P(X=k)=1/k where k is any number between 1 and 6 in this case.
The graph will be a rectangle.
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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.
Can the Empirical Rule of probability be applied to the uniform probability distribution?
Yes, except that if you know that the distribution is uniform there is little point in using the empirical rule.
The shape of any uniform probability distribution is?
Rectangular
Is the uniform probability distribution is symmetric about the mean and median?
yes
The probability density function for a uniform distribution ranging between 2 and 6 is?
4
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.
What is a model in which each outcome has an equal probability of occurring?
A uniform distribution.A uniform distribution.A uniform distribution.A uniform distribution.
Can the Empirical Rule of probability be applied to the uniform probability distribution?
Yes, except that if you know that the distribution is uniform there is little point in using the empirical rule.
Is the uniform probability distribution's standard deviation proportional to the distribution's range?
no
The shape of any uniform probability distribution is?
Rectangular
Is the uniform probability distribution?
This appears to be an incomplete question.
What does uniform probability mean in math?
Uniform probability can refer to a discrete probability distribution for which each outcome has the same probability. For a continuous distribution, it requires that the probability of the outcome is directly proportional to the range of values in the desired outcome (compared to the total range).
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.
Is the uniform probability distribution is symmetric about the mean and median?
yes
What does uniform mean in math?
Uniform probability can refer to a discrete probability distribution for which each outcome has the same probability. For a continuous distribution, it requires that the probability of the outcome is directly proportional to the range of values in the desired outcome (compared to the total range).
What is the important similarity between the uniform and normal probability distribution?
They are both continuous, symmetric distribution functions.
What is an important difference between the uniform and normal probability distributions?
The uniform distribution is limited to a finite domain, the normal is not.
