the variance of the uniform distribution function is 1/12(square of(b-a)) and the mean is 1/2(a+b).
The modes of a probability density function might be defined as the (countable) set of points in the domain of the function for which the function achieves local maxima. Since the probability density function for the uniform distribution is constant by definition it has no local maxima, hence no modes. Hence, it cannot be bimodal.
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le standard normal distribution is a normal distribution who has mean 0 and variance 1
The standard normal distribution is a normal distribution with mean 0 and variance 1.
Actually the normal distribution is the sub form of Gaussian distribution.Gaussian distribution have 2 parameters, mean and variance.When there is zero mean and unit variance the Gaussian distribution becomes normal other wise it is pronounced as Gaussian.Wrong! The standard normal distribution has mean 0 and variance 1, but a normal distribution is the same as the Gaussiand, and can have any mean and variance. Google stackexcange "what-is-the-difference-between-a-normal-and-a-gaussian-distribution"
the variance of the uniform distribution is (a+b)/12
The exponential distribution and the Poisson distribution.
Divide the total number of incidents by the total time. The result, representing the average number of incidents per unit of time, is the mean as well as the variance of the Poisson distribution.
The modes of a probability density function might be defined as the (countable) set of points in the domain of the function for which the function achieves local maxima. Since the probability density function for the uniform distribution is constant by definition it has no local maxima, hence no modes. Hence, it cannot be bimodal.
The term for uniform distribution refers to a statistical distribution where every outcome is equally likely to occur. In a continuous uniform distribution, this is represented by a constant probability density function over a specified interval. In discrete cases, each individual outcome has the same probability, making it a flat probability mass function. In both cases, the distribution is characterized by a lack of bias toward any particular value.
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It is exp(20t + 25/2*t^2).
See: http://en.wikipedia.org/wiki/Uniform_distribution_(continuous)
yes
A normal distribution can have any value for its mean and any positive value for its variance. A standard normal distribution has mean 0 and variance 1.
It is a discrete distribution in which the men and variance have the same value.
A uniform distribution.A uniform distribution.A uniform distribution.A uniform distribution.