normal probability distribution การใช้

• First, for an ordinary normal probability distribution M ( X ) represents it.
• These arise as moments of normal probability distributions : The " n "-th moment of the normal distribution with expected value and variance 2 is
• My exercise involves normal probability distribution and chi-square test, but I hadn't read up on either of them when I submitted the idea.
• Since real-world quantities are often the balanced sum of many unobserved random events, the central limit theorem also provides a partial explanation for the prevalence of the normal probability distribution.
• Unlike multiplicative fluctuations, " additive " fluctuations do not lead to Benford's law : They lead instead to normal probability distributions ( again by the central limit theorem ), which do not satisfy Benford's law.
• As required, even though \ mu appears as an argument to the function g, the distribution of g ( \ mu, X ) does not depend on the parameters \ mu or \ sigma of the normal probability distribution that governs the observations X _ 1, \ ldots, X _ n.