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The rule is often called Chebyshev's theorem, about the range of standard deviations around the mean, in statistics. The inequality has great utility because it can be applied to any probability distribution in which the mean and variance are defined. For example, it can be used to prove the weak law of large numbers.
Chebyshev's theorem is any of several theorems proven by Russian mathematician Pafnuty Chebyshev. Bertrand's postulate, that for every n there is a prime between n and 2n. Chebyshev's inequality, on the range of standard deviations around the mean, in statistics; Chebyshev's sum inequality, about sums and products of decreasing sequences
This theorem makes rigorous the intuitive notion of probability as the expected long-run relative frequency of an event's occurrence. It is a special case of any of several more general laws of large numbers in probability theory. Chebyshev's inequality. Let X be a random variable with finite expected value μ and finite non-zero variance σ 2.
Range Expected fraction of population inside range Expected fraction of population outside range Approx. expected frequency outside range Approx. frequency outside range for daily event μ ± 0.5σ: 0.382 924 922 548 026: 0.6171 = 61.71 % 3 in 5 Four or five times a week μ ± σ: 0.682 689 492 137 086 [5] 0.3173 = 31.73 % 1 in 3 Twice or ...
His conjecture was completely proved by Chebyshev (1821–1894) in 1852 [3] and so the postulate is also called the Bertrand–Chebyshev theorem or Chebyshev's theorem. Chebyshev's theorem can also be stated as a relationship with π ( x ) {\displaystyle \pi (x)} , the prime-counting function (number of primes less than or equal to x ...
Anti-concentration inequalities, on the other hand, provide an upper bound on how much a random variable can concentrate, either on a specific value or range of values. A concrete example is that if you flip a fair coin n {\displaystyle n} times, the probability that any given number of heads appears will be less than 1 n {\displaystyle {\frac ...
In statistics, the method of moments is a method of estimation of population parameters.The same principle is used to derive higher moments like skewness and kurtosis. It starts by expressing the population moments (i.e., the expected values of powers of the random variable under consideration) as functions of the parameters of interest.
In probability theory, the multidimensional Chebyshev's inequality [1] is a generalization of Chebyshev's inequality, which puts a bound on the probability of the event that a random variable differs from its expected value by more than a specified amount.