This law can be proved using Kronecker's lemma and a theorem that states that the variance of a sequence of independent random variables divided by the sum of n2 ["n squared"] is finite; that theorem is Kolmogorov's three-series theorem.
This law can be proved using Kronecker's lemma and a theorem that states that the variance of a sequence of independent random variables divided by the sum of n2 ["n squared"] is finite; that theorem is Kolmogorov's three-series theorem. One form of this law sometimes named for Markov relaxes its requirement for  (*) independently and identically distributed samples. It's not the central limit theorem, but one form of this statement says that its desired result will converge almost surely, while its contrasting "weak" form converges in probability and can be proved by Chebyshev's inequality. For 10 points, state this probabilistic theorem that says that as n grows, the mean of a group of samples converges to its hypothetical true mean.
This law can be proved using Kronecker's lemma and a theorem that states that the variance of a sequence of independent random variables divided by the sum of n2 ["n squared"] is finite; that theorem is Kolmogorov's three-series theorem.
law of large numbers [prompt on "law of averages," accept strong or weak law of large numbers, accept LLN, accept Bernoulli's Theorem] 