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Cochran's theorem then states that Q 1 and Q 2 are independent, with chi-squared distributions with n − 1 and 1 degree of freedom respectively. This shows that the sample mean and sample variance are independent.
Let z 0 be a root of a holomorphic function f, and let n be the least positive integer such that the n th derivative of f evaluated at z 0 differs from zero. Then the power series of f about z 0 begins with the n th term, and f is said to have a root of multiplicity (or “order”) n. If n = 1, the root is called a simple root. [4]
The simplest bootstrap method involves taking the original data set of heights, and, using a computer, sampling from it to form a new sample (called a 'resample' or bootstrap sample) that is also of size N. The bootstrap sample is taken from the original by using sampling with replacement (e.g. we might 'resample' 5 times from [1,2,3,4,5] and ...
Bézout's theorem asserts that n homogeneous polynomials of degree , …, in n + 1 indeterminates define either an algebraic set of positive dimension, or a zero-dimensional algebraic set consisting of points counted with their multiplicities.
The multinomial distribution models the outcome of n experiments, where the outcome of each trial has a categorical distribution, such as rolling a k-sided die n times. Let k be a fixed finite number. Mathematically, we have k possible mutually exclusive outcomes, with corresponding probabilities p 1, ..., p k, and n independent trials.
Neyman allocation, also known as optimum allocation, is a method of sample size allocation in stratified sampling developed by Jerzy Neyman in 1934. This technique determines the optimal sample size for each stratum to minimize the variance of the estimated population parameter for a fixed total sample size and cost.
These three multiplicities define three multisets of eigenvalues, which may be all different: Let A be a n × n matrix in Jordan normal form that has a single eigenvalue. Its multiplicity is n, its multiplicity as a root of the minimal polynomial is the size of the largest Jordan block, and its geometric multiplicity is the number of Jordan blocks.
The first row shows the possible p-values as a function of the number of blue and red dots in the sample. Although the 30 samples were all simulated under the null, one of the resulting p-values is small enough to produce a false rejection at the typical level 0.05 in the absence of correction.