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  2. Binomial coefficient - Wikipedia

    en.wikipedia.org/wiki/Binomial_coefficient

    This formula follows from the multiplicative formula above by multiplying numerator and denominator by (n − k)!; as a consequence it involves many factors common to numerator and denominator. It is less practical for explicit computation (in the case that k is small and n is large) unless common factors are first cancelled (in particular ...

  3. Binomial distribution - Wikipedia

    en.wikipedia.org/wiki/Binomial_distribution

    for k = 0, 1, 2, ..., n, where =!! ()! is the binomial coefficient. The formula can be understood as follows: p k q n−k is the probability of obtaining the sequence of n independent Bernoulli trials in which k trials are "successes" and the remaining n − k trials

  4. Order statistic - Wikipedia

    en.wikipedia.org/wiki/Order_statistic

    Probability density functions of the order statistics for a sample of size n = 5 from an exponential distribution with unit scale parameter. In statistics, the kth order statistic of a statistical sample is equal to its kth-smallest value. [1]

  5. Normal distribution - Wikipedia

    en.wikipedia.org/wiki/Normal_distribution

    The multivariate normal distribution describes the Gaussian law in the k-dimensional Euclidean space. A vector X ∈ R k is multivariate-normally distributed if any linear combination of its components Σ k j=1 a j X j has a (univariate) normal distribution. The variance of X is a k×k symmetric positive-definite matrix V.

  6. Sample mean and covariance - Wikipedia

    en.wikipedia.org/wiki/Sample_mean_and_covariance

    The sample (2, 1, 0), for example, would have a sample mean of 1. If the statistician is interested in K variables rather than one, each observation having a value for each of those K variables, the overall sample mean consists of K sample means for individual variables.

  7. Cohen's kappa - Wikipedia

    en.wikipedia.org/wiki/Cohen's_kappa

    Cohen's kappa measures the agreement between two raters who each classify N items into C mutually exclusive categories. The definition of is =, where p o is the relative observed agreement among raters, and p e is the hypothetical probability of chance agreement, using the observed data to calculate the probabilities of each observer randomly selecting each category.

  8. Weibull distribution - Wikipedia

    en.wikipedia.org/wiki/Weibull_distribution

    For k > 1, the density function tends to zero as x approaches zero from above, increases until its mode and decreases after it. The density function has infinite negative slope at x = 0 if 0 < k < 1, infinite positive slope at x = 0 if 1 < k < 2 and null slope at x = 0 if k > 2. For k = 1 the density has a finite negative slope at x = 0.

  9. Degrees of freedom (statistics) - Wikipedia

    en.wikipedia.org/.../Degrees_of_freedom_(statistics)

    Then, at each of the n measured points, the weight of the original value on the linear combination that makes up the predicted value is just 1/k. Thus, the trace of the hat matrix is n/k. Thus the smooth costs n/k effective degrees of freedom. As another example, consider the existence of nearly duplicated observations.