enow.com Web Search

Search results

  1. Results from the WOW.Com Content Network
  2. 68–95–99.7 rule - Wikipedia

    en.wikipedia.org/wiki/68–95–99.7_rule

    In statistics, the 68–95–99.7 rule, also known as the empirical rule, and sometimes abbreviated 3sr, is a shorthand used to remember the percentage of values that lie within an interval estimate in a normal distribution: approximately 68%, 95%, and 99.7% of the values lie within one, two, and three standard deviations of the mean, respectively.

  3. Empirical distribution function - Wikipedia

    en.wikipedia.org/.../Empirical_distribution_function

    In statistics, an empirical distribution function (commonly also called an empirical cumulative distribution function, eCDF) is the distribution function associated with the empirical measure of a sample. [1] This cumulative distribution function is a step function that jumps up by 1/n at each of the n data points. Its value at any specified ...

  4. Freedman–Diaconis rule - Wikipedia

    en.wikipedia.org/wiki/Freedman–Diaconis_rule

    A formula which was derived earlier by Scott. [2] Swapping the order of the integration and expectation is justified by Fubini's Theorem. The Freedman–Diaconis rule is derived by assuming that is a Normal distribution, making it an example of a normal reference rule.

  5. Empirical measure - Wikipedia

    en.wikipedia.org/wiki/Empirical_measure

    In probability theory, an empirical measure is a random measure arising from a particular realization of a (usually finite) sequence of random variables. The precise definition is found below. The precise definition is found below.

  6. Estimating equations - Wikipedia

    en.wikipedia.org/wiki/Estimating_equations

    In statistics, the method of estimating equations is a way of specifying how the parameters of a statistical model should be estimated.This can be thought of as a generalisation of many classical methods—the method of moments, least squares, and maximum likelihood—as well as some recent methods like M-estimators.

  7. Kolmogorov–Smirnov test - Wikipedia

    en.wikipedia.org/wiki/Kolmogorov–Smirnov_test

    Illustration of the Kolmogorov–Smirnov statistic. The red line is a model CDF, the blue line is an empirical CDF, and the black arrow is the KS statistic.. In statistics, the Kolmogorov–Smirnov test (also K–S test or KS test) is a nonparametric test of the equality of continuous (or discontinuous, see Section 2.2), one-dimensional probability distributions.

  8. Mean squared error - Wikipedia

    en.wikipedia.org/wiki/Mean_squared_error

    In machine learning, specifically empirical risk minimization, MSE may refer to the empirical risk (the average loss on an observed data set), as an estimate of the true MSE (the true risk: the average loss on the actual population distribution). The MSE is a measure of the quality of an estimator.

  9. Empirical probability - Wikipedia

    en.wikipedia.org/wiki/Empirical_probability

    More generally, empirical probability estimates probabilities from experience and observation. [ 2 ] Given an event A in a sample space, the relative frequency of A is the ratio ⁠ m n , {\displaystyle {\tfrac {m}{n}},} ⁠ m being the number of outcomes in which the event A occurs, and n being the total number of outcomes of the experiment.