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  2. Mean - Wikipedia

    en.wikipedia.org/wiki/Mean

    The arithmetic mean (or simply mean or average) of a list of numbers, is the sum of all of the numbers divided by their count.Similarly, the mean of a sample ,, …,, usually denoted by ¯, is the sum of the sampled values divided by the number of items in the sample.

  3. Average - Wikipedia

    en.wikipedia.org/wiki/Average

    The method of taking the mean for reducing observation errors was indeed mainly developed in astronomy. [5] [7] A possible precursor to the arithmetic mean is the mid-range (the mean of the two extreme values), used for example in Arabian astronomy of the ninth to eleventh centuries, but also in metallurgy and navigation. [6]

  4. Arithmetic mean - Wikipedia

    en.wikipedia.org/wiki/Arithmetic_mean

    The arithmetic mean can be similarly defined for vectors in multiple dimensions, not only scalar values; this is often referred to as a centroid. More generally, because the arithmetic mean is a convex combination (meaning its coefficients sum to ), it can be defined on a convex space, not only a vector space.

  5. Moving average - Wikipedia

    en.wikipedia.org/wiki/Moving_average

    Smoothing of a noisy sine (blue curve) with a moving average (red curve). In statistics, a moving average (rolling average or running average or moving mean [1] or rolling mean) is a calculation to analyze data points by creating a series of averages of different selections of the full data set.

  6. Geometric mean - Wikipedia

    en.wikipedia.org/wiki/Geometric_mean

    The geometric mean is more appropriate than the arithmetic mean for describing proportional growth, both exponential growth (constant proportional growth) and varying growth; in business the geometric mean of growth rates is known as the compound annual growth rate (CAGR). The geometric mean of growth over periods yields the equivalent constant ...

  7. Algorithms for calculating variance - Wikipedia

    en.wikipedia.org/wiki/Algorithms_for_calculating...

    Algorithms for calculating variance play a major role in computational statistics.A key difficulty in the design of good algorithms for this problem is that formulas for the variance may involve sums of squares, which can lead to numerical instability as well as to arithmetic overflow when dealing with large values.

  8. Sample mean and covariance - Wikipedia

    en.wikipedia.org/wiki/Sample_mean_and_covariance

    The arithmetic mean of a population, or population mean, is often denoted μ. [2] The sample mean ¯ (the arithmetic mean of a sample of values drawn from the population) makes a good estimator of the population mean, as its expected value is equal to the population mean (that is, it is an unbiased estimator).

  9. Point estimation - Wikipedia

    en.wikipedia.org/wiki/Point_estimation

    This method is based on law of large numbers, which uses all the known facts about a population and apply those facts to a sample of the population by deriving equations that relate the population moments to the unknown parameters. We can then solve with the sample mean of the population moments. [10]