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  2. Weighted arithmetic mean - Wikipedia

    en.wikipedia.org/wiki/Weighted_arithmetic_mean

    The degrees of freedom of the weighted, unbiased sample variance vary accordingly from N − 1 down to 0. The standard deviation is simply the square root of the variance above. As a side note, other approaches have been described to compute the weighted sample variance. [7]

  3. Weight function - Wikipedia

    en.wikipedia.org/wiki/Weight_function

    A weight function is a mathematical device used when performing a sum, integral, or average to give some elements more "weight" or influence on the result than other elements in the same set. The result of this application of a weight function is a weighted sum or weighted average.

  4. Arithmetic mean - Wikipedia

    en.wikipedia.org/wiki/Arithmetic_mean

    In mathematics and statistics, the arithmetic mean (/ ˌ æ r ɪ θ ˈ m ɛ t ɪ k / arr-ith-MET-ik), arithmetic average, or just the mean or average (when the context is clear) is the sum of a collection of numbers divided by the count of numbers in the collection. [1] The collection is often a set of results from an experiment, an ...

  5. Mean absolute percentage error - Wikipedia

    en.wikipedia.org/wiki/Mean_absolute_percentage_error

    It is a measure used to evaluate the performance of regression or forecasting models. It is a variant of MAPE in which the mean absolute percent errors is treated as a weighted arithmetic mean. Most commonly the absolute percent errors are weighted by the actuals (e.g. in case of sales forecasting, errors are weighted by sales volume). [3]

  6. Kernel regression - Wikipedia

    en.wikipedia.org/wiki/Kernel_regression

    In statistics, kernel regression is a non-parametric technique to estimate the conditional expectation of a random variable.The objective is to find a non-linear relation between a pair of random variables X and Y.

  7. Kernel smoother - Wikipedia

    en.wikipedia.org/wiki/Kernel_smoother

    Example: The resulting function is smooth, and the problem with the biased boundary points is reduced. Local linear regression can be applied to any-dimensional space, though the question of what is a local neighborhood becomes more complicated. It is common to use k nearest training points to a test point to fit the local linear regression.

  8. Inverse-variance weighting - Wikipedia

    en.wikipedia.org/wiki/Inverse-variance_weighting

    In statistics, inverse-variance weighting is a method of aggregating two or more random variables to minimize the variance of the weighted average. Each random variable is weighted in inverse proportion to its variance (i.e., proportional to its precision). Given a sequence of independent observations y i with variances σ i 2, the inverse ...

  9. Moving average - Wikipedia

    en.wikipedia.org/wiki/Moving_average

    Mathematically, the weighted moving average is the convolution of the data with a fixed weighting function. One application is removing pixelization from a digital graphical image. [citation needed] In the financial field, and more specifically in the analyses of financial data, a weighted moving average (WMA) has the specific meaning of ...