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  2. Coefficient of determination - Wikipedia

    en.wikipedia.org/wiki/Coefficient_of_determination

    Ordinary least squares regression of Okun's law.Since the regression line does not miss any of the points by very much, the R 2 of the regression is relatively high.. In statistics, the coefficient of determination, denoted R 2 or r 2 and pronounced "R squared", is the proportion of the variation in the dependent variable that is predictable from the independent variable(s).

  3. Symmetric mean absolute percentage error - Wikipedia

    en.wikipedia.org/wiki/Symmetric_mean_absolute...

    The earliest reference to a similar formula appears to be Armstrong (1985, p. 348), where it is called "adjusted MAPE" and is defined without the absolute values in the denominator. It was later discussed, modified, and re-proposed by Flores (1986).

  4. Goodness of fit - Wikipedia

    en.wikipedia.org/wiki/Goodness_of_fit

    where and are the same as for the chi-square test, denotes the natural logarithm, and the sum is taken over all non-empty bins. Furthermore, the total observed count should be equal to the total expected count: ∑ i O i = ∑ i E i = N {\displaystyle \sum _{i}O_{i}=\sum _{i}E_{i}=N} where N {\textstyle N} is the total number of observations.

  5. Regression analysis - Wikipedia

    en.wikipedia.org/wiki/Regression_analysis

    For example, least squares (including its most common variant, ordinary least squares) finds the value of that minimizes the sum of squared errors ((,)). A given regression method will ultimately provide an estimate of β {\displaystyle \beta } , usually denoted β ^ {\displaystyle {\hat {\beta }}} to distinguish the estimate from the true ...

  6. Iteratively reweighted least squares - Wikipedia

    en.wikipedia.org/wiki/Iteratively_reweighted...

    IRLS is used to find the maximum likelihood estimates of a generalized linear model, and in robust regression to find an M-estimator, as a way of mitigating the influence of outliers in an otherwise normally-distributed data set, for example, by minimizing the least absolute errors rather than the least square errors.

  7. Non-linear least squares - Wikipedia

    en.wikipedia.org/wiki/Non-linear_least_squares

    Consider a set of data points, (,), (,), …, (,), and a curve (model function) ^ = (,), that in addition to the variable also depends on parameters, = (,, …,), with . It is desired to find the vector of parameters such that the curve fits best the given data in the least squares sense, that is, the sum of squares = = is minimized, where the residuals (in-sample prediction errors) r i are ...

  8. Effect size - Wikipedia

    en.wikipedia.org/wiki/Effect_size

    In statistics, an effect size is a value measuring the strength of the relationship between two variables in a population, or a sample-based estimate of that quantity. It can refer to the value of a statistic calculated from a sample of data, the value of one parameter for a hypothetical population, or to the equation that operationalizes how statistics or parameters lead to the effect size ...

  9. Root mean square deviation - Wikipedia

    en.wikipedia.org/wiki/Root_mean_square_deviation

    Download QR code; Print/export Download as PDF; Printable version; ... is computed for T different predictions as the square root of the mean of the squares of the ...