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  2. Accuracy and precision - Wikipedia

    en.wikipedia.org/wiki/Accuracy_and_precision

    Accuracy is also used as a statistical measure of how well a binary classification test correctly identifies or excludes a condition. That is, the accuracy is the proportion of correct predictions (both true positives and true negatives) among the total number of cases examined. [10] As such, it compares estimates of pre- and post-test probability.

  3. Uncertainty - Wikipedia

    en.wikipedia.org/wiki/Uncertainty

    In the last notation, parentheses are the concise notation for the ± notation. For example, applying 10 1 ⁄ 2 meters in a scientific or engineering application, it could be written 10.5 m or 10.50 m, by convention meaning accurate to within one tenth of a meter, or one hundredth. The precision is symmetric around the last digit.

  4. Reliability (statistics) - Wikipedia

    en.wikipedia.org/wiki/Reliability_(statistics)

    Reliability theory shows that the variance of obtained scores is simply the sum of the variance of true scores plus the variance of errors of measurement. [7] = + This equation suggests that test scores vary as the result of two factors: Variability in true scores

  5. Predictability - Wikipedia

    en.wikipedia.org/wiki/Predictability

    Examples of US macroeconomic series of interest include but are not limited to Consumption, Investment, Real GNP, and Capital Stock. Factors that are involved in the predictability of an economic system include the range of the forecast (is the forecast two years "out" or twenty) and the variability of estimates.

  6. Bias–variance tradeoff - Wikipedia

    en.wikipedia.org/wiki/Bias–variance_tradeoff

    In statistics and machine learning, the bias–variance tradeoff describes the relationship between a model's complexity, the accuracy of its predictions, and how well it can make predictions on previously unseen data that were not used to train the model. In general, as we increase the number of tunable parameters in a model, it becomes more ...

  7. Statistical dispersion - Wikipedia

    en.wikipedia.org/wiki/Statistical_dispersion

    In statistics, dispersion (also called variability, scatter, or spread) is the extent to which a distribution is stretched or squeezed. [1] Common examples of measures of statistical dispersion are the variance, standard deviation, and interquartile range. For instance, when the variance of data in a set is large, the data is widely scattered.

  8. 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).

  9. Coefficient of variation - Wikipedia

    en.wikipedia.org/wiki/Coefficient_of_variation

    This follows from the fact that the variance and mean are independent of the ordering of x. Scale invariance: c v (x) = c v (αx) where α is a real number. [22] Population independence – If {x,x} is the list x appended to itself, then c v ({x,x}) = c v (x). This follows from the fact that the variance and mean both obey this principle.