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  2. Normalization (statistics) - Wikipedia

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

    This is common on standardized tests. See also quantile normalization. Normalization by adding and/or multiplying by constants so values fall between 0 and 1. This is used for probability density functions, with applications in fields such as quantum mechanics in assigning probabilities to | ψ | 2.

  3. Feature scaling - Wikipedia

    en.wikipedia.org/wiki/Feature_scaling

    In machine learning, we can handle various types of data, e.g. audio signals and pixel values for image data, and this data can include multiple dimensions. Feature standardization makes the values of each feature in the data have zero-mean (when subtracting the mean in the numerator) and unit-variance.

  4. Standard score - Wikipedia

    en.wikipedia.org/wiki/Standard_score

    Comparison of the various grading methods in a normal distribution, including: standard deviations, cumulative percentages, percentile equivalents, z-scores, T-scores. In statistics, the standard score is the number of standard deviations by which the value of a raw score (i.e., an observed value or data point) is above or below the mean value of what is being observed or measured.

  5. Normal distribution - Wikipedia

    en.wikipedia.org/wiki/Normal_distribution

    Conversely, if ⁠ ⁠ is a normal deviate with parameters ⁠ ⁠ and , then this ⁠ ⁠ distribution can be re-scaled and shifted via the formula = / to convert it to the standard normal distribution.

  6. 68–95–99.7 rule - Wikipedia

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

    For an approximately normal data set, the values within one standard deviation of the mean account for about 68% of the set; while within two standard deviations account for about 95%; and within three standard deviations account for about 99.7%. Shown percentages are rounded theoretical probabilities intended only to approximate the empirical ...

  7. Unbiased estimation of standard deviation - Wikipedia

    en.wikipedia.org/wiki/Unbiased_estimation_of...

    Bias in standard deviation for autocorrelated data. The figure shows the ratio of the estimated standard deviation to its known value (which can be calculated analytically for this digital filter), for several settings of α as a function of sample size n. Changing α alters the variance reduction ratio of the filter, which is known to be

  8. Mean squared error - Wikipedia

    en.wikipedia.org/wiki/Mean_squared_error

    The MSE either assesses the quality of a predictor (i.e., a function mapping arbitrary inputs to a sample of values of some random variable), or of an estimator (i.e., a mathematical function mapping a sample of data to an estimate of a parameter of the population from which the data is sampled).

  9. Standard error - Wikipedia

    en.wikipedia.org/wiki/Standard_error

    The following expressions can be used to calculate the ... The standard deviation of the sample data is a description of the variation in measurements, while the ...