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  2. Normalization (statistics) - Wikipedia

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

    In another usage in statistics, normalization refers to the creation of shifted and scaled versions of statistics, where the intention is that these normalized values allow the comparison of corresponding normalized values for different datasets in a way that eliminates the effects of certain gross influences, as in an anomaly time series. Some ...

  3. Feature scaling - Wikipedia

    en.wikipedia.org/wiki/Feature_scaling

    Feature standardization makes the values of each feature in the data have zero-mean (when subtracting the mean in the numerator) and unit-variance. This method is widely used for normalization in many machine learning algorithms (e.g., support vector machines, logistic regression, and artificial neural networks).

  4. Normalizing constant - Wikipedia

    en.wikipedia.org/wiki/Normalizing_constant

    This is the probability mass function of the Poisson distribution with expected value λ. Note that if the probability density function is a function of various parameters, so too will be its normalizing constant. The parametrised normalizing constant for the Boltzmann distribution plays a central role in statistical mechanics.

  5. 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.

  6. Quantile normalization - Wikipedia

    en.wikipedia.org/wiki/Quantile_normalization

    To quantile normalize two or more distributions to each other, without a reference distribution, sort as before, then set to the average (usually, arithmetic mean) of the distributions. So the highest value in all cases becomes the mean of the highest values, the second highest value becomes the mean of the second highest values, and so on.

  7. Normal distribution - Wikipedia

    en.wikipedia.org/wiki/Normal_distribution

    The Lagrange constraints that () is properly normalized and has the specified mean and variance are satisfied if and only if , , and are chosen so that = (). The entropy of a normal distribution X ∼ N ( μ , σ 2 ) {\textstyle X\sim N(\mu ,\sigma ^{2})} is equal to H ( X ) = 1 2 ( 1 + ln ⁡ 2 σ 2 π ) , {\displaystyle H(X)={\tfrac {1}{2}}(1 ...

  8. Normality test - Wikipedia

    en.wikipedia.org/wiki/Normality_test

    Simple back-of-the-envelope test takes the sample maximum and minimum and computes their z-score, or more properly t-statistic (number of sample standard deviations that a sample is above or below the sample mean), and compares it to the 68–95–99.7 rule: if one has a 3σ event (properly, a 3s event) and substantially fewer than 300 samples, or a 4s event and substantially fewer than 15,000 ...

  9. Index of dispersion - Wikipedia

    en.wikipedia.org/wiki/Index_of_dispersion

    In probability theory and statistics, the index of dispersion, [1] dispersion index, coefficient of dispersion, relative variance, or variance-to-mean ratio (VMR), like the coefficient of variation, is a normalized measure of the dispersion of a probability distribution: it is a measure used to quantify whether a set of observed occurrences are clustered or dispersed compared to a standard ...