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  2. Normalization (statistics) - Wikipedia

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

    Normalizing residuals when parameters are estimated, particularly across different data points in regression analysis. Standardized moment: Normalizing moments, using the standard deviation as a measure of scale. Coefficient of variation

  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. Database normalization - Wikipedia

    en.wikipedia.org/wiki/Database_normalization

    Database normalization is the process of structuring a relational database in accordance with a series of so-called normal forms in order to reduce data redundancy and improve data integrity. It was first proposed by British computer scientist Edgar F. Codd as part of his relational model .

  5. 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 ...

  6. Standardization - Wikipedia

    en.wikipedia.org/wiki/Standardization

    Standardization (American English) or standardisation (British English) is the process of implementing and developing technical standards based on the consensus of different parties that include firms, users, interest groups, standards organizations and governments. [1]

  7. Data quality - Wikipedia

    en.wikipedia.org/wiki/Data_quality

    These programs, and program auditors, increasingly seek tools to standardize and streamline the process of determining the quality of data, [26] verify the quality of reported data, and assess the underlying data management and reporting systems for indicators. [27]

  8. Age adjustment - Wikipedia

    en.wikipedia.org/wiki/Age_adjustment

    When comparing data from a specific country or region, using a standard population from that country or region means that the age-adjusted rates are similar to the true population rates. [6] On the other hand, standardizing data using a widely used standard such as the WHO standard population allows for easier comparison with published statistics.

  9. Industry standard data model - Wikipedia

    en.wikipedia.org/wiki/Industry_standard_data_model

    An industry standard data model, or simply standard data model, is a data model that is widely used in a particular industry. The use of standard data models makes the exchange of information easier and faster because it allows heterogeneous organizations to share an agreed vocabulary, semantics, format, and quality standard for data.