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  2. Data set - Wikipedia

    en.wikipedia.org/wiki/Data_set

    The data set lists values for each of the variables, such as for example height and weight of an object, for each member of the data set. Data sets can also consist of a collection of documents or files. [2] In the open data discipline, data set is the unit to measure the information released in a public open data repository. The European data ...

  3. Winsorizing - Wikipedia

    en.wikipedia.org/wiki/Winsorizing

    For example, a 90% winsorization would see all data below the 5th percentile set to the 5th percentile, and all data above the 95th percentile set to the 95th percentile. Winsorized estimators are usually more robust to outliers than their more standard forms, although there are alternatives, such as trimming (see below), that will achieve a ...

  4. Logistic regression - Wikipedia

    en.wikipedia.org/wiki/Logistic_regression

    Logistic regression is used in various fields, including machine learning, most medical fields, and social sciences. For example, the Trauma and Injury Severity Score (), which is widely used to predict mortality in injured patients, was originally developed by Boyd et al. using logistic regression. [6]

  5. Bootstrapping (statistics) - Wikipedia

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

    This works by partitioning the data set into equal-sized buckets and aggregating the data within each bucket. This pre-aggregated data set becomes the new sample data over which to draw samples with replacement. This method is similar to the Block Bootstrap, but the motivations and definitions of the blocks are very different.

  6. SPSS - Wikipedia

    en.wikipedia.org/wiki/SPSS

    Early versions of SPSS Statistics were written in Fortran and designed for batch processing on mainframes, including for example IBM and ICL versions, originally using punched cards for data and program input. A processing run read a command file of SPSS commands and either a raw input file of fixed-format data with a single record type, or a ...

  7. Oversampling and undersampling in data analysis - Wikipedia

    en.wikipedia.org/wiki/Oversampling_and_under...

    Overabundance of already collected data became an issue only in the "Big Data" era, and the reasons to use undersampling are mainly practical and related to resource costs. Specifically, while one needs a suitably large sample size to draw valid statistical conclusions, the data must be cleaned before it can be used. Cleansing typically ...

  8. Compositional data - Wikipedia

    en.wikipedia.org/wiki/Compositional_data

    In geology, a rock composed of different minerals may be a compositional data point in a sample of rocks; a rock of which 10% is the first mineral, 30% is the second, and the remaining 60% is the third would correspond to the triple [0.1, 0.3, 0.6]. A data set would contain one such triple for each rock in a sample of rocks.

  9. Generalized additive model for location, scale and shape

    en.wikipedia.org/wiki/Generalized_additive_model...

    The generalized additive model for location, scale and shape (GAMLSS) is a semiparametric regression model in which a parametric statistical distribution is assumed for the response (target) variable but the parameters of this distribution can vary according to explanatory variables.