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Data Analysis

The data analysis framework of Meta.Numerics is organized into classes that represent different kinds of data collections:
  • Sample: Each observation consists of a single number drawn form a population. For example: weight, income, or lifetime are measured for a study group.
    • MultivariateSample: Each observation consists of a set of numbers drawn from a population. For example: weight, income, and lifetime are measured for a study group.
  • DataSet: Each observation consists of a measured value and error bar associated with a single independent variable. For example: the solubility of a substance in water is measured as a function of temperature.
    • DataSet<T>: Each observation consists of a measured vallue and error bar associated with an arbitrary independent variable.
  • BinaryContingencyTable: Observations are classified by subjects that fell into one binary catetory compared to those that fell into another binary category. For example: a study group is classified into subjects that were treated and not treated, survived and not survived.
    • ContingencyTable: Obsercations are classified by subjects that fell into one category set compared to those that fell into another category set. For eample: a study group is classified by subject grade level and whether the subject passed or failed the grade.

Each kind of data collection allows for its own set of descriptive statistics, statisitcal tests, and model fitting procedures. They are described in detail below.

Last edited Apr 29, 2010 at 1:03 AM by ichbin, version 2

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