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  2. Ordinal data - Wikipedia

    en.wikipedia.org/wiki/Ordinal_data

    Ordinal data is a categorical, statistical data type where the variables have natural, ordered categories and the distances between the categories are not known. [1]: 2 These data exist on an ordinal scale, one of four levels of measurement described by S. S. Stevens in 1946.

  3. List of statistical tests - Wikipedia

    en.wikipedia.org/wiki/List_of_statistical_tests

    Scaling of data: One of the properties of the tests is the scale of the data, which can be interval-based, ordinal or nominal. [3] Nominal scale is also known as categorical. [6] Interval scale is also known as numerical. [6] When categorical data has only two possibilities, it is called binary or dichotomous. [1]

  4. Level of measurement - Wikipedia

    en.wikipedia.org/wiki/Level_of_measurement

    Level of measurement or scale of measure is a classification that describes the nature of information within the values assigned to variables. [1] Psychologist Stanley Smith Stevens developed the best-known classification with four levels, or scales, of measurement: nominal, ordinal, interval, and ratio.

  5. Composite measure - Wikipedia

    en.wikipedia.org/wiki/Composite_measure

    An example of a composite measure is an IQ test, which gives a single score based on a series of responses to various questions. Three common composite measures include: indexes - measures that summarize and rank specific observations, usually on the ordinal scale; [1]

  6. Scale (social sciences) - Wikipedia

    en.wikipedia.org/wiki/Scale_(social_sciences)

    Examples are attitude scales and opinion scales. Some data are measured at the ratio level. Numbers indicate magnitude of difference and there is a fixed zero point. Ratios can be calculated. Examples include: age, income, price, costs, sales revenue, sales volume, and market share.

  7. Statistical classification - Wikipedia

    en.wikipedia.org/wiki/Statistical_classification

    In statistics, where classification is often done with logistic regression or a similar procedure, the properties of observations are termed explanatory variables (or independent variables, regressors, etc.), and the categories to be predicted are known as outcomes, which are considered to be possible values of the dependent variable.

  8. Ranking (statistics) - Wikipedia

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

    In statistics, ranking is the data transformation in which numerical or ordinal values are replaced by their rank when the data are sorted.. For example, if the numerical data 3.4, 5.1, 2.6, 7.3 are observed, the ranks of these data items would be 2, 3, 1 and 4 respectively.

  9. Ordinal regression - Wikipedia

    en.wikipedia.org/wiki/Ordinal_regression

    Ordinal regression turns up often in the social sciences, for example in the modeling of human levels of preference (on a scale from, say, 1–5 for "very poor" through "excellent"), as well as in information retrieval. In machine learning, ordinal regression may also be called ranking learning. [3] [a]