Qualitative property in the context of "Continuum (measurement)"

Data (/ˈdeɪtə/ DAY-tə, US also /ˈdætə/ DAT-ə) are a collection of discrete or continuous values that convey information, describing the quantity, quality, fact, statistics, other basic units of meaning, or simply sequences of symbols that may be further interpreted formally. A datum is an individual value in a collection of data. Data are usually organized into structures such as tables that provide additional context and meaning, and may themselves be used as data in larger structures. Data may be used as variables in a computational process. Data may represent abstract ideas or concrete measurements.Data are commonly used in scientific research, economics, and virtually every other form of human organizational activity. Examples of data sets include price indices (such as the consumer price index), unemployment rates, literacy rates, and census data. In this context, data represent the raw facts and figures from which useful information can be extracted.

Data are collected using techniques such as measurement, observation, query, or analysis, and are typically represented as numbers or characters that may be further processed. Field data are data that are collected in an uncontrolled, in-situ environment. Experimental data are data that are generated in the course of a controlled scientific experiment. Data are analyzed using techniques such as calculation, reasoning, discussion, presentation, visualization, or other forms of post-analysis. Prior to analysis, raw data (or unprocessed data) is typically cleaned: Outliers are removed, and obvious instrument or data entry errors are corrected.

A physical property is any property of a physical system that is measurable. The changes in the physical properties of a system can be used to describe its changes between momentary states. A quantifiable physical property is called physical quantity. Measurable physical quantities are often referred to as observables. Some physical properties are qualitative, such as shininess, brittleness, etc.; some general qualitative properties admit more specific related quantitative properties, such as in opacity, hardness, ductility, viscosity, etc.

Physical properties are often characterized as intensive and extensive properties. An intensive property does not depend on the size or extent of the system, nor on the amount of matter in the object, while an extensive property shows an additive relationship. These classifications are in general only valid in cases when smaller subdivisions of the sample do not interact in some physical or chemical process when combined.

Continuum (pl.: continua or continuums) theories or models explain variation as involving gradual quantitative transitions without abrupt changes or discontinuities. In contrast, categorical theories or models explain variation using qualitatively different states.

In medicine, describing a disease as acute denotes that it is of recent onset; it occasionally denotes a short duration. The quantification of how much time constitutes "short" and "recent" varies by disease and by context, but the core denotation of "acute" is always qualitatively in contrast with "chronic", which denotes long-lasting disease (for example, in acute leukaemia and chronic leukaemia).

In the context of the mass noun "acute disease", it refers to the acute phase (that is, a short course) of any disease entity. For example, in an article on ulcerative enteritis in poultry, the author says, "in acute disease there may be increased mortality without any obvious signs", referring to the acute form or phase of ulcerative enteritis.

In statistics, a categorical variable (also called qualitative variable) is a variable that can take on one of a limited, and usually fixed, number of possible values, assigning each individual or other unit of observation to a particular group or nominal category on the basis of some qualitative property. In computer science and some branches of mathematics, categorical variables are referred to as enumerations or enumerated types. Commonly (though not in this article), each of the possible values of a categorical variable is referred to as a level. The probability distribution associated with a random categorical variable is called a categorical distribution.

Categorical data is the statistical data type consisting of categorical variables or of data that has been converted into that form, for example as grouped data. More specifically, categorical data may derive from observations made of qualitative data that are summarised as counts or cross tabulations, or from observations of quantitative data grouped within given intervals. Often, purely categorical data are summarised in the form of a contingency table. However, particularly when considering data analysis, it is common to use the term "categorical data" to apply to data sets that, while containing some categorical variables, may also contain non-categorical variables. Ordinal variables have a meaningful ordering, while nominal variables have no meaningful ordering.

The Mohs scale (/moʊz/ MOHZ) of mineral hardness is a qualitative ordinal scale, from 1 to 10, characterizing scratch resistance of minerals through the ability of harder material to scratch softer material.

The scale was introduced in 1812 by the German geologist and mineralogist Friedrich Mohs, in his book Versuch einer Elementar-Methode zur naturhistorischen Bestimmung und Erkennung der Fossilien (transl. Attempt at an elementary method for the natural-historical determination and recognition of fossils); it is one of several definitions of hardness in materials science, some of which are more quantitative.

A nominal category (also nominal variable or nominal group) is a collection of objects or ideas grouped according to a particular qualitative property. Nominal categories do not have a natural order, which means that statistical analyses of these variables will always produce the same results, regardless of the order in which the data is presented.

A variable used to associate each data point in a set of observations, or in a particular instance, to a certain qualitative category is a categorical variable. Categorical variables have two types of scales, ordinal and nominal. The first type of categorical scale is dependent on natural ordering, levels that are defined by a sense of quality. Variables with this ordering convention are known as ordinal variables. In comparison, variables with unordered scales are nominal variables.