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Chronological dating, or simply dating, is the process of attributing to an object or event a date in the past, allowing such object or event to be located in a previously established chronology. This usually requires what is commonly known as a "dating method".
In archaeology, absolute dating is usually based on the physical, chemical, and life properties of the materials of artifacts, buildings, or other items that have been modified by humans and by historical associations with materials with known dates (such as coins and historical records). For example, coins found in excavations may have their ...
Categorical: Represent groups of objects with a particular characteristic. Categorical variables can either be nominal or ordinal. Nominal variables for example gender have no order between them and are thus nominal. Ordinal variables are categories with an order, for sample recording the age group someone falls into. [50]
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. [1] The first type of categorical scale is dependent on natural ordering, levels that are defined by a sense of quality.
In information science and ontology, a classification scheme is an arrangement of classes or groups of classes. The activity of developing the schemes bears similarity to taxonomy, but with perhaps a more theoretical bent, as a single classification scheme can be applied over a wide semantic spectrum while taxonomies tend to be devoted to a single topic.
Craters are very useful in relative dating; as a general rule, the younger a planetary surface is, the fewer craters it has. If long-term cratering rates are known to enough precision, crude absolute dates can be applied based on craters alone; however, cratering rates outside the Earth-Moon system are poorly known. [8]
Early work on statistical classification was undertaken by Fisher, [1] [2] in the context of two-group problems, leading to Fisher's linear discriminant function as the rule for assigning a group to a new observation. [3] This early work assumed that data-values within each of the two groups had a multivariate normal distribution.
Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically, sometimes they are grouped together as categorical variables, whereas ratio and interval measurements are grouped together as quantitative variables, which can be either discrete or continuous, due to their numerical nature.