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The term big data has been in use since the 1990s, with some giving credit to John Mashey for popularizing the term. [22] [23] Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process data within a tolerable elapsed time.
While, strictly speaking, two- and higher-dimensional data sets are "multi-dimensional", the term "multidimensional" tends to be applied only to data sets with three or more dimensions. [2] For example, some forecast data sets provide forecasts for multiple target periods, conducted by multiple forecasters, and made at multiple horizons.
The sample size is an important feature of any empirical study in which the goal is to make inferences about a population from a sample. In practice, the sample size used in a study is usually determined based on the cost, time, or convenience of collecting the data, and the need for it to offer sufficient statistical power. In complex studies ...
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 ...
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
Vicon Physical Action Data Set Dataset 10 normal and 10 aggressive physical actions that measure the human activity tracked by a 3D tracker. Many parameters recorded by 3D tracker.
Attributes are closely related to variables. A variable is a logical set of attributes. [1] Variables can "vary" – for example, be high or low. [1] How high, or how low, is determined by the value of the attribute (and in fact, an attribute could be just the word "low" or "high"). [1] (For example see: Binary option)
The four datasets composing Anscombe's quartet. All four sets have identical statistical parameters, but the graphs show them to be considerably different. Anscombe's quartet comprises four datasets that have nearly identical simple descriptive statistics, yet have very different distributions and appear very different when graphed.