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Observations and Measurements (O&M) is an international standard [1] which defines a conceptual schema encoding for observations, and for features involved in sampling when making observations. While the O&M standard was developed in the context of geographic information systems , the model is derived from generic patterns proposed by Fowler ...
In statistics and econometrics, cross-sectional data is a type of data collected by observing many subjects (such as individuals, firms, countries, or regions) at a single point or period of time. Analysis of cross-sectional data usually consists of comparing the differences among selected subjects, typically with no regard to differences in time.
In descriptive statistics, summary statistics are used to summarize a set of observations, in order to communicate the largest amount of information as simply as possible. Statisticians commonly try to describe the observations in
A data point or observation is a set of one or more measurements on a single member of the unit of observation. For example, in a study of the determinants of money demand with the unit of observation being the individual, a data point might be the values of income, wealth, age of individual, and number of dependents. Statistical inference ...
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]
Panel data is the general class, a multidimensional data set, whereas a time series data set is a one-dimensional panel (as is a cross-sectional dataset). A data set may exhibit characteristics of both panel data and time series data. One way to tell is to ask what makes one data record unique from the other records.
This act of summarizing several natural data patterns with simple rules is a defining characteristic of these "empirical statistical laws". Examples of empirically inspired statistical laws that have a firm theoretical basis include: Statistical regularity; Law of large numbers; Law of truly large numbers; Central limit theorem; Regression ...
Aggregate data are applied in statistics, data warehouses, and in economics. There is a distinction between aggregate data and individual data. Aggregate data refers to individual data that are averaged by geographic area, by year, by service agency, or by other means. [ 2 ]