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An OLAP cube is a multi-dimensional array of data. [1] Online analytical processing (OLAP) [2] is a computer-based technique of analyzing data to look for insights. The term cube here refers to a multi-dimensional dataset, which is also sometimes called a hypercube if the number of dimensions is greater than three.
Comparison of some 1- and 2-dimensional interpolations. Black and red / yellow / green / blue dots correspond to the interpolated point and neighbouring samples, respectively. Their heights above the ground correspond to their values.
In many disciplines, two-dimensional data sets are also called panel data. [1] 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 ...
State-transition tables are typically two-dimensional tables. There are two common ways for arranging them. In the first way, one of the dimensions indicates current states, while the other indicates inputs. The row/column intersections indicate next states and (optionally) outputs associated with the state transitions.
In mathematics, bilinear interpolation is a method for interpolating functions of two variables (e.g., x and y) using repeated linear interpolation. It is usually applied to functions sampled on a 2D rectilinear grid , though it can be generalized to functions defined on the vertices of (a mesh of) arbitrary convex quadrilaterals .
While the terms allude to the rows and columns of a two-dimensional array, i.e. a matrix, the orders can be generalized to arrays of any dimension by noting that the terms row-major and column-major are equivalent to lexicographic and colexicographic orders, respectively. It is also worth noting that matrices, being commonly represented as ...
In other words, the two variables are not independent. If there is no contingency, it is said that the two variables are independent. The example above is the simplest kind of contingency table, a table in which each variable has only two levels; this is called a 2 × 2 contingency table. In principle, any number of rows and columns may be used.
Group 2 is composed of two variables {C1, C2} identical to the same variable C uncorrelated with the first two. This example is not completely unrealistic. It is often necessary to simultaneously analyse multi-dimensional and (quite) one-dimensional groups. Each group having the same number of variables has the same total inertia.