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There are three equivalent methods that can be used to determine the values of a point on the plot: Parallel line or grid method. The first method is to use a diagram grid consisting of lines parallel to the triangle edges. A parallel to a side of the triangle is the locus of points constant in the component situated in the vertex opposed to ...
A ridgeline plot (also known as a joyplot [1] [note 1]) is a series of line plots that are combined by vertical stacking to allow the easy visualization of changes through space or time. The plots are often overlapped slightly to allow the changes to be more clearly contrasted. [2] [3] [4] [5]
An alternative definition of the Minkowski difference is sometimes used for computing intersection of convex shapes. [3] This is not equivalent to the previous definition, and is not an inverse of the sum operation. Instead it replaces the vector addition of the Minkowski sum with a vector subtraction. If the two convex shapes intersect, the ...
t-distributed stochastic neighbor embedding (t-SNE) is a statistical method for visualizing high-dimensional data by giving each datapoint a location in a two or three-dimensional map. It is based on Stochastic Neighbor Embedding originally developed by Geoffrey Hinton and Sam Roweis, [ 1 ] where Laurens van der Maaten and Hinton proposed the t ...
Line plot survey is a systematic sampling technique used on land surfaces for laying out sample plots within a rectangular grid to conduct forest inventory or agricultural research. It is a specific type of systematic sampling , similar to other statistical sampling methods such as random sampling , but more straightforward to carry out in ...
This is an important technique for all types of time series analysis, especially for seasonal adjustment. [2] It seeks to construct, from an observed time series, a number of component series (that could be used to reconstruct the original by additions or multiplications) where each of these has a certain characteristic or type of behavior.
This line attempts to display the non-random component of the association between the variables in a 2D scatter plot. Smoothing attempts to separate the non-random behaviour in the data from the random fluctuations, removing or reducing these fluctuations, and allows prediction of the response based value of the explanatory variable .
Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data preprocessing.. The data is linearly transformed onto a new coordinate system such that the directions (principal components) capturing the largest variation in the data can be easily identified.