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Small multiple map series showing the trends in partisan voting margins in Utah, 1900–2012. Small multiples are a popular technique in cartographic design for multivariate mapping. As with the small multiple chart, each panel uses the same underlying two-dimensional space, but in this case that is a geographic space.
Manasseh Wepundi noted the difference between "the unit of analysis, that is the phenomenon about which generalizations are to be made, that which each 'case' in the data file represents and the level of analysis, that is, the manner in which the units of analysis can be arrayed on a continuum from the very small (micro) to very large (macro ...
Data points may be labelled if there are few of them. Dot plots are one of the simplest plots available, and are suitable for small to moderate sized data sets. They are useful for highlighting clusters and gaps, as well as outliers. Forest plot : is a graphical display that shows the strength of the evidence in quantitative scientific studies.
A popular large-scale text corpus. None Text NLP 2015 [105] Zhu, Yukun, et al. Stanford Natural Language Inference (SNLI) Corpus Image captions matched with newly constructed sentences to form entailment, contradiction, or neutral pairs. Entailment class labels, syntactic parsing by the Stanford PCFG parser 570,000 Text
Multidimensional scaling (MDS) is a means of visualizing the level of similarity of individual cases of a data set. MDS is used to translate distances between each pair of n {\textstyle n} objects in a set into a configuration of n {\textstyle n} points mapped into an abstract Cartesian space .
Information visualization, on the other hand, deals with multiple, large-scale and complicated datasets which contain quantitative (numerical) data as well as qualitative (non-numerical, i.e. verbal or graphical) and primarily abstract information and its goal is to add value to raw data, improve the viewers' comprehension, reinforce their ...
By counting the number of observations that exist for each of the 60 unique combination of variables, QCA can determine which descriptive inferences or implications are empirically supported by a data set. Thus, the input to QCA is a data set of any size, from small-N to large-N, and the output of QCA is a set of descriptive inferences or ...
Graphical statistical methods have four objectives: [2] The exploration of the content of a data set; The use to find structure in data; Checking assumptions in statistical models; Communicate the results of an analysis. If one is not using statistical graphics, then one is forfeiting insight into one or more aspects of the underlying structure ...