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2. List of analyses of categorical data - Wikipedia

   en.wikipedia.org/wiki/List_of_analyses_of...

   This is a list of statistical procedures which can be used for the analysis of categorical data, also known as data on the nominal scale and as categorical variables.

3. Mosaic plot - Wikipedia

   en.wikipedia.org/wiki/Mosaic_plot

   A mosaic plot, Marimekko chart, Mekko chart, or sometimes percent stacked bar plot, is a graphical visualization of data from two or more qualitative variables. [1] It is the multidimensional extension of spineplots, which graphically display the same information for only one variable. [ 2 ]

4. Bar chart - Wikipedia

   en.wikipedia.org/wiki/Bar_chart

   A bar chart or bar graph is a chart or graph that presents categorical data with rectangular bars with heights or lengths proportional to the values that they represent. The bars can be plotted vertically or horizontally. A vertical bar chart is sometimes called a column chart and has been identified as the prototype of charts. [1]

5. Statistical graphics - Wikipedia

   en.wikipedia.org/wiki/Statistical_graphics

   Whereas statistics and data analysis procedures generally yield their output in numeric or tabular form, graphical techniques allow such results to be displayed in some sort of pictorial form. They include plots such as scatter plots , histograms , probability plots , spaghetti plots , residual plots, box plots , block plots and biplots .

6. Plot (graphics) - Wikipedia

   en.wikipedia.org/wiki/Plot_(graphics)

   A biplot allows information on both samples and variables of a data matrix to be displayed graphically. Samples are displayed as points while variables are displayed either as vectors, linear axes or nonlinear trajectories. In the case of categorical variables, category level points may be used to represent the levels of a categorical variable.

7. Data analysis - Wikipedia

   en.wikipedia.org/wiki/Data_analysis

   Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]