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  2. Biplot - Wikipedia

    en.wikipedia.org/wiki/Biplot

    A biplot overlays a score plot with a loading plot. 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.

  3. Plot (graphics) - Wikipedia

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

    A generalised biplot displays information on both continuous and categorical variables. Bland–Altman plot : In analytical chemistry and biostatistics this plot is a method of data plotting used in analysing the agreement between two different assays .

  4. File:Biplot of Anderson's Iris data set.svg - Wikipedia

    en.wikipedia.org/wiki/File:Biplot_of_Anderson's...

    Biplot of the Principal components analysis of Anderson's Iris data set. The SVG was created with R's biplot function using the CairoSVG device of the Cairo R package: Date: 24 September 2008: Source: I created this work entirely by myself. Author: Calimo: SVG development

  5. Correspondence analysis - Wikipedia

    en.wikipedia.org/wiki/Correspondence_analysis

    Correspondence analysis (CA) is a multivariate statistical technique proposed [1] by Herman Otto Hartley (Hirschfeld) [2] and later developed by Jean-Paul Benzécri. [3] It is conceptually similar to principal component analysis, but applies to categorical rather than continuous data.

  6. File:IrisDAbiplot.jpg - Wikipedia

    en.wikipedia.org/wiki/File:IrisDAbiplot.jpg

    Main page; Contents; Current events; Random article; About Wikipedia; Contact us; Donate

  7. Bi-plot - Wikipedia

    en.wikipedia.org/?title=Bi-plot&redirect=no

    Pages for logged out editors learn more. Contributions; Talk; Bi-plot

  8. File:Spectramap Biplot Iris Flower Data Set FULL.jpg

    en.wikipedia.org/wiki/File:Spectramap_Biplot...

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  9. Principal component analysis - Wikipedia

    en.wikipedia.org/wiki/Principal_component_analysis

    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.