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  • Visualizations with TIBCO Statistica®

    Statistica has a wide selection of scientific and technical charts featuring built-in analytic options. There are 2- and 3-dimensional graphical displays, ternary graphs, special 4-dimensional graphs, multidimensional graphs, categorized multigraphs, matrices of graphs, icons, tessellations, spectral 2- and 3-dimensional graphs, compound graphs, and other specialized procedures.

    Table of Contents


    If interactive dashboards are needed, then explore Visualizations and Dashboards with TIBCO Spotfire®

    General Graphs

    2D Graphs

    • 2D Box Plots
    • 2D Custom Function Plots
    • 2D Histograms
    • 2D Line Plots
    • 2D Range Plots
    • 2D Scatterplots
    • Bag Plots
    • Bar Plots
    • Means w/Error Plots
    • Missing/Range Data Plots
    • Normal Probability Plots
    • Parallel Coordinate Plots
    • Pie Charts
    • Probability-Probability Plots
    • Quantile-Quantile Plots
    • Scatter Icon Plots
    • Scatter Image Plots
    • Scatterplots w/Box Plots
    • Scatterplots w/Histograms
    • Sequential/Stacked Plots
    • Variability Plots

    3D Sequential Graphs

    • Bivariate Histograms
    • Box Plots
    • Range Plots
    • Raw Data Plots

    3D XYZ Graphs

    • Categorized Ternary Plots
    • Categorized XYZ Plots
    • Contour Plots
    • Custom Function Plots
    • Scatter Image Plots
    • Scatterplots
    • Surface Plots
    • Ternary Plots
    • Wafer Plots

    Read more about Statistica's general graphs

    Graphs Integrated with Statistical Procedures

    Each analytic module has visualizations that are common for the specific analytics.

    For example:

    • descriptive statistics have histograms to review the data's distribution
    • nonparametric ordinal description statistics have options for box & whisker plots
    • The generalized linear model (GLM) module can create a normal probability plot for the raw residuals
    • case-effect (Ishikawa) diagram available in the Process Analysis module
    • quality control has graph types; individuals, X-bar, EWMA, MA, CuSum. Variance visualization types are moving range, R, S. 



    Configuration Options

    Graphs can be configured prior to creation or after the creation of the graph. 

    Multigraphics management capabilities are available (i.e. compound graphs).

    Graphs can be saved as ActiveX objects in Microsoft Word. The object contains not only all configuration features and also the data needed to continue editing  (fitting, smoothing, etc.). Other save options are jpg, bmp, png, wmf, emf, pdf, gif, tiff, svg. A collection of graphs can be published in Microsoft PowerPoint; one graph per slide. 

    Graphs can be treated as a starting point for configuration. Graph configurations can be saved and reused. 

    Different fit types, scaling, statistics, case weights, reference lines, and titles can be included or excluded on the graph.

    The Selection conditions (Sel Cond) functionality marks specific rows as included or excluded for the graph without changing the data. Example of a selection condition:

     Variable-Named-Advert = "PEPSI" or Variable-Named-Test = 1

    The type of graph can be changed after it is created. For example, a scatterplot can be changed to a bubble plot. A histogram can be changed to a Pareto chart. 

    Cartesian and Polar coordination systems are supported. 



    Fitting Arbitrary Functions

    Additional facilities to fit user-defined functions of practically unlimited complexity to the data are described in the section on Nonlinear Estimation. The function minimization can be performed using a selection of powerful fitting algorithms, including Levenberg-Marquardt, quasi-Newton, Simplex, Hooke-Jeeves pattern moves, and the Rosenbrock pattern search method of rotating coordinates), and according to the default or user-defined loss functions.

    Fitting, Smoothing, Overlaying

    A selection of general-purpose smoothing and function fitting methods are available at any point of your analysis as part of the general graphics options. They include distribution fitting options; Beta, Exponential, Extreme Value, Gamma, Laplace, Lognormal, Lowess, Normal, Poisson, Rayleigh, and Weibull. Standard fitting and smoothing procedures are also available; linear, exponential, logarithmic, spline, normal, polynomial (of user-selectable order), bicubic spline, distance-weighted least squares smoothing, negative exponentially-weighted smoothing, ternary linear and quadratic, ternary cubic and special cubic.

    User-defined 2- and 3-dimensional functions (as well as sets of parametric curves e.g., to draw a circle or an ellipse) can also be plotted and overlaid on the graphs. The functions can reference a wide variety of distributions including Beta, Binomial, Cauchy, Chi-square, Exponential, Extreme Value, F, Gamma, Geometric, Laplace, Logistic, Lognormal, Normal, Pareto, Poisson, Rectangular, Rayleigh, Student's t, and Weibull, as well as their integrals and inverses.

    Interactive Scaling

    You can directly interact with the scaling on the graph by hovering the mouse pointer about the axis labels toward the end of the axis and pulling left or right to change the scaling. Interactive scaling is a powerful graphical exploratory technique that enables you to reveal hidden trends by stretching or compressing the desired parts of the display. Similarly, Interactive panning allows you to pan to the right or left by hovering the mouse pointer above the axis labels toward the center of the axis.


    Refresh Data

    All graphs created from the spreadsheet data can automatically maintain their links to the data. The data can be added to a Statistica Workbook and the graphs, by default, will be added to the same Workbook. The Workbook can then be shared. If the spreadsheet is linked to an outside data source, graphs can be set to update automatically whenever the spreadsheet links are updated (e.g., from a remote data warehouse or set different databases). Automating and updating a graph can be especially useful for quality control visualizations. 


    A large number of options enable the user to control every aspect of the scale. For example, support is provided for multiple (parallel) scales. Scales can feature multiple scale breaks that can be used to "compress" specified segments of the display. Scale values can be placed at arbitrary locations, and their format can be controlled using a selection of options.

    Moreover, facilities are provided to automate tedious aspects of scale definitions; for example, date scale values can be created automatically. Statistica can be instructed to display only every n'th scale value, and specific aspects of the scale definitions can be automatically transferred to the opposite scale or to all scales.


    Point markers on plots can be made transparent with an interactive transparency control with on-screen sliders. Transparency control is a powerful graphical exploratory technique that enables you to reveal trends hidden in the dense concentrations of data points (especially scatterplots and scatterplot matrices generated from extremely large data sets).


    The goal is to achieve the optimal density level to uncover patterns obscured by a large number of random points (white noise) that create the "ink blot" effect.

    Additionally, making plot areas transparent allows portions of the plot to overlap while still being visible.


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