Notes and Technical Information

  • 2D Fit Lines
    You can fit an equation to the points in the 2D plots by selecting one of the predefined functions described below:
  • 3D Graph Surfaces
    You can fit one of several equations to the data or use one of the data smoothing procedures in a 3D surface plot by selecting one of the predefined functions described below:
  • Assessing the Fit of a Distribution via Quantile-Quantile Plots
    A typical use for quantile-quantile plots is to determine whether a given distribution provides an adequate fit to a set of data. In order to assess the fit of the theoretical distribution to the observed data, the non-missing observed values of the variable are ordered (x1 < ... < xn) and then these values (xi) are plotted against the inverse probability distribution function denoted as F-1 [specifically, F-1(i - rankadj/n + nadj), where F-1 depends on the distribution, and rankadj and nadj are user-defined adjustments]. A regression line is then fitted to the data in the resulting scatterplot. If the observed values fall on the regression line (fitting line), then it can be concluded that the observed values follow the specified distribution. The equation of the fitting line (Y=a + bx, given in the third title of the resulting Q-Q plot) provides parameter estimates (a and b, where a is the Threshold parameter and b is the Scale parameter) for the best fitting distribution (see the respective distribution for more information on these parameters).
  • Axis Overview
    The axes form an integral part of all 2D and 3D graphs; each point on the graph is identified by a pair (in 2D graph) of values on the x- and y-axis, or by a triplet (in 3D graph) of values on the x-, y-, and z-axes. Statistica uses the general term Axis for the x-, y-, or z-axes and provides several options (before and after creating the graph) to control their characteristics and properties. Not only can you choose its title; scaling; major, minor, and custom units; and scale values, but you also can customize the appearance of axes by changing thickness, color, and patterns.
  • Calculating Contour Levels and Distance
  • Categorization Methods
    There are five general methods of categorization of values and they will be reviewed briefly in this section: Integer mode, Categories, Boundaries, Codes, and Multiple subsets. Note that the same methods of categorization can be used to categorize cases into component graphs and to categorize cases within component graphs (e.g., in histograms or box plots).
  • Categorized Scatterplots - Overview
  • Distance-Weighted Least Squares Fitting
    Unlike some other fitting procedures, the distance-weighted least squares procedure does not fit to the data one function that can be easily described by a single formula and plotted independently from the data.
  • Distributions
  • Fitting Options for Ternary Plots
    The following four regression functions can be fit to the data in a Graphs menu graph or Categorized ternary plot. Note that these functions are derived from the equations for the respective standard polynomial functions, using the restriction that the sum of the values of the component (X, Y, Z) variables for each case is equal to a constant (e.g., 1.0). For example, the simple first-degree model:
  • Fitted Functions for Histograms
  • Mark Selected Subsets
  • Maximum Likelihood Method for Q-Q and P-P Plots
  • Method of Categorization
  • Mini-Formatting Toolbar
    An important aspect of graph customization is the appearance of the letters and numbers on the graph so that they match well with rest of the text in your report or presentation.
  • Negative Exponentially-Weighted Fitting
  • Outliers and Extremes
  • Parametric Curve
    Parametric equations can be used to represent curves whose graphs are not simple functions of the type y=f(x), where y and x are represented along the vertical and horizontal axes, respectively. Instead, the curves in the x-y plane are defined parametrically as two simultaneous functions of a parameter t that ranges over some interval (minimum, maximum). You can specify an equation y=f(t) for the y-component of the curve, and an equation x=g(t) for the x-component of the curve, for a specified range of parameter t.  
  • Spline Fitting
    It can be demonstrated that curves of any complexity can be described by a sequence of segments defined as polynomials. In practice, most real-life curves can be reliably approximated by a sequence of third-order (cubic) polynomials.
  • Suppressing Scale Values Near Scale Breaks
    In order to suppress displaying a scale value on the left side (or below for vertical axes) of the break, (after you have made the scale break) you can: