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Statit Custom QC Graphics

In addition to variable and attribute charts, Statit provides the following.

Frequency Charts

  • Pie Chart. Displays several values, each as a slice of a pie. Each slice may be labeled what percentage of the total pie it represents.
  • Bar/Line Charts. Display one or more sets of Y values in relation to a single X value. Statit allows a wide variety of attributes for individualBar/Line Items to be controlled. Statit supports 7 different styles of Bar/Line charts:
    • Bar Chart. Displays vertical or horizontal bars next to each other.
    • Area Chart. Plots one or more variables with the area between the X axis and the values filled in, creating a colored, shaded or pattern filled area.
    • Curve (spline) Chart. Plots a fitted curve through each value in the variable.
    • Line Chart. Plots one or more variables in a fashion similar to that of an Area Chart, but without the filled area beneath the plot.
    • LoWeSS (Locally Weighted Scatterplot Smoother) Chart. Plots values for a variable with a robust smoothed curve fitted to the values added.
    • Point Chart / Scatterplot. Plots the values of a variable as individual points on the chart.
    • Trend Chart (linear regression). Plots the values of a variable and overlies a "trend" line.
  • XY Chart. An XY chart typically plots one or more Y values against a single X value. Statit allows up to 6 unique X values to each be plotted against a corresponding unique Y value. There are three types of XY charts:
    • Point Chart / Scatterplot. Plots one or more XY pairs of variables on a single chart, showing each pair as an individual point in the plot.
    • Line Chart. Plots one or more XY pairs of variables on a single chart, with each plot shown as a connected line.
    • Curve (spline) Chart. Plots one or more XY pairs of variables on a single chart, with each plot shown as a smoothed spline curve running through the points in the plot.
  • Histogram. Plots a histogram for measurement variables.

Distribution Plots

  • Box Plot. Displays a box plot for each variable in the variable list. The procedure requires a real variable and can handle a grouping variable that is numeric or string.
  • Probability Plot. Display a normal probability plot for a single variable. The purpose of this plot is to show whether the data approximates a normal distribution, which can be an important assumption in many statistical analyses.
  • Q-Q Plot. Examines the distribution of one variable or compares the distributions of two variables. It may be used to generate any one of three types of plots:
    • Percentile plot
    • Percentile Comparison
    • Empirical Q-Q Plot

Relationship Plots

  • X-Y and Contour Plots:
    • XY Plot. Displays a single variable on the X axis and one or more variables on the Y axis. The default XY plot produced is a scatter plot. Each point in the graph is identified by a marker symbol. Where there are multiple Y variables, a different marker is used for each Y variable. An XY plot may also have the points connected; these are called curve plots.
    • XYZ Plot. Displays a scatter plot where a classification variable is used to determine groups for a single Y variable. Each of these groups will be plotted as a separate Y variable, up to a maximum of 12 groups.
    • Bubble Plot. Displays a single XY plot with the marker symbol size based on a response variable. The marker symbol is always a circle.
    • Sunflower Plot. Is useful when both the X and Y variables are categorical and the response variable contains counts or frequencies. The values of the response variable are represented by petals—a single point is represented by a dot.
    • Contour Plot. For each point (x, y) in equally-spaced grid of points in the X-Y plane, a representative value of z is computed by local smoothing (fitting a local quadratic regression). Then a contour plot is made representing the relation of these computed z-values to (x, y) points in the grid.
  • Scatterplot Matrix. Displays scatter plot matrices, that is, all variables in the list are plotted against each other. This makes it easy to track an interesting point or group of points from plot to plot. An optional smooth curve can be drawn through each scatterplot to help visualize the relationship between the two variables.
  • Function Contour Plot. Displays contour plots of a mathematically-defined relation Z = f(X,Y), as opposed to a contour plot for empirical data. The plot is drawn in a manner that represents three-dimensional relationships in two dimensions. Lines or areas in the plot represent levels of magnitude, Z, corresponding to a position (X,Y) on a plane.

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