An Overview of Aabel 3 Features
SummaryAabel is a modern, powerful, and featurerich Mac OS X Universal package, including:
System Requirements


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Aabel Unique Pipeline ArchitectureAabel employs a pipeline architecture that allows realtime, twoway interaction between graphic viewers and their source worksheets, and between plotted data displayed on the same or on different viewers.This unique design has many advantages:
of Aabel Pipeline Design Data Exploration ToolsData brushing can be used to highlight data interrelationships (see the image below). However, Aabel takes data exploration beyond data brushing. 

 A powerful tool called Xzoom (X for exclude) allows walking through hierarchies of numeric or categorical data rapidly, to discover patterns and information that are otherwise masked.
 Data filtering: if you have mapped data patterns that you want to explore further, you can use the viewer filtering tools to extract subsets of data using multidimensional filter criteria.)
 New: Xzooming now allows walking through hierarchies of data in both directions (i.e., stepwise excluding, and stepwise return to previous states).
For more information regarding the use of exploration tools, click here.
Multidimensional Data Filtering Tools
 Aabel provides easy to use tools for data filtering, comparable to a database search.
 You can use multiple criteria that can span multiple variables for generating subsets of data.
 Data filtering can be performed in worksheets, database metaphors, graphic viewer documents, and data manipulator.
 The graphic viewer supports data filtering for generating subsets of data based on data patterns.
 The data filtering results can be used interactively, or saved in autogenerated Aabel worksheets.
To view examples, click here.
Statistics and Exploratory Data Analysis
Aabel Stats Analyzer
The Stats Analyzer has a modern interface that is designed to provide flexibility and ease of use. It is an integral part of the graphic viewer that allows creating presentation and publication quality graphic outputs (tables and graphs).
 Aabel Stats Analyzer is designed to allow different analyses without the need to create a different viewer for each method.
 The graphs and tables generated by statistical methods and multivariate data analysis will be displayed on the viewer page(s).
 The viewer filters, if activated, will be available to Stats Analyzer.
 Many methods allow storing the results in autogenerated worksheets that can be used for followup analysis, etc.
Statistical and Multivariate Data Analysis Methods
Testing for Normality
 Probability plot and accompanying ShapiroWilk test
 KolmogorovSmirnov test for testing normality of a single sample
 New: ShapiroWilk test for normality
Testing for Homogeneity of Variance
 New: Hartley's Fmax test
 New: Bartlett's chisquare test
 Oneway betweensubjects ANOVA (for more information, click here)
 Twoway betweensubjects ANOVA (for more information, click here)
 Oneway withinsubjects (repeated measures) (for more information, click here)
 New: Twoway repeated measures ANOVA (withinsubjects factorial) (for more information, click here)
 New: Twoway mixed factorial ANOVA: mixed betweenwithin design (for more information, click here)
 New: Testing Strength of Association: Partial ω^{2}
 New: Locally best invariant test
 New: Multisample sphericity test: Harris W
 New: Extended data layout options for ANOVA designs
Multiple Comparison Tests Accompanying ANOVA
 Tukey's HSD Test
 NewmanKeuls (NeumanKeuls) Test on Ordered Means
 Tukey B Test for Contrast on Ordered Means
 New: Dunnett Test
 TukeyKramer Test
 Scheffé Test
 BonferroniDunn Test
For more information regarding multiple comparisons accompanying ANCOVA, click here.
Analysis of Covariance (ANCOVA)
Singlefactor betweensubjects ANCOVA:
 Analysis of covariance, analysis of variance for dependent, and analysis of variance of covariate.
 New: Testing for homogeneity of regression
 New: Tukey's HSD Test on Adjusted Means
 New: Scheffé Test Adjusted Means
For more information regarding singlefactor betweensubjects ANCOVA, click here.
 The ChiSquare GoodnessofFit Test
 The SingleSample ChiSquare Test for Population Variance
For more information, click here.
tTests
 Single samples ttest
 Paired samples ttest
 Unpaired samples ttest
 New: Extended data layout options for unpaired samples ttest
FTest
 Testing equality of variance
 New: Extended data layout options for Ftest
zTests
 Paired samples ztest
 Unpaired samples ztest
 New: Extended data layout options for unpaired samples ztest
 Correlations and covariance matrices
 Pearson productmoment correlation coefficient (Pearson's r)
 New: Fisher's z transformation (z_{r}) (provided for both correlation matrix and Pearson's r)
 Spearman's rankorder correlation coefficient (Spearman's ρ) (nonparametric)
 Kendall's rank correlation coefficient (Kendall's τ) (nonparametric)
For more information, click here.
Internal Consistency Reliability
 New: Cronbach's alpha
 New: KuderRichardson rho (Formula 20)
 New: KuderRichardson rho (Formula 21)
For more information, click here.
 Wilcoxon signedranks test
 Wilcoxon matched pairs signedranks test
 New: Correction for continuity for the normal approximation of the Wilcoxon signedRranks and Wilcoxon matched pairs signedranks
 MannWhitney U test (Wilcoxon ranks sum test)
 KruskalWallis test
 New: Extended data layout options for MannWhitney U and KruskalWallis tests
 Spearman's rankorder correlation coefficient
 Kendall's rank correlation coefficient (Kendall's tau)
 Friedman twoway analysis of variance by ranks
 KolmogorovSmirnov goodnessoffit test for a single sample
For more information, click here.
 Twoway contingency tables: a twoway frequency table used to summarize two categorical random variables
 Twoway tables of cell descriptive statistics (mean, median, variance, standard variation, standard error, etc.)
 Mosaic/Parquet matrix diagrams: a graphical method for visualizing n x m contingency tables
For more information, click here.
KaplanMeier Survival Analysis
 KaplanMeier survival analysis using raw survival data
 KaplanMeier survival analysis using summarized survival data
 Output including survival curves, logrank significance test report, and life table
For more information, click here.
Receiver Operating Characteristic Curves (ROC)
 ROC for a single test
 ROC for two tests on paired samples
 ROC for two tests on unpaired samples
For more information, see examples.
Regression Analysis and Curve Fitting
Regressions concerning two continuous variables
 Linear (X on Y)
 Linear (thru zero)
 Major axis
 Reduced major axis
 Polynomial
 Exponential
 Logarithmic
 Power
For more information and examples, click here.
Cubic Spline interpolation
 A cubic spline is made from piecewise thirdorder polynomials that pass through the control points provided.
UserDefined NonLinear Regression
 Userdefine regression provides a library of functions and an interactive interface.
For more information regarding regression methods in Aabel, click here.
 Logistic regression in Aabel includes probability and logit (probability) and allows flexible customizing of the generated charts.
 Probability charts with multiple dimension projections also generate a table of statistics.
For more information and examples, click here.
 Multiple regression analysis provides an equation that relates a single dependent variable to multiple independent (predictor) variables.
 Aabel provides predicted values and residuals, an ANOVA report for total regression, the regression summary table (providing regression parameters, the corresponding statistic, and the confidence limits), and an ANOVA report for testing the significance of individual predictor variables.
For more information, click here.
Partial Least Squares Regression (PLS)
 The PLS method implemented in Aabel (i) uses principal component analysis (PCA) to derive the prediction functions from factors calculated from crossproduct matrices involving both Y and X variables, and (ii) allows predicting one or more dependent variables from a set of independent (predictor variables).
 PLS regression can be performed on a single data set (as for multiple regression), or using a training data set for predicting unknowns, new events, etc.
 The predictor coefficients and the predictions will be stored in two autogenerated worksheets.
For more information, click here.
 New: Mahalanobis Distance
 New: Jackknifed Mahalanobis Distance
Log Center transformation is necessary for proportion or percentage data, i.e., for data where the row sums of the selected variables have a constant sum. The outlier analysis UI controls include the necessary preprocessing trasformation methods (such as logcentring), which can be chosen, if needed.
For more information, click here.
Principal Component Analysis (PCA)
 PCA analysis allows optional preprocessing of the data prior to the main analysis.
 PCA results can be stored in the source worksheet or in a new worksheet.
 The scores and loadings can be graphically displayed using Aabel charts and tables.
For more information, click here.
 Factor analysis allows optional preprocessing of the data prior to the main analysis.
 Aabel provides Rmode and Qmode factor analyses and the option for Kaiser varimax rotation.
 The loadings, communality, and "unique" data will be placed in an autogenerated worksheet.
You can use Aabel graphing capabilities to display the loadings, the fraction of variance of the variables explained by the model (i.e., communality) and the fraction that is not (i.e., "unique").
For more information, click here.
Polynomial Trend Surface Analysis (Map Analysis)
 Polynomial trend surface analysis of XYZ data
 Polynomial trend surface analysis of matrix data
 Matrix cellwise operations
The map analysis output includes new matrices and/or XYZ estimates of trend and residuals, as well as an ANOVA report for significance of regression of K^{th} order polynomial trend surface.
For more information, click here.
 The method partition a data set of n objects into k clusters via an iterative process that continues until the sum of squares from points to the assigned cluster centers is minimized, i.e., until all cluster centers are at the mean of their voronoi sets.
 Aabel uses the method of Hartigan and Wong (1979), performs several random starts, and attempts to converge to a global minimum of the squared error distortion.
For more information, click here.
 The weighted pairgroup with arithmetic averaging
For more information, click here.
 Dot plots are an alternative to histograms of continuous data; in a dot plot, each data point (individual observation) is plotted on a continuous scale using a symbol (on the Xaxis).
 Option of applying object marker colors to dot symbols allows distinguishing groups of data.
For more information and examples, click here.
New: Parallel Dot Plot of Repeated Measures
This graph is designed to display dot plots of score (response values) form k >=2 repeated measures (dependent samples) on axes that are parallel to one another and equally spaced, with all axes having the same value range (see the righthand side image).
 Lines extending from their positions on one axis to the next connect the dependent data points.
 The probability significance test (p value) provided with the plot is based on oneway repeated measures ANOVA.
For an example, click here.
Heatmap displaying a worksheet matrix
 A basic heatmap diagram displays a worksheet matrix layout using a color scale, i.e., the color of each cell (within the color scale applied) is determined by its values.
Heatmaps with optional scaling of Zvalues
 Optional scaling of Zvalues allows representing the colorcoded Z scale proportional to the corresponding cell values.
To view examples of basic heatmaps, click here.
Frequency Distribution/ Histograms
Histograms
 Histograms of continuous data: absolute, relative, cumulative
 New: zScore histogram
 Histograms of categorical data: absolute, relative, cumulative
 Pareto charts
 Ogive
 Spine plots
For more information, click here.
Oneway frequency analysis of categorical data
 This method generates a frequency table from a single categorical variable.
New: Nway frequency analysis of categorical data
 This method allows generating nway frequency output reports with 2Ddataslices representing levels of two or more grouping variables.
For more information, click here.
Statistical Quality Control Using Shewhart and Other Control Charts
Aabel provides Shewhart control charts for variables, Shewhart control charts for attributes, and other related charts:
 Xbar (R) chart
 Xbar (S) chart
 R chart
 S chart
 LeveyJennings chart
 Individual measurements chart
 Moving range chart
 p chart
 np chart
 c chart
 u chart
The Westgard multirules procedure and Western Electric Company (WECO) warning rules can be optionally applied to the appropriate quality control charts.
For more information, click here.
 Normal Probability
 Uniform Probability
Normal probability plot that is provided as an option for testing normality, allows adding ShapiroWilk normality test to the plot output.
For more information, click here.
Box & Whisker and BoxPercentile Plots
 Oneway box & whisker plots (regular and notched)
 Twoway box & whisker plots (regular and notched)
 Different options for plotting the whisker and outliers
 OneWay boxpercentile plots
 TwoWay boxpercentile plots
 New: Extended data layout options for generating box & whisker and boxpercentile plots
For more information regarding box & whisker and boxpercentile plots, click here.
Bar and Line Plots of Mean, Median, Max., Min.
These plots are used for comparing mean, medians, maximum, or minimum values of multiple variables, or of subgroups/categories of variables.
 Oneway bar plots
 Twoway bar plots
 Oneway line plots
 Twoway line plots
 New: Extended data layout options for generating bar or line plots of mean, median, etc.)
 To add error bars to mean bars/lines, the options include:
 Standard error of mean
 Standard deviation
 New: Confidence interval (including options of 90.0%, 95.0%, 97.5%, 99.0%)
For more information regarding bar plots, click here; for more information regarding line plots, click here.
 Stacks of mean lines, used to display the effect of one factor at each level of another factor
 To add error bars to interaction plots, you can choose one of the following options:
 Standard error of mean
 Standard deviation
 New: Confidence interval (including options of 90.0%, 95.0%, 97.5%, 99.0%)
For more information, click here.
These plots compare the response values (scores, measurements) obtained from k >=2 samples/groups, each of which representing data from pqs levels of experimental conditions. The plot options include:
 Threeway mean bar plot
 Threeway mean dot plot
 To add error bars to mean bars/dots, you can choose one of the following options:
 Standard error of mean
 Standard deviation
 New: Confidence interval (including options of 90.0%, 95.0%, 97.5%, 99.0%)
For more information, click here.
 Oneway diamond mean comparison plot
 Twoway diamond mean comparison plot
 New: Extended data layout options for generating diamond plots)
For more information, click here.
Bland & Altman and Paired tTest Difference Plots
In Aabel, the Bland & Altman method for comparing two methods of measurement or two paired variables provides:
 A plot of differences vs. mean
 A plot of differences as a % of averages vs. mean
 A plot of ratios vs. mean
 In addition to the plot, the results can optionally be displayed in a table or stored in a worksheet.
For more information and example graphs, click here.
 Oneway descriptive statistics that can include mean, median, standard deviation, standard error, maximum, minimum, variance, skewness, sum, count/sample size
Primary and Secondary Data Transformations
 Standardizing, normalizing, etc.
 logcenter and meancenter transformations
 Ranking variables individually or jointly
Thematic Maps and Map Projection Utilities
A thematic map displays the spatial distribution of an attribute relating to specific data themes (e.g., population density, rainfall, soil type, median age, number of housing units, pollutants concentration in air, etc.). Aabel provides import facilities, map projections utilities and graphing abilities to create thematic maps.
Importers
Importers are provided to enable users to import and arrange polygon data and create thematic maps that are suitable for their purpose, instead of being provided with maps without ability to choose or modify them. Aabel provides importers for:
 ArcView shape files and associated dBase files
 Formats provided by the USGS Coastline Extractor: Arc/Info Ungenerate, Mapgen, Matlab, Splus
Thematic map diagrams
 Polygon thematic maps (that can display descriptive statistics or original values of associated data by colorcoding)
 Polygon point frequency thematic maps
 Thematic grouping (a feature that allows combining a base map with a chart such as a bubble diagram to create thematic maps using point symbols, or with a scatter pie to display contribution of parts to a whole in different XY coordinates)
Map projection utilities (coordinate transformations)
 Map projection utilities (coordinate transformations) are an integrated part of Aabel worksheets, and include 19 projection systems.
For more information and example thematic maps, click here.
Data Visualization and Graph Types
Features of Graphic Viewer
The graphic viewer is the Aabel interface for data visualization & charting and for displaying the graphical results of statistical analysis.
 The graphic viewer can work simultaneously with multiple data sources, and has a realtime, two way interaction with data.
 The graphic viewer toolbar provides tools for interacting with data, zooming, moving and resizing graphics, creating tables and single or multiline text, drawings, and numerous customizing features.
 Saved graphic viewer files do not store static blocks of data; they store references to the source documents containing the data.
 The viewer palettes include data browsers and controls that allow interaction with data, dynamic data visualization and charting, arranging and ordering graphic sublayers.
 You can create color themes that are stored as part of the global parameters and can be accessed from any viewer.
Specialized Scientific Graphing
Structural Diagrams: Stereographic, Rose, and Lineament
The corresponding data for stereographic charts are stored in worksheets with selected variables as:
 Trend
 Dip/Plunge
 Azimuth
Stereographic diagrams (displaying stereographic projections of directional data):
 Stereographic scatter
 Stereographic contour
Rose diagrams (circular histograms):
 Full circle rose with area of the petals proportional to the frequency (symmetric or asymmetric>
 Full circle rose with length of the petals proportional to the frequency (symmetric or asymmetric
 Half circle rose
Structural lineament diagrams:
 Full circle lineament (symmetric or asymmetric>
 Half circle Full circle lineament
To view examples, click here.
Geospider Plots Diagram (MultiElement Spidergram)
 Geospider diagram, also known as a multielement spidergram, is used to compare the element composition of a rock, normalized against known reference rocks.
 Aabel is distributed with a plot definitions library that includes 34 spidergram reference plot definitions for representing geochemical data.
To view examples, click here.
 This diagram is designed to plot genomic sequence alignment by mapping query sequences to target sequences.
 Four plot types are provides.
 Stepwise Xzooming allows exploring the sequence alignment data.
To view examples, click here.
Combination Column Graphs (CCG Diagrams)
 This diagram is designed to permit a combination of single and/or multiple variable column graphs that reflect variation against sorted values (in an increasing order) of a common Yaxis. It is hence ideal for plotting variations against depth, time, or similar variables.
 Each column graph can be created from a single variable or from multiple variables.
 The CCG graphs can combine bars, text, area or stacked area, and single or multiple line plots.
To view examples, click here.
GeneralPurpose 2D, Ternary, Matrix, Contour, and Polar Graphs
Matrix Scatter Diagram and Related Plots
 Full matrix with X=Y cells displaying histograms, variable names or other options
 Half matrix
 Multiprofile diagram
 Styles include:
 Connecting data points by X, by Y, or by pipeline order
 Connecting data points by bgroup by X, by Y, or, by pipeline order
 Connecting data points to origo
 Displaying convex hulls of groups
 New: Connecting data points to group centroids
 New: Connecting data points to group medians
 New: Connecting the group centroids
 New: Connecting the group medians
To view examples, click here.
Voronoi Diagram (Thiessen or Dirichlet Polygons)
 The Voronoi diagram subdivides the XY space of the chart into polygons surrounding each data point. These polygons represent the most compact division of space possible.
 The plot types include:
 Plain Voronoi
 Polygons color coded by calculated area
 Polygons color coded by values of a third variable
 Polygons color coded by areanormalized values of Z
To view examples, click here.
Waterfall charts are ideal for comparing variations between multiple data sets that are obtained under similar conditions.
 New: Waterfall line series
 New: Waterfall area series
 New: Waterfall pseudosurface
To view examples, click here.
2D and Ternary Contour Charts
 XYZ contour (contouring by triangulation)
 Contouring by gridding; for creating a continuous surface from an unevenlyspaced data set; Aabel gridder uses:
 The nnearest neighbors method, with or without a quadrant or octant constraint on the search pattern
 New: The option of generating the real world coordinate values for labeling the columns and rows of the gridded data (this feature allows displaying the real world coordinate values along the X and Yaxis of matrix plots such as gridded vector graphs or heatmaps)
 Contour matrix chart (map view of regularly spaced data)
 Ternary contour (a 2D projection of a ternary diagram using four variables, contouring by triangulation)
To view examples, click here.
 Bubble charts displaying values of a 3rd variable using size of the bubble marker (the bubble markers can be round or square)
 Bubble charts displaying values of a 3rd variable using a color scale
 Bubble charts displaying values of a 3rd variable using size of the bubble marker, and of a4th variable using a color scale
To view examples, click here.
 Ternary scatter: a 2D projection of a ternary plot, displaying the relative proportions (as percentages) of three variables on three axes
 Ternary contour: a 2D projection of a ternary diagram that displays values of a 4th dimension using a color scale
 New: Ability to draw the plot clockwise or counterclockwise
 The option of displaying error regions (calculating all possible combinations and using convex hull to delineate the resulting hexagonal error region)
To view examples, click here.
 Stacked area spider with equal scaling
 Line spider chart
 Line spider with equal scaling (i.e., values of spokes are equally scaled relative to the one with the largest value)
 Line spider for percentage data
 Line spider that transforms data to 100%
 Filled spider chart
 Filled spider with equal scaling (the area covered by different categories (data objects) are filled by color)
Spider charts, also known as radar charts, plot variations in multiple data series that represent the same factors.
To view examples, click here.
 Polar line series
 New: Polar stacked area
 New: Polar stacked petal
 New: Polar scatter series
Polar charts can be used to display nondirectional data series (e.g., wind speed) against variation in angles. Aabel provides:
To view examples examples, click here.
XY Binary Scatter and Related Charts
A binary scatter chart displays the data as a set of points on an X, Y set of coordinates. You can generate subsets of data from selected data points, and the selections can be regular or irregular.
Binary scatter graph types:
 Binary scatter displaying values of a 3^{rd} variable as value labels at the XY data point coordinates
 Connecting data points by X, by Y, or by pipeline order
 Connecting data points by group by X, by Y, or, by pipeline order
 Connecting data points to origo
 Displaying convex hulls of groups
 New: Connecting data points to group centroids
 New: Connecting data points to group medians
 New: Connecting the group centroids
 New: Connecting the group medians
To view examples, click here.
XY and Double XY Scatter Series Charts
Scatter series charts display scatter data points from one or more data series, plotted against a value axis, a category axis, or date & time. In a horizontal style plot, data from each worksheet row have the same X coordinate and unique Y coordinates. In a vertical style plot, data from each worksheet row have the same Y coordinate and unique X coordinates.
 XY horizontal scatter series
 XY vertical scatter series:
 Scatter series across X categories
 Scatter series across Y categories
 Double Y horizontal scatter Series
 Double X scatter series
 Double XY scatter series
 XY line series (sort along X): X and Y valueaxes
 XY line series (sort along Y): X and Y valueaxes
 Line series across X categories: Y valueaxis, X is categoryaxis
 Line series across Y categories: X valueaxis, Y categoryaxis
 Double Y line series (sort along X): Y1, Y2, and X valueaxes
 Double X line series (sort along Y): X1, X2, and Y valueaxes
 Double Y line series across X categories: Y1 and Y2 valueaxes, X categoryaxis
 Double X line series across Y categories: X1 and X2 valueaxes, Y categoryaxis
 Double XY line Series (sort along X1, X2): Y1, Y2, X1, X2 all valueaxes
 Double XY line Series (sort along Y1, Y2): Y1, Y2, X1, X2 all valueaxes
 Added features to XY line charts include:
 New: Using a color gradient range to differentiate the series (instead of defining color attributes for each series individually)
 New: Breaking the series lines for missing data points
To view examples, click here.
 Column and charts
 Stacked column and stacked bar charts
 Clustered column and clustered bar charts
 XY (valueaxes) column and bar charts
To view examples, click here.
 Singlevariable area charts
 Stacked (multiple variables) area charts
 XY (valueaxes) area charts
To view examples, click here.
Combination Charts (ColumnLine, BarLine, AreaLine)
 Double Y combination columnline
 Double Y combination barline
 Double Y combination arealine
 Double X combination arealine
To view examples, click here.
To view examples, click here.
Diagrams of Multiple, Independent ValueAxes Column, Bar or Area Graphs
 MultipleY column (each Yaxis is an independent value axis, and the X is the diagram's common category axis)
 MultipleY area
 MultipleX bar (each Xaxis is an independent valueaxis, and the Y is the diagram's common categoryaxis)
 MultipleX area
 Automatic data processing of the value axes includes:
 Calculating fraction for each data object (worksheet row)
 Calculating fraction for each variable (worksheet column)
 Standardizing each data object (worksheet row)
 Standardizing each variable (worksheet column)
To view examples, click here.
Pie Charts and XY Scatter Pie
 2D pie chart
 3D pie chart
 Different options are available for scaling the individual pies when plotting more than one pie in a given chart pane: radius (r) proportional to sum (linear or logarithmic), r² proportional to sum (linear or logarithmic), or r³ proportional to sum (linear or logarithmic).
 XY scatter pie charts display pies on an XY chart, i.e., each whole pie represents a worksheet row, each pie slice is a variable, and the center of each pie has a unique XY coordinate.
To view examples, click here.
HighLow, HighLowClose, OpenHighLowClose, and Range Charts
 Highlowclose (HLC) charts
 Openhighlowclose (OHLC) charts
 Range charts (using ordered or unordered data series)
 XY (valueaxes) highlow charts (the high and low values of one or more data series are plotted against a continuous variable)
 XY (valueaxes) range charts (the high, midpoint, and low values of one or more data series are plotted against a continuous variable)
To view examples, click here.
 Matrix vector angle (with geographical and geometrical options)
 Matrix vector radiusangle (with geographical and geometrical options)
 XY vector (with geographical and geometrical options)
To view examples, click here.
General Purpose 3D Charts
3D Contours and 3D Mesh Plots
 New: 3D contour surface of regularly spaced data
 New: 3D mesh (wire frame) plots
 3D XYZ Contour Points (3D projection of irregularly spaced data, contouring by triangulation)
To view examples, click here.
3D Scatter and 3D Spinning Scatter
3D Scatter
 Displaying the data points at intersections of X, Y, and Z values in a 3D grid
 Providing the option of displaying values of a 4^{th} dimension using a color scale
To view examples, click here.
3D Spinning Scatter
 Suitable for exploratory pattern recognition in multivariate data
 Providing the option of displaying values of a 4^{th} dimension using a color scale
3D Column, Pyramid, Band, and Area Charts
 Different types of prism/column, pyramid
 Pyramid
 Area
 Band
To view examples, click here.
Applying Error Bars
Adding error bars to charts allows a graphical display of the statistical probability of errors, the experimental and analytical errors, etc. Error bars can be symmetrical or asymmetrical, and can be based on standard error, standard deviation, fixed values, or values of a variable (e.g., calculated errors, etc.).
The Error Bars Defined for Individual Variables
 Binary scatter plots
 Matrix Scatter diagram
 Scatter series
 Line series
 Column and bar charts (including stacked and clustered columns and bars)
 Area and stacked area charts
For error bar examples, click here.
 The error bars defined for individual variables can not be applied independently to a ternary chart.
 Aabel calculates 33 possible combinations for each ternary data point and uses the convex hull to delineate the resulting hexagonal error region.
For more information regarding ternary error regions, click here.
Curve Fitting
Curve Fitting
The curve fitting features were outlined earlier, in the context of regression analysis.
 For more information regarding curve fitting with predefined functions, click here.
 For more information regarding userdefined, nonlinear curve fitting, click here.
Graphic Export Formats
Format for exporting graphics
 Bit images (raster files), including:
 BMP
 JPEG
 TIFF
 PNG
 Bitmap PICT
 Photoshop
 SGI
 TGA
For information regarding export resolution and to view example, click here.
Formats for copying graphics/ drag & drop
Features of Aabel Worksheets
The worksheet features and capabilities are outlined below. For an overview of worksheet capabilities, click here.
 Excel format: 95, 972004 workbook (.xls)
 Delimited text data (tab, comma, semicolon, space, etc.)
 Fortran formatted data
 Delimited numeric matrix data
 Binary numeric matrix formats (8 bit, 16 bit, 32 bit, and 64 bit data)
 dBase (II, III, IV) formats
 Arc/Info Ungenerate
 Mapgen
 Matlab
 Splus
 ArcView shape files
To view the UI for delimited and shape file importers, click here.
Supported Variable Types
 Numbers (any legal numerical representation with no explicit sample space)
 Cartographic coordinates:
 Longitude, Latitude
 Easting, Northing
 Directional/orientational data:
 Trend
 Dip/Plunge
 Azimuth
 Date and Time (30 builtin date & time formats, and tools for customdefining a format)
 Text (with Unicode support)
Numbers of Rows & Columns
 Aabel does not impose limits on the number of rows and columns in worksheets. The limitation is mainly related to the speed penalty imposed by currently available hardware technology and the CPU resources and physical memory available to the application. Other factors that can impose speed penalty are (i) large amounts of complex unicode textual data, and (ii) use of complex symbols, or unicode symbols for graphing large data sets.
Quality Control of Numeric Data
Aabel's quality control tools allow:
 Displaying report summary statistics for each variable with a single click
 Checking the number of missing values of a variable (discontinuous data, etc.)
 Detecting outofrange values (impossible values, punching errors, etc.) and highlighting them in the corresponding worksheet rows
 Dealing with outofrange values using different options
Predefined Units of Weights and Measures
 Data stored in different worksheets or databases do not always have the same units of measurement. This may result in inconsistencies during data analysis and processing from multiple data sources. Aabel provides the optional feature of tagging the variables with their appropriate units.
 A wide range of predefined units of weights and measures are included. Once you set or change your preferred units in the global preferences, during data analysis, charting, or data processing, Aabel checks the tagged variables for unit inconsistencies and transforms the data to your preferred unit without modifying the source data.
The Worksheet Map Projection Utilities
Aabel worksheet projection utilities allow:
 Transforming data that has been imported with no explicit sample space to cartographic coordinates and store the data as longitudelatitude, or to define data using a different projection system
 Transforming data from one projection system into another system
For more information regarding this feature, click here.
 It displays a list of all variables present in the worksheet
 If a worksheet has been generated from the results of data filtering, the userdefined filter criteria will be stored in the worksheet notebook
 If the worksheet has been generated from crosstabulating and pivoting, the information regarding the original worksheet file, primary and secondary split variables, etc., will be stored in the worksheet notebook
 It provides a text area for storing notes, e.g., data source references, etc.
The worksheet notebook has four functions:
To view the UI, click here.
The Worksheet Data Management Tools
 Crossinterpolation
 Sorting with a single as well as with multiple keys
 Transposing
 Merging multiple textual data columns into one column
 Merging numeric or textual data without concatenating
 Reordering rows and columns
 New: Providing a palette for rapid scanning through data columns in a worksheet with large number of variables or for bringing a specific data column into the field of view
 Recoding text, or numeric values to categories
 Pivoting and summarizing multidimensional data (cross tabulation)
 Splitting and pivoting saw data
 Splitting and recoding a categorical variable to binary dummy variables
 Stacking columns
 Grouping data objects based on values of a categorical variable: mapping categorical data to markers
 New: Grouping data objects based on values of a numeric variable: mapping numeric values to marker color properties
 Mapping object markers to categorical data
 Mapping marker codes to marker
 Multidimensional data filtering tools for generating subsets of data
The Worksheet Formula Editor
 The formula editor provides 46 predefined mathematical and statistical functions, and includes standard as well as Boolean/conditional operators.
 In Aabel, variables are identified by names, and not by the position of worksheet columns. However, to facilitate sequential calculations, the formula editor provides functions that operate on variables by indices instead of names.
 New: The current version allows addressing worksheet row indices, i.e., Aabel worksheet operations are column based; the new feature enables addressing the worksheet cells explicitly.
 In addition to functions provided for columnwise calculations, the formula editor provides a number of functions for rowwise calculations.
 Assign and calculate commands are provided for applying the same formula to multiple columns.
The Worksheet Symbol and Color Palettes
 Symbol and color palettes are used for defining markers that represent individual data objects, data groups, or the source worksheet(s) on the plotted graphs. For significance of worksheet markers in data representation, view an example.
 Color palettes are provided for modifying color attributes of symbols associated with different marker types
 A color palette is provided for coloring the values of selected cells.
Data Manipulator
While the worksheet formula editor is provided for data processing within a given the worksheet, the data manipulator has the following abilities:
 It can simultaneously process data from multiple worksheets and database metaphors
 It allows building custom formulas
 It permits multidimensional data filtering to enable data processing based on userdefined criteria
To view the manipulator UI, click here.
Aabel Database Metaphor
An Aabel database metaphor is a data management container for holding any combination of Aabel worksheets and folders containing worksheets. A saved database metaphor document does not store a static block of data, but stores references to the source worksheets.
 It acts as a dynamic data source for data analysis, data processing, and data visualization and charting
 The current features are designed for:
 Being used as a more powerful alternative to templates
 Easy exclusion/inclusions of groups of objects in exploratory plots
 Performing multidimensional data filtering of datasets sourced from multiple worksheets
For functions, practical use, and limitations of database metaphor, click here.
Aabel Viewer Text and Table EditorsAabel table and text editors support Unicode. The Table Editor
The Text Frame (Text Box) Editor
The Text Line Editor
For more information regarding Aabel table and text editors, click here. 
Unicode SupportAabel supports Unicode for storing data in worksheets, for graphing data, and for creating tables, text lines and text frames. Ranking of categorical data is based on the US English writing system to ensure consistency in statistical outputs independent of the machine language settings. 
Viewer UserDefined Color Themes
Graphic Sublayers Manager
Each chart pane or other graphic object you create is placed in a transparent sublayer of the current viewer page and its specifications are displayed in the graphic sublayers palette. The sublayers palette allows:
 Creating multiple live charts (hotlinked to data), from the same pipeline or different pipelines, on a single viewer page
 Overlaying XY charts, or creating a thematic group
 Selecting objects (useful for selecting objects positioned behind other objects on the viewer page)
 Changing the stacking order of sublayers and hence the objects they contain
 Hiding sublayer, locking the objects they contain, deleting objects, etc.
 Modifying the chart pane size by defining exact dimensions
 Modifying the transparency of fills, lines, and markers of 2D graphs
 Applying Xzoom to exclude data from a given chart without affecting other charts using the same data source(s)
 Hiding/showing chart legends and 2D chart axes
Color Management and Interactive Color Palette Editor
The color palettes and color gradient palettes in Aabel are initially represented using default colors. However, you can freely change the items of a palette or create new color gradients from simple, complex, or pure colors, with a few clicks of the mouse: the color editor in Aabel is interactive and easy to use.
 Color palettes are present in worksheets, the graphic viewer, database metaphors, and all chart customizing dialogs. They allow choosing or modifying colors of markers, lines, fills, contours, colorcoded tables, text, etc.
 When using a color scale to represent data, the data range will map to the color space of the gradient palette. You can customize the color gradient palette or create and save new palettes for different plotting purposes.
For more information, click here.
Customizing Tools for Data Representation
Aabel Symbols and Unicode Glyphs
Colored symbols (markers) are used to represent data in many chart types. Markers representing data can be predefined or inherited from the source worksheet(s). The uniqueness of markers is defined by their symbols, color, and size. Aabel provides two types of symbol palettes:
 Aabel symbols: A palette with 175 marker symbols, each of which can be scaled from 50 to 200% of the initial size (100%) in 20 steps.
 Unicode glyphs/symbols: A palette that holds 176 Unicode characters/symbols/glyphs is available for data representation. Each item in this palette can be replaced by a Unicode character/glyph from the System character palette, and can be scaled from 50 to 200% of the initial size (100%) in 20 steps.
To view examples, click here.
Customizing Chart Axis Attributes
Numerous chartaxis customizing tools are provided for different chart categories. Features include (but are not limited to):
 Choice of linear or logarithmic scale, forward or reverse (when applicable)
 Editing abilities to choose title proxy names
 Applying subscript, superscript, or Greek glyphs to axis titles
 Control of the axis line thickness, color, length and type of major and minor grids
 Control of the axis range and steps (when applicable)
 Numeric label display format settings (fixed or scientific format, decimal places, use of leading zero, use of a prefix or suffix, etc.)
 Control of axis text properties and the position of labels (e.g., font properties, color, etc.)
 Controls for customizing axis attributes of plot types that do not share the common axis properties of 2D and 3D charts (e.g., spider, rose, stereographic, polar, mosaic, dendrograms)
For more information and examples, click here.
Displaying Object and Value Labels on Charts
 You can optionally display object or value labels on many Aabel charts.
 Value labels can display a 3rd dimension on an XY chart, a 4th dimension on a ternary, bubble, contour point, or 3D scatter chart, and a 5th dimension on a bubble, ternary contour, or 4D scatter chart.
 The labels can be customized with frames and backdrop colors, or different font properties.
To view examples, click here.
Customizing Chart Legends and Legend Entries
Legends provide explanation for data represented by graphical information.
Legend entries representing data groups or data series
 The legend keys (markers, lines, color or pattern fill)
 The legend text items (names of the data groups or series associated with the legend keys)
 Symbol, color, and size of markers representing data objects or data series
 Thickness, type, and color of lines representing data series
 Regression lines, confidence belt, and major axis ellipse fill and line attributes
 Fill attributes representing data series.
Legend entries editor allows modifying:
Legend entries representing data ranges
 Color scale legends are used in 2D and 3D contours, 3D mesh, bubble charts, waterfall pseudosurface, and thematic maps.
 The editor for this legend type allows adding frame to the legend, changing font attributes of the legend title, defining the legend labels font, and display format attributes, binning the color range, etc.
Legend font and background properties
 The legend font size and style can be changed using the Text menu or shortcuts.
 Chart legends have a transparent background by default. You can optionally add a colored background to any legend type.
For more information regarding options for customizing legends and legend entries, click here.
High Quality Quartz Patterns for Black & White Publications Many 2D graph types use fill attributes to identify different data series or data groups (e.g., color or pattern properties of columns, bars, areas, etc.). Due to high cost of color publications, many users prefer to use B & W patterns (when possible) for publication purposes.


Managing Transparency
 The transparency tab in the Graphics Sublayers Manager provides separate controls for modifying transparency of fills, lines, and markers of 2D graphs.
Drawing Lines and Geometric Shapes Drawing tools include:
For these lines and geometrical shapes, you can change the color of line or fill, apply patterns to fill, and change transparency of the fill. 

Viewer PagesIn a graphic viewer you can create many pages, each of which can hold a single or multiple chart(s), graphical displays of statistical analysis, tables, etc.


UserDefined Templates
Templates can be created for each chart category and can include properties that do not have any datadependency.
 Examples of attributes that can be saved as templates are chart axis lines (thickness and color), axis label and title fonts (font type, size, color), markers, lines, fill attributes used for representing data series (if you are not using variable hotlinked properties).
 A saved template can be loaded into any viewer. The properties defined for any chart in the template will override the corresponding document attributes for the given chart(s).
Tutorial Style User Guide Documentation
The Aabel user guide is PDFbased and is designed to provide:
 An onscreen manual with highresolution graphics, flexible navigation controls, and complete hot linked crossreferencing throughout the user guide that includes over 600 illustrations
 High quality print output
 Stepbystep guide to using the diverse features and capabilities of the application