- 64-bit throughout
- Provides a highly modern GUI and unmatched integration of statistical/multivariate data analysis, exploratory analytics, scientific graphing & thematic mapping, data processing utilities, practical use and graphic quality.
- An icon of power and modeless simplicity for professional users (modeless panels and windows do not block other activities of the application).
- Distributed with three high quality PDF eBooks with over 1100 illustrations, providing a step-by-step guide for using the diverse features of the application, as well Quick Start Guide documentation for new users.

- Numerous statistical, multivariate, and exploratory data analysis modules:
- A unique data pipeline design that (i) allows real-time two-way interaction between the source worksheets and generated graphs, and (ii) is the technology behind the design of powerful data exploration tools such as X-zooming (walking through hierarchies of data by step-wise excluding and step-wise return to previous states), interactive multidimensional data filtering, and filtering based on data patterns/data regions:
- User-controlled managing of data flow through the Statistics and Visualization Pipelines for adding power and simplicity to data analysis:
- Exploratory analytics beyond data brushing and easy to use data-reduction techniques for complex multivariate data
- Interactive data visualization with over 160 chart types (including specialized as well as general purpose graph categories):
- Thematic mapping (including importers for ArcView Shape Files (.shp) and Arc/Info files), map projection utilities, and different thematic map graph types
- Multidimensional data filtering comparable to a database search for generating subsets of data using (i) the worksheet filter utility, and (ii) the graphic viewer filter utility

Univariate & Multivariate Statistical Charts: Overview

Specialized Scientific Graphing

General Purpose 2D, 3D, Matrix, Voronoi, Bubble, Others: Overview

Thematic Maps and Map Projection Utilities: Overview

Map Projection Utilities (Coordinate Transformations): Overview

- Simple design for accessing Stats Analyzer, Chart Designer, Graphic Organizer, and Interactive Filtering from the the same window, i.e., the graphic viewer:
- Native worksheets allowing import of diverse data formats (including the SPSS .sav files), supporting diverse variable types (continuous and nominal numeric values, string, date, trend, dip, azimuth, longitude, latitude, easting, northing), providing numerous data management tools and other utilities, and enabling a rapid glance at distribution and descriptive statistics of different variables, range control of numeric data, and more
- Modeless simplicity, now including over 95% of the application panels and windows
- Support for exporting vector graphics (PDF and EPS) and bitmap formats (PNG, JPEG, JPEG 2000, TIFF, TIFF, GIF, BMP)
- Interactive color management tools; choice of RGB vs. CMYK, creating and managing gradients
- Continuous viewer pages, which makes it possible to generate any number of statistical output, view them by scrolling throughout the pages, use them for live presentation purposes, or export them using different options (including exporting all viewer pages as a single PDF document)
- Graphic Organizer, a modeless panel with multiple important functions related to graphic contents of viewer pages
- Numerous flexible customizing capabilities, majority of which modeless panels, updating any modification on the fly
- Diverse document-independent data processing utilities including profile extractor, polygon data merger/data optimizer, trans-worksheet calculator, k-nearest neighbor and natural neighbor gridders, polynomial trend surface gridder, multi-worksheets sequential data merger, matrix cell-wise normalizer, matrix-to-matrix cell-wise operator, variable-based data transformer, 3D grid data processor, and 3D grid volume & area calculator:

Pipelines are data channels connecting the data sources (Aabel worksheets) (i) to graphic viewer data users (i.e., Stats Analyzer and Chart Designer), and (ii) to data processing utilities. Data pipelines have a unique design with many advantages:

- You can perform graphing or statistical analysis from multiple Aabel worksheets, without a need to merge the data into a single worksheet.
- You can have real-time interaction with data, e.g., change variables, change chart types, etc., all from within a single interface, and the results will be dynamically updated.
- Created charts are not static graphics. They are hot-linked to the source worksheet(s) used to create the chart(s). If you make changes to source worksheet(s), the modifications will be dynamically updated on all charts using the data.

More Information

Schematic Representation of Data Flow in Data Pipelines
Data exploration in Aabel goes beyond data brushing. The pipeline architecture of the application has enabled the design of powerful and dynamic data exploration capabilities such as:

- X-zooming (X for exclude), a tool with unique functionalities*
- Performing multidimensional data filtering interactively (while performing exploratory analysis)

* X-zooming allows walking through hierarchies of numeric or categorical data rapidly, to (i) discover patterns and information that are otherwise masked, and (ii) extract data subsets from factorial levels of categorical data, or from numeric data, interactively.

The GLM procedure allows the option of choosing dummy, effect, or orthogonal coding, and providing a simple and easy to use GUI.

- ANOVA: Between-subjects design
- Test of simple main effects for analyzing n-way interactions in between-subjects ANOVA
- ANOVA: Within-subjects (repeated measures) design
- ANOVA: Mixed between-within design
- ANCOVA: Between-subjects design

Pairwise multiple comparisons accompanying between-subjects design include:

- Tukey's HSD Test
- Newman-Keuls (Neuman-Keuls) test
- Tukey B Test
- Fisher's LSD
- Dunnett Test
- Tukey-Kramer Test
- Scheffé Test
- Bonferroni-Dunn Test
- Dunn-Sidak Test
- Dunnett' C Test
- Games Howell Test

- Shapiro-Wilk test
- D'Agostino-Pearson test
- Kolmogorov-Smirnov test (single sample)
- Probability plot

- Hartley's Fmax test
- Bartlett's chi-square test
- Levene test
- Brown-Forsythe test
- Cochran's C test

- Wilcoxon signed-ranks test
- Wilcoxon matched pairs signed-ranks test
- Mann-Whitney U test (Wilcoxon ranks sum test)
- Kruskal-Wallis test
- Spearman's rank-order correlation coefficient

- Kendall's rank correlation coefficient (Kendall's tau)
- Friedman two-way analysis of variance by ranks
- Kolmogorov-Smirnov goodness-of-fit test for a single sample

- One-way descriptive statistics
- n-Way descriptive statistics (generating tables from 2D slices)
- Multifactor cell means
- Weighted Arithmetic Mean

- One-way frequency analysis of categorical data
- n-Way frequency analysis of categorical data
- One-Way binning analysis of continuous data
- Two-Way binning analysis of continuous data

Gaussian kernel AMISE bandwidth analyzer:

- Efficient use of Kernel density estimate, which is widely used in data mining and pattern recognition, depends on computation of the optimal bandwidth of the kernel. While AMISE (Asymptotic Mean Integrated Squared Error) optimal bandwidth can provide the best estimation, it involves extensive and time-consuming computation.
- Gigawiz has developed a
**unique hybrid solver**for processing the data and estimating the corresponding AMISE optimal bandwidth. - The Gigawiz hybrid solver has been stress tested using 10000 data sets, including both real world and simulated data.

Gaussian kernel density trace charts including:

- A graph type whose plotting involves processing the data and computing the AMISE optimal bandwidth (using the Gigawiz hybrid solver) while generating the resulting chart on the fly.
- A graph type with the rule of thumb bandwidth selectors (i.e., Silverman's approach and percentage of sample range), for exploratory visualization of the effect of bandwidth on kernel density estimation
- Z-Score kernel density trace

- The Chi-Square Goodness-of-Fit Test (this test, also referred to as the
*χ*^{2}test for a single sample) - The Single-Sample Chi-Square Test for Population Variance

- Single samples t-test
- Paired samples t-test
- Unpaired samples t-test

- F-test

- Paired samples z-test
- Unpaired samples z-test

- Correlations and covariance matrices
- Correlation matrix with pairwise significance test
- Pearson product-moment correlation coefficient (Pearson
*r*)

- Fisher's
*z*transformation (*z*_{r}) - Spearman's rank-order correlation coefficient (Spearman's ρ)
- Kendall's rank correlation coefficient (Kendall's τ)

- Bray-Curtis and Zero-Adjusted Bray-Curtis Dissimilarity
- Correlation Coefficient Dissimilarity
- Euclidean and Manhattan Distance Measures

- Two-Way Contingency Tables
- Two-Way Tables of Cell Descriptive Statistics

Aabel provides the following methods for internal consistency reliability estimates:

- Cronbach's alpha
- Kuder-Richardson rho (Formula 20)
- Kuder-Richardson rho (Formula 21)

The survival analysis includes:

- Kaplan-Meier survival analysis
- Nelson-Aalen survival analysis
- Cox proportional hazard regression

Survival analysis charts include:

- Kaplan-Meier and Nelson-Aalen charts, with option of plotting the survival curves With 95% pointwise confidence intervals
- Cumulative hazard rate chart
- Hazard ratio chart
- Cox regression chart

In addition to curve fitting methods avaiable in the Chart Designer panel, analysis of built-in regression methods* in the Stats Analyzer enables:

- Storing the predicted and residual values in source worksheets
- Testing normality of the regression residuals (when appropriate).
- Storing the cubic spline interpolation output in a source worksheet

* The regression types include: Y on X, linear major axis, linear, reduced major axis, polynomial, exponential, logarithmic, power, cubic spline.

- ROC for a single test
- ROC for two tests on paired samples
- ROC for two tests on unpaired samples

- Polynomial trend surface analysis of XYZ data
- Polynomial trend surface analysis of matrix data

- Multiple regression with numeric predictors
- Multiple regression with categorical predictors

Output from the multiple regression analysis can include:

- The Correlation Matrix of Predictors (specific to analysis with numeric predictors)
- Collinearity diagnostics (specific to analysis with numeric predictors)
- Predicted and residual values
- Multiple regression coefficients and model summary report
- ANOVA for the significance of full model regression
- ANOVA test report for contribution of individual predictors to the model
- Normality tests on regression residuals

The canonical output can include:

- The correlation matrix of observed variables
- Canonical correlation results, including correlation summary and multivariate significance tests
- Eigenvalues
- Canonical coefficients (weights) and structure coefficients
- Canonical scores
- Redundancy analysis
- D'Agostino-Pearson teston observed variables and canonical scores

More Information

Canonical Correlation Analysis
The PCA output can include:

- Correlation or covariance matrix
- Bartlett's test of sphericity
- Partial coefficient matrix
- Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy
- Eigenvalues and PC loadings
- PC scores

More Information

Principal Component Analysis (PCA)
- R-mode factor analysis
- Q-mode factor analysis

The R-mode factor analysis output can include:

- Correlation matrix of the original data
- Bartlett's test of sphericity
- Partial coefficient matrix
- Kaiser-Meyer-Olkin (KMO) measure of sampling adequac
- Reproduced and residual correlation matrices
- Loadings and communalities for extracted factors
- Factor scores

The agglomerative methods in Aabel NG include:

- Single Linkage (Nearest neighbor)
- Complete Linkage (Furthest Neighbor)
- McQuitty's Method (WPGMA)
- UPGMA: Average Linkage Between Groups (Unweighted)
- Gower's Method (Median)
- Ward's method
- Centroid Method

K-means clustering allows analyzing inter-object relationship in multidimensional space, and is used for clustering/grouping data objects with similar characteristics.

- You can define the number of clusters to be generated with the following limitation:
*K*<*n*. - 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.

- Mahalanobis Distance
- Jackknifed Mahalanobis Distance
- Exploratory outlier analysis chart:
- This graph type allows (i) classifying zones that include groups of objects with different range of Mahalanobis distance below the outlier zone, and (ii) generating subsets of data from different zones for further analysis.

The PLS method implemented in Aabel (i) uses principal component analysis (PCA) to derive the prediction functions from factors calculated from cross-product matrices involving both Y and X variables, and (ii) allows predicting one or more dependent variables from a set of independent (predictor variables).

- PLS performed on a single data set (as for multiple regression)
- Using a training data set for predicting unknowns, new events, etc.

Logistic regression allows you to predict a discrete outcome from a set of independent variables that may be continuous, discrete, or binary. The dependent variable is binary/dichotomous/binominal.

The method includes probability and logit (probability), and alllows generating:

- Probability plots with multiple dimension projections
- Probability plots with one independent variable

- LOWESS is an outlier resistant, robust locally weighted regression and scatter smoothing, in which the fitted value of
*x*_{k}is the value of a polynomial fit to the data using weighted least squares, where the weight for (*x*_{i},*y*_{i}) is large if*x*_{i}is close to*x*_{k}and small if*x*_{i}is far from*x*_{k}. - The features and graph types include:
- Exploratory LOWESS Charts
- LOWESS Residual Dependence Graph
- Adding Weighted Confidence Interval (CI) to LOWESS Curves
- Using the LOWESS of Residuals as a Diagnostic Tool*

* The residual plot can provide information for choosing a reasonable value for the local smoothing width).

Parallel coordinates are designed to represent multidimensional information and provide a tool for visualizing multivariate relations.

This graph category provides the following graph types:

- Exploratory parallel coordinates
- Binned density-based parallel coordinates
- Sigma- or CI-based parallel coordinates
- Factorial parallel coordinates

- Exploratory Heatmap with:
- Hierarchical clustering methods
- Principal component analysis (PCA)

- Basic Heatmap allowing:
- Optional scaling of matrix cell-values
- Representing cell values using a circular-shaped or square-shaped frame insert

Shewhart control charts for variables:

*X*bar (*R*) chart*X*bar (*S*) chart*R*chart*S*chart

Other control charts for variables:

- Levey-Jennings chart (quality control of laboratory measurements)
- Individual measurements
- Moving range chart

Shewhart control charts for attributes:

*p*chart*np*chart*c*chart*u*chart

The Westgard multi-rules procedure and Western Electric Company (WECO) warning rules can be optionally applied to the appropriate quality control charts.

- Exploratory outlier analysis chart
- Factor scree plot
- Factor cumulative (%) variance chart

More Information

Data Screening Charts
- Univariate histogram of continuous data
- 2D histogram of continuous data
- Multi-profile histograms

- Categorical histogram
- Pareto chart
- Ogive chart
- Spine chart

- Quantile-quantile Chart
- Normal probability
- Uniform probability

- Single sample frequency dot plots
- Parallel-axes dot plot of paired samples/repeated measures
- Parallel-axes dot plot of unpaired samples

- One-way box & whisker
- Two-way box & whisker
- Three-way box & whisker

Box & whisker chart types include:

- One-way box-percentile
- Two-way box-percentile
- Three-way box-percentile

Box-percentile chart types include:

- One-way violin
- Two-way violin
- Three-way violin
- For interactive, exploratory visualization, you can choose one of the following options:
- Silverman's approach using alpha
- Silverman's approach using IQR
- Percentage of sample range (for this option, you can obtain the AMISE optimal bandwidth estimate (as well as the corresponding scaling factor) in the Stats Analyzer and use the output for providing the user-defined scaling factor)

A violin chart comprises a combination of box plot and density trace; violin chart types include:

Bandwidth Selector Options:

- One-way bar Chart
- Two-way bar chart
- Three-way bar chart
- Error bar options include standard error of mean, standard deviation, confidence interval (90%, 95%, 97.5%, 99%)

More Information

Mean Bar Charts
- Two-Way Interaction Chart (Two-Way Stacked Mean line Plot)
- One-Way Mean line Chart
- Error bar options include standard error of mean, standard deviation, and confidence interval (90%, 95%, 97.5%, 99%)

More Information

Mean line and Interaction Charts
- One-way diamond mean comparison chart
- Two-way diamond mean comparison chart

- Mosaic diagram (observed frequencies)
- Parquet diagram (expected frequencies)
- Mosaic (Goodness-of-Fit Statistics)
- Mosaic Diagram Arranged by Categories
- Parquet (Matrix of Expected Frequencies)
- Chessboard Diagram (Magnifying Missing Data)

The Bland & Altman method for comparing two methods of measurement or two paired variables; the confidence interval display options include:

- ±1.96 standard deviation
- Paired t-test CI (95%)
- Paired t-test CI (99%)

More Information

Bland & Altman Difference Plots

- ArcView shape files and associated dBase files
- Formats provided by the USGS Coastline Extractor:
- Arc/Info Ungenerate
- Mapgen and Matlab
- Splus

- Thematic map of continuous data
- Thematic map of categorical data
- Thematic symbol chart
- Thematic contour of matrix data
- Thematic heatmap
- Thematic XY point frequency chart

- Geographic
- Albers Conical Equal Area
- Azimuthal Equidistant
- Equidistant Conic
- Gnomonic
- Hammer
- Interrupted Goode Homolosine
- Lambert Conformal Conic
- Mercator
- Miller Cylindrical

- Mollweide
- Orthographic
- Polar Stereographic
- Polyconic
- Robinson
- Stereographic
- Transverse Mercator
- Universal Transverse Mercator (UTM)
- Van der Grinten

More Information

Map Projection Utilities and Projection Datum Options

- Wind rose projects a large quantity of data in a simple graphical plot that summarize the occurrence of winds, showing their speed, direction and frequency in a polar coordinate grid.

More Information

Wind Rose Module (Using Raw Wind Data)

- Stereographic scatter
- Stereographic point density contour
- Full circle structural rose with area of the petals proportional to the frequency, symmetric or asymmetric
- Full circle structural rose with length of the petals proportional to the frequency, symmetric or asymmetric
- Half circle structural rose with length or area of petals proportional to the frequency
- Full circle structural lineament, symmetric or asymmetric
- Half circle structural Full circle lineament

- A specialized module for generating n-dimensional spidergrams (geospider diagram) for comparing abundance of REE or multi-elements of an analyzed rock sample normalized to their abundance in type-rocks.
- Aabel is distributed with a plot definitions library that includes 30 spidergram reference plot definitions for representing geochemical data.

- Specialized group-based REE or multi-elements spidergrams include:
- Group-based n-dimensional spidergrams differentiated by data object markers and/or line attributes
- Group-based REE / multi-elements Bands
- The group-based diagrams provide flexibility for exploratory visualization of spidergrams generated from large data sets.

- This diagram is designed to plot genomic sequence alignment by mapping query sequences to target sequences.
- Includes four plot types
- Stepwise X-zooming allows exploring the sequence alignment data

More Information

Sequence Alignment Diagram
- This diagram is designed to permit a combination of column graphs that reflect variation against sorted values (in an increasing order) of a common Y-axis. It is hence ideal for plotting variations against depth, time, or similar variables.
- The automatic sorting of the data will take place in the Visualization & Statistics pipeline, without changing the order they are stored in the data source.
- Each column of a CCG diagram can include either a single variable or multiple variables.

- XY scatter chart with important exploratory features
- XY clustered rays connected mean or median of the groups
- XY convex hulls of groups
- Cross-axes XY scatter
- Scatter series charts, including:
- Scatter series sort by X values
- Scatter series across X categories
- Double-Y scatter series sort by X values

- XY Scatter-line combination charts
*****: - With data objects connected along the sorted values of Y-axis
- With data objects connected along the sorted values of X-axis
- In an XY scatter chart, the data points represent groups of data objects that are plotted in the pipeline order (i.e., the order they are stored in the worksheet)
- In a line chart, the data points represent variables plotted along the sorted values of the Y- or X-axis.
- Combination graph types enable plotting a line chart in which the data points represent groups of data objects.

***** Why Combining XY scatter and Line Chart Properties?

- Multi-profile scatter-line charts:
- Trends of multiple X-variables plotted across the sorted values of a common Y-axis
- Trends of multiple Y-variables plotted across the sorted values of a common X-axis

- Matrix of XY scatter graphs
- Matrix of clustered rays connected mean or median of the groups
- Matrix of convex hulls of groups displaying the group boundaries and the amount of overlap in a multivariate set of data
- Matrix of XY (value-axes) area graphs

- 3D scatter
- 4D scatter (displaying a 4th dimension using a color scale)
- 3D spinning chart (this graph type is suitable for exploratory pattern recognition in multivariate data)

These 3D charts allow continuous rotation (360 degrees).

- Ternary scatter:
- Supporting ternary zooming
- Having ability to draw the plot clockwise or counterclockwise
- Having the option of displaying error regions

- Ternary bubble*

* A ternary chart displaying a 4^{th} dimension whose values are shown by bubbles using a continuous color scale.

- 2D Contour of matrix data* using binned scale
- 2D Contour of matrix data* using continuous scale
- Contour of XYZ data using binned scale, contouring by triangulation

* For generating matrix data from XYZ data, you can use k-nearest or natural neighbor gridder or polynomial trend-surface gridder utilities.

- 3D contoured surface requiring matrix data*
- 3D mesh (wire frame) requiring matrix data*
- 3D XYZ Contour Points, contouring by triangulation

* For generating matrix data from XYZ data, you can use the k-nearest neighbor, natural neighbor, or polynomial trend-surface gridder utility.

- The Voronoi diagram subdivides the X-Y space of the chart into polygons surrounding each data point. These polygons represent the most compact division of space possible.
- Voronoi diagram is particularly suited for studying the area of influence of each point.
- The chart types include:
- Standard Voronoi
- Polygons color coded by values of a third variable
- Group-based voronoi

Waterfall charts are ideal for comparing variations between multiple data sets that are obtained under similar conditions. The chart types include:

- Waterfall line series
- Waterfall area series
- Waterfall pseudo-surface

Spider charts (also known as radar charts) display variations in multiple data series that represent the same factors. The chart types include:

- Stacked area spider with option of using individual, individual normalized, or equal scaling of spokes
- Line spider chart with option of using individual or equal scaling
- Line spider with option of using percentage data or transforming data into 100% values during plotting
- Filled spider chart with option of using individual or equal scaling

Polar charts can be used to display non-directional data series (e.g., data representing speed) against variation in angles. The values of the non-directional data will be plotted proportional to the radius of the polar chart.

- Polar stacked-area/-petal series
- Polar stacked-petal series chart is not a substitute for wind rose diagram. To summarize and represent the wind data, see the Wind Rose Module.

- Polar scatter series
- Polar line series

- Standard bubble chart supporting regression
- 4D Bubble Chart (4
^{th}D Color-Coded) - Group-based bubble chart* supporting regression by group

* The bubbles belong to different groups of data object are differentiated by the source worksheet rows marker colors.

- Line series sort by X values
- Line series across X categories
- Run sequence
- Multiple paired XY line series
- Double-Y line series sort by X values
- Double-Y line series across X categories

- Column and stacked column charts, including:
- Single variable column chart
- Stacked and 100% stacked column charts
- XY (value-axes) column and stacked column charts

- Clustered column chart (side-by-side columns)
- Column-line combination chart*
- Multi-profile column charts (stacks of column charts)

* The combination graphs share the same category axis, but can have the same or separate value axis.

- Bar and stacked bar charts, including:
- Single variable bar chart
- Stacked and 100% stacked bar charts
- XY (value-axes) column and stacked bar charts

- Clustered bar chart (side-by-side bars)
- Bar-line combination chart*

* The combination graphs share the same category axis, but can have the same or separate value axis.

- Area and stacked area charts, including:
- Single variable area chart
- Stacked and 100% stacked area charts
- XY (value-axes) column and stacked area charts

- Area-line combination charts*
- Multi-profile area charts

* The combination graphs share the same category axis, but can have the same or separate value axis.

- 3D column
- 3D area
- 3D band

More Information

3D Column, Area, and Band Charts
- XY scatter pie chart*
- Standard pie chart

* Displaying pies on an XY chart, where the center of each pie has a unique XY coordinate.

- Stacked (univariate) bubble chart
- XY scatter stacked bubble chart*

* Displaying stacked bubbles on an XY chart, where the center of each stacked bubble has a unique XY coordinate.

- Stock charts (candlestick and bar) with the option of displaying Bollinger band
- High-low bar and column charts
- XY (value axes) high-low bar and column charts

- XY vector using h and v vector components
- Matrix (gridded) vector)
- XY vector

Adding error bars to charts allows a graphical display of the statistical probability of errors, the experimental and analytical errors, the data outliers, etc. You can choose to apply the error bars defining them symmetrically or asymmetrically.

Error bars defined for individual variables can be applied to:

- XY scatter chart
- Scatter series charts
- Matrix scatter
- Line series charts
- Column charts
- Bar charts
- Area charts
- Mean bar and interaction charts

Ternary error regions can be applied to:

- Ternary scatter charts

- Linear (X on Y)
- Linear (thru zero)
- Major axis
- Reduced major axis
- Polynomial
- Exponential
- Logarithmic
- Power
- Cubic Spline interpolation

Providing:

- A library of functions
- An interactive interface

The graphic viewer is the Aabel interface for:

- Data visualization & charting
- Exploratory data analysis
- Displaying the output (tables and/or graphs) from statistical analysis
- Accessing different panels for performing analysis and customizing graphics

In a graphic viewer document, you can:

- Use a single page or continuous pages
- Export all pages as PDF in a single document

The graphic viewer data analysis and graphing panels include:

- Stats Analyzer
- Chart Designer
- Graphic Organizer

Data management and customizing panels include:

- Filter panel
- Data Pipeline Controller
- Curve fit and error bars panels
- Axis and Legend panels, etc.

- Aabel NG Stats Analyzer has a modern interface, designed to provide flexibility and ease of use; it allows performing different analyses from the same or different worksheet sources 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 auto-generated worksheets that can be used for follow-up analysis, etc.

- Chart Designer panel from within a single interface provides controls for:
- Selecting and plotting different graph types
- Choosing the required variables and plot options (When modifying variables or/and plot options of an existing graph, the changes will dynamically be updated on the graph)

Graphic Organizer panel displays a list of all graphics of the viewer page in focus, and allows:

- Selecting objects (useful for selecting objects positioned behind other objects on the viewer page)
- Changing the stacking order of objects
- Hiding, locking, or deleting objects
- Modifying size of any object by defining exact dimensions
- Modifying the opacity of fills, lines, and markers of 2D charts

- Applying X-zoom 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
- Alternative means of activation an inactive chart pane

- If you are working with a wide range of data but are only interested in a subset based on specific criteria, the Aabel data filtering tools can be used to limit data to your criteria.
- Aabel data filtering is comparable to database searches; furthermore, the results of data filtering can be dynamically updated on charts currently using the data to which an active filter is applied.

- Worksheet Filter Utility allows creating filter or extracting subsets of data from a single worksheet.
- Graphic viewer Filter Utility provide the following abilities:
- Allows extracting subsets of data from multiple worksheets that are the data members of a Visualization and Statistics Pipeline
- Can be used to filter regions of data according to data patterns
- Can be used for interactive filtering and hence for data exploration

The Visualization & Statistics Pipelines have important user-controlled functions, providing flexibility for:

- Viewing how many pipelines are transmitting data to a viewer
- Viewing which pipeline is the viewer active pipeline
- Viewing the list of worksheet members of viewer data pipelines
- Creating new pipelines in an existing viewer document
- Deleting an inactive pipeline

- The table stylizer is provided for (i) editing the auto-generated tables that are part of the statistical methods output, and (ii) creating tables.
- The text editor allows creating/editing multiple-line text, and provides Rich Text and Unicode support.

- Aabel supports Unicode for storing data in worksheets, for graphing data, and for creating tables 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.

Appearance panel provides controls for applying and modifying background fill (color or gradients) to specific objects, including:

- Adding background to charts contained within the axis lines
- Adding background to objects drawn using tools from the viewer toolbar
- Adding background to any test box or table
- Controls for customizing stroke attributes of selected objects
- Controls for defining an arrowhead for straight lines or polylines

- Aabel graphic viewers use a white background, and drawings of chart axis, ticks and grids, axis labels and titles will initially be black (which can be modified using the controls in the Axis and Legend panels)
- The color theme designer allows creating and storing user-defined color themes, where for each theme you can define:
- A background (solid) color or gradient for the viewer pages
- Colors for different chart elements, text box and table fonts, strokes and fills of graphic objects

- Vector images:
- EPS

- Bitmap formats:
- PNG
- JPEG
- JPEG 2000
- TIFF
- GIF
- BMP

- PDF (the images copied into the clipboard will be PDF)

During export or copying, Aabel honors opacity applied to gradients (whether the opacity of the gradient is uniform or defined differently for each color).

- SPSS.sav files*
- Excel format: 95, 97-2004 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
- ArcView shape files (and associated .dbf and .shx files)
- Arc/Info Ungenerate
- Mapgen
- Matlab
- Splus (2-Column ASCII)

* Sav importer has the option of importing the .sav file labels both for cell values and variable names.

- Continuous numeric
- Nominal
- String (with Unicode support)
- Longitude, Latitude
- Easting, Northing
- Trend
- Dip/Plunge
- Azimuth

- Date formats (pre-defined and custom design):
- Having a range of 500,000 B.C. to 80,000 A.D
- Using a Julian Calendar from January 4713 B.C. to October 1582, and then switching to the present Gregorian Calendar
- Having a resolution of one millisecond from the first of January 4713 B.C. to 80 000 A.D., and one year between 500 000 B.C. to January 4713 B.C.

- Multidimensional data filtering tools for generating subsets of data
- Sorting worksheet data with a single or multiple keys
- Transposing data
- Pivoting and summarizing multidimensional data (cross tabulation)
- Cross-interpolation
- Merging multiple textual data columns into one column
- Merging numeric or textual data without concatenating
- Recoding string values on a one-to-one basis
- Recoding multiple string values to a single value
- Recoding nominal number values to corresonding string values
- Recoding continuous data to categories representing data ranges
- Reordering rows and columns

- Pivoting and summarizing multidimensional data
- Splitting and pivoting raw data
- Stacking data columns
- Cross interpolation
- Assigning markers to groups of data objects using levels of a categorical vatiable
- Generating a categorical variable from unique object markers
- Generating marker codes from object markers (
- Variable finder panel (provided 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)

- Provides 46 pre-defined mathematical and statistical functions
- Includes Boolean/conditional operators
- Provides functions that operate on variables by indices instead of names
- Allows addressing the worksheet cells explicitly
- Provides assign and calculate commands for applying the same formula to multiple columns

- It displays a list of all variables present in the worksheet
- It provides a text area for storing notes such as data source references or other information
- The worksheet notebook further stores important information regarding some of the analysis, data filtering, etc.

More Information

The Worksheet Notebook

- Subtracting a grid Z-values from another (e.g., generating a grid for an isopach map)
- Subtracting a grid Z-values from a user-defined XY plane
- Subtracting a user-defined XY plane from a grid Z-values
- Multiplying a grid Z-values by a constant, e.g., converting the Z-values from feet to meter
- Resampling the displayed grid by user-defined number of grid cells along the X and Y coordinates; e.g., for increasing resolution of a grid for volume or area calculations.

This Open-GL based utility allows exploring single or multiple grid(s) and performing the following data processing, the results of which can be stored in an auto-generated worksheet for further analysis or graphing purposes. The functions include:

- The output corresponding to the user-defined polygon path includes:
- 2D area within the defined path
- Surface area within the defined path
- Positive volume with the defined path (i.e., the volume calculated for the Z-values above zero
- Maximum Z-value within the defined path
- Minimum Z-value within the defined path

This utility allows calculating a number of parameters from a matrix either by applying the calculations to a user-defined polygon path comprising straight segments, or to the total data coverage.

- The output corresponding to all available data in the source worksheet includes:
- 2D area
- Surface area
- Positive volume
- Negative volume
- Maximum Z-value
- Minimum Z-value

- k-Nearest Neighbor and Natural Neighbor Utility allows:
- Creating a continuous surface from an unevenly spaced XYZ data set

- Polynomial Trend-Surface Utility allows:
- Creating trend surfaces from XYZ data to derive a continuous smooth surface from irregular data, or isolate regional trends from local variations

- This utility can be used for extracting a profile from a matrix by:
- (a) Defining a path comprising straight segments
- (b) Generating a worksheet to store the length and the corresponding Z values of the defined profile at control points

- This utility allows:
- Merging polygons into a single polygon allows generating spatial mask data required for plotting thematic contours or thematic heatmaps
- Optimizing polygon data for reducing the file size of worksheets storing polygon coordinate data significantly

- This utility allows:
- Merging data from multiple worksheets
- Generating a new worksheet in which the sequentially merged data are placed

- These utilities include:
- Matrix-to-Matrix Cell-Wise Operator (this must not be confused with matrix arithmetic)
- Matrix Cell-Wise Normalizer

- This utility allows performing variable-based data transformation.
- Transformations include standardize, normalize, logarithmisize (four options), log center, mean center, square, square root, ranking variables individually, ranking variables jointly.

- In addition to the built-in worksheet calculator that allows calculations within a single worksheet, Aabel provides a utility (Trans-Worksheet Calculator).

*Why having two calculator utilities?*

- As outlined below, each calculator utility has different capabilities and limitations, which are specific to requirements of the task to be performed.

- Can be used for calculating formulas across
**multiple**worksheets that are members of a data processing Pipeline. - Will honor the on/off (checked/unchecked) state of the source worksheet object links.
- Can
**not**use variable indices (instead of variable names) for calculations, and can not address worksheet cells explicitly. - Does
**not**provide functions that are dependent on number of objects/cases. - Does
**not**allow assigning the same formula to multiple worksheet columns.

- Can be used for calculating formulas only within a
**given**worksheet. - Can
**not**honor the on/off (checked/unchecked) state of the source worksheet object links. - Can use variable indices (instead of variable names) for calculations, and can address worksheet cells explicitly.
- Provides functions that are dependent on number of objects/cases.
- Allows assigning the same formula to multiple columns.

- Aabel uses RGB and CMYK color models.
- Color picker pane has 256 color swatches, each of which can be customized independently.
- Color picker pane is present in worksheets, the graphic viewer, and all chart customizing panels, allowing to choose or modify colors of markers, lines, fills, contours, color-coded tables, text, etc.
- The conversion between different color models can be based on the System- or user-installed profile.
- The color editor allows creating, customizing, storing, and using color arrays for mapping data ranges to color scales.

For representing data points, the graphic viewer panels provide:

- 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

A wide variety of customizing controls are avaiable, including:

- Tools for customizing legend keys (markers, lines, color or pattern fill)
- Ability to display legends column-wise or row-wise when appropriate to the chart type
- Ability to Modifying legend font size and style
- Choice for using a transparent or colored background for the legend
- Ability to edit legend entries annotation or reordering the entries
- Tools for customizing binned or continuous color scale legends

Axis customizing controls vary contextually depending on the graph category you are are customizing; th Axis panel provides controls for:

- Choice of linear or logarithmic scale, forward or reverse (when applicable)
- Using title proxy names
- Modifying the axis line thickness, color, length and type of major and minor grids
- Modifying the axis range and steps (when applicable)
- Using different label display format settings (fixed or scientific format, decimal places, use of leading zero, use of a prefix or suffix, etc.)
- Changing axis text properties and position of labels
- Customizing axis attributes of plot types that do not share the common axis properties of 2D and 3-D charts (e.g., spider, rose, stereographic, polar, mosaic, dendrograms, etc.)

This feature allow:

- Optional display of object or value labels on charts ((when applicable)
- Displaying a 3rd dimension on an XY chart, a 4th dimension on a ternary, bubble, or 3D scatter chart, using Value labels
- Customizing labels using the Label Stylizer panel controls

More Information

Label Display Examples

Aabel NG is distributed with three high-quality PDF eBooks, which collectively include 1800 pages, and over 1100 illustrations. The eBooks provide flexible navigation controls, complete hot linked cross-referencing throughout each volume, step-by-step instructions for using the diverse features and capabilities of the application, and can produce high quality print output.

- Aabel NG (NG2, v.5): eBook Volume I (885 pages; 518 illustrations):
- Chapter 1. Getting Started
- Chapter 2. Data Pipeline Design of Aabel NG
- Chapter 3. Data Importers and the Importer Document
- Chapter 4. Worksheet Features and Utilities
- Chapter 5. Graphic Viewer Features, Panels and Controls
- Chapter 6. Exporting, Copying, and Importing Graphics
- Chapter 7. Data Visualization and Charting*
- Chapter 8. Map Projection Utilities and Thematic Maps
- Chapter 9. Graphic Organizer
- Chapter 10. Curve Fitting
- Chapter 11. Error Bars
- Chapter 12. Chart Customizing Features
- Chapter 13. Color Management
- Chapter 14. Objects and Attributes
- Chapter 15. Multi-Dimensional Data Filtering
- Chapter 16. Data Exploration Tools

* This chapter covers a wide range of general-purpose and a number of specialized charts.

- Aabel NG (NG2, v.5): eBook Volume II (885 pages; 585 illustrations):
- Chapter 1. The Stats Analyzer General Features
- Chapter 2. Statistics and Multivariate Analysis Modules
- Chapter 3. Univariate & Multivariate Statistical Charts**
- Aabel NG (NG2, v.5): eBook Volume III (101 pages; 56 illustrations):
- Data Processing Utilities
- Aabel NG Quick Start Guide for New Users (47 pages and 40 illustrations):
- A quick start guide and examples for using the Stats Analyzer modules
- A quick start guide and examples for using the Chart Designer panel and creating graphs
- The significance of graphic viewer tools, selections modes, pages, and active chart pane

** This chapter covers a wide range of statistical charts.