SAS JMP Statistical Discovery v10.0-RECOiL

SAS JMP Statistical Discovery v10.0-RECOiL | 420 MB

The software product for interactive statistical analysis of the level of Statgraphics, SPSS, Statistica, StatView, SPlus, Minitab focused on small and medium businesses, as well as the individual user. Full integration, as well as the opportunity to work with servers and to run a static procedure implemented in SAS, makes this product a powerful tool of analysis in its market segment. SAS JMP Statistical Discovery - both the product name and a subsidiary of SAS Institute Inc. The acronym is formed from the JMP "John's Mac Program" (author of the John Sall), one of the founders and currently the vice president of SAS Institute Inc., Heading along the way a business unit JMP. Currently, SAS JMP Statistical Discovery is really a competitor SAS for problems with not very large volume of data, but is positioned as a personal tool, in contrast to the corporate system of SAS.

Dynamic data visualization and deep analytics on the desktop. For more than 20 years, statisticians, engineers, data analysts, researchers, marketers and decision makers in almost every industry have relied on JMP to reveal the stories hidden in their data. This visual discovery software from SAS sets itself apart by linking robust statistics with graphics on the desktop, producing visual representations of data that reveal context and insight impossible to see in a table of numbers. JMP allows you to be more efficient, tackle difficult statistical problems and bring your data analysis to a whole new level. Data and information visualization, design of experiments, and statistical modeling techniques from simple to advanced are all within your grasp with this powerful platform. And when you make JMP your analytic hub, you can work with your other favorite tools: SAS, Microsoft Excel and R.

New Features in JMP ?10

JMP 10, our biggest release ever, accelerates exploration and discovery. It offers new ways to visualize and understand your data and share your findings with others. Analysis is faster, especially with large data sets, and you get more options for drag-and-drop graph-building, as well as new tools for analyzing quality and reliability data, designing efficient experiments, interactively comparing models, creating customized applications, and more.

A powerful 32 - or 64-bit application, depending on your hardware, JMP 10 adds features that make the software more dynamic, powerful and intuitive in the following areas:

Graph Builder
User interface improvements make more changes available directly
from the Graph Builder window.
New elements are available: Line of Fit (regression line), Density
Ellipse, Violin Plot, Pie Chart, Shaded Area, Heatmap, Treemap,
Caption Box, Function.
Multiple nested categorical X variables, resulting in nested X axes.
Map shapes maintain aspect ratio.
Map shapes support Asian world map view.
Points and Map regions can be colored by size.
Analyze command directly launches Fit Model.
Improved performance allows processing of even large tables,
with millions of records, in a few seconds.
Bubble Plot
Geographic map data is retained in output when exporting to
Flash (SWF).
Supports color themes.
Includes new shapes and the addition of arrows.
Column Switcher
Use Column Switcher to swap out a column within a report,
allowing you to interchange and reconstitute the analysis with
the new column.
Local Data Filter
Local Data Filter is a contextual data filter embedded within a
report, localized so it doesn't affect the states of other reports or
data tables.

Local Data Filter is a data filter that you can add to many platforms that lets you filter and focus on specific categories without disturbing the original data table.
General Enhancements
Faster throughout, especially with large data.
Graph preference pane includes a preview that lets you visualize
how custom-graphing parameters will look in reports.
Drag-and-drop replacement of variables is available in many
platforms.
300 DPI graphical output options are available.
Ability to compare two data tables, highlighting differences.

Output options are provided for saving graphics with 300 DPI resolution in the. Jpg,. Tif, and. Png file-formats.
New Windows Environment Enhancements
User interface for Microsoft Windows offers improvements for
easier customization.
Better integration with the Windows 7 taskbar.
Nonlinear
New Fit Curve personality fits nonlinear data to a number of models without needing to pre-impute a formula or values.
Includes popular bioassay and pharmacokinetic models for data analysis.
Tests multiple model estimates, parallelism and comparison of fits.
Partial Least Squares
Improved PLS platform with refined graphs and reports.
Study Distance, T-Square, Diagnostics, and Variable Importance Plots.
Allows variable clustering, creates representative variables for groups of closely related variables.

The improved Partial Least Squares (PLS) platform has more refined graphs and reports.
Design of Experiments (DOE)
New Discrete Numeric Factors specify trials at prescribed numeric stops.
Power analysis is computed using contrasts between treatment levels.
Variance inflation factors determine relative variances of factors in reference to a hypothetical orthogonal design.
Evaluate Design command evaluates any table treated as a design. You can change model and alias terms and see the updated diagnostics.
Center points and replicates are available before number of runs is selected.
Control Chart Builder
Build control charts interactively through drag-and-drop process.
Intelligent control chart building based on what chart you want to see.
Nest categories on the fly or append and replace data for exploration of other natural groups.
Use for initial problem solving in process data to visualize where problems are.
Measurement Systems Analysis
Supports Wheeler's EMP approach.
Assess variation in your measurement systems and gauges.
Study parallelism, bias, variance components.
Use the Profiler to study your system, perform trade-off analysis and optimize number of measurements for cost savings.
Reliability Growth
Use Crow-AMSAA analysis of repairable systems.
Analyze mean time between failure (MTBF) and cumulative failure counts for systems with multiple stages.
Use piecewise change-point detection to find a time point where reliability model shifts.
Reliability Forecasting
Use historical warranty repair costs to create warranty forecasts.
Interactively and visually explore combinations of service terms and production volumes to see their impact on forecasted repairs.

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