New features in SigmaPlot 12
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SigmaPlot 12 graphing software from SYSTAT takes you beyond simple spreadsheets to help you show off your work clearly and precisely. Below are som of the new features in SigmaPlot 12.
Microsoft Office Style Ribbons
Ribbons provide context sensitive feature grouping for enhanced functionality and ease of use. These include high resolution button icons. Pressing the Alt key will display Key Tips for those who use keyboard shortcuts instead of the mouse. Also hot keys are displayed in expanded tooltips.
New Property Browser
The new graph object and property browser is shown below. Object selection in the graph is displayed in the upper Page Objects window and the object's properties displayed in the Object Properties window below. The user selects an object on the graph which is then displayed in the Page Objects window and its properties are displayed in the Object Properties list below.
Upon selecting or changing a property the graph updates immediately with no clicking of Ok or Apply required. This feature is called the Dynamic Update. Property group styles may be copied and applied across like groups thus simplifying property specification. New window show/hide/Auto hide buttons are seen in the upper right corner of the windows above. These are used for window real-estate management.
Formatted Text in Worksheet Cells
Text in the worksheet can now be formatted with super and subscript, bold, Greek letters, etc. and can be used in tick labels. Subscripts are shown in the X axis tick labels in the graph below.
Custom Tables in Reports
Tables with predefined styles or user-customized may be placed in the SigmaPlot report. Data in the table may be copy and pasted to or from an Excel worksheet. The control for inserting a pre-defined or custom table is shown below followed by an example table.
New Curve Fitting Features
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Curve Fitting Weight Variables may now be Specified Generally
Weight variables in the curve fitter may now be specified quite generally as functions of the parameters. These weights will change at each iteration of the curve fitter which was not the case in previous versions. As special cases this general specification includes the three new data weighting features:
- weighting by predicted values (a commonly requested and statistically desirable feature)
- weighting as a function of residuals (for robust regression)
- weighting as any function of the parameters (used by those that have measured their experiment errors in detail or know them from other's work)
Weighting by the predicted values is known to result in better curve fit statistics. Robust regression will tend to ignore outlying data values and result in a better fit to the non-outlying data. Some scientists perform replicate measurements to determine what the measurement error distribution is and then create a weighting function. They can then incorporate these errors in the curve fit by using this predetermined weighting function.
Parameter covariance matrix and confidence intervals added to
nonlinear regression reports
Two statistics have been added to the nonlinear regression report: 1) parameter confidence intervals and 2) the parameter covariance matrix. Both can be used to obtain estimates of the error in the parameters of a curve fit.
Implicit Function Curve Fitting
Implicit() can be very useful in curve fitting functions where the equation you want to fit is implicit. An example of this occurs in drug synergy problems where the use of one drug causes the second drug to have a more potent effect. The equations for a simple implicit function curve fit are shown below.
New statistics Features
One-Sample Signed Rank test
This is a new statistic which determines whether the median of a data set is significantly different from a user-specified value. It is used when you want to compare some data to a fixed value and you know the data is not normally distributed. The prior existing parametric one sample t-test is used to determine if the mean value in a column of data was significantly different from a single value entered by the user.
Deming Regression
Deming regression allows for errors in both X and Y variables – a technique for method comparison where the X data is from one method and the y data the other. The Deming regression method basically extends the normal linear regression, where the X values are considered to be error-free, to the case where both X and Y (both methods) have error. Hypotheses can then be tested, slope different from 1.0 for example, to determine if the methods are the same. For example, it might be used to compare two instruments designed to measure the same substance or to compare two algorithmic methods of detecting tumors in images. The graph compares the two methods to determine if they are different or the same. A report shown below gives statistical results.
Normal distribution comparison
For a preliminary quality control analysis the engineer might collect data and quickly look at it assuming it is normally distributed. SigmaPlot generates normal distribution curves for each data set using the mean and standard deviation of the data. By examining the mean and variance (and other statistics) the engineer can quickly determine if a problem exists. The analysis in SigmaPlot produces a graph for visual interpretation and a report for numerical examination. The graph and report for printer tray gap measurements in the four tray corners is shown below. The normal distributions are compared to the limit lines also graphed and to each other. The data statistics are shown in the report.
Parallel line analysis
Parallel line analysis determines if linear regression slopes and intercepts of multiple data sets are significantly different. It is commonly used in the biosciences to determine relative potency (EC50), bioassays for specific coagulation factors and inflammatory lymphokines and for radioimmunoassays for prostaglandins. In the example below the slopes are not different (P > 0.05) but the intercepts are (P < 0.0001). The report is written to describe the results in understandable language.
Bland-Altman method comparison technique
The Bland-Altman graph and statistics is another approach to method comparison. The Bland-Altman method is a plot that shows the difference between the two methods and computes the 95% limits of agreement. If the difference between the limits of agreement is small then the two methods agree and are considered to be the same. The graph on the left directly compares the results of measurements using both methods. The graph on the right plots the difference Y-X versus the mean (Y+X)/2 and uses the limits of agreement technique developed by Bland and Altman to determine if the two methods are the same.
Improvements in post-hoc testing in ANOVA
The P value for Dunnett's test has been added. It is not found in some statistics programs but is now in SigmaPlot. The computation of multiple comparisons in 3 Way ANOVA has been improved to provide increased accuracy.
Enzyme Kinetics Analysis
Many reactions are controlled by enzymes. Therefore enzymes can be used as drugs in certain situations. A way to measure the enzyme's behavior is by way of its reaction rate. Enzyme Kinetics Analysis provides the method to analyze reaction rates. This feature provides the curve fitting and graphing capabilities needed to analyze and present enzyme kinetics data. Import the data, select the type of study, select candidate kinetic models from over 40 different equations and select the interactive graphs to display the results. SigmaPlot's powerful non-linear curve-fitter then fits all selected equations to your data and ranks them by goodness of fit. The selected interactive graphs help demonstrate the kinetic mechanism. The graphs shown below are created by the enzyme kinetics analysis feature and, since most are based on ratios of the measured variables, are very difficult to generate by hand. These are just four of the seven graph types provided.
New Graphing Features
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Enhanced Graphics Features from GDI+
The improved customizable GDI+ graphics software support provides enhanced graphics features:
- Color gradients are available for almost all objects including area fills and lines. You see color gradients in the popular science journals and in poster sessions at trade shows. They are also frequently used in PowerPoint presentations. Though not absolutely required they provide an artistic look. The following graph has a gray background gradient and a color gradient to white for each bar.
- Area objects may be made transparent with the user setting the degree of transparency. Also, areas with color gradients have two controls for both the initial and final color transparencies. As shown below, transparent symbols can be used to show the “mass” of overlapping objects. Also, transparency can be used to show results that lie behind other results. In the graphs below, compare the transparent symbols on the left to the non-transparent on the right to see the histogram “mass” effect of overlapping symbols.
Radar Graph
The radar graph allows plotting and comparison of multiple variables beyond the two or three previously allowed in SigmaPlot. It is not unusual to graph 5 to 20 variables in a radar graph – 35 are shown in the graph below. A new data format was created to allow input of the variable names in a worksheet column. There are six radar subtypes for almost any application: scatter, line, line & scatter, vector, area, line & error band. Two area radar plots of 35 variables are shown below. The areas are transparent (new transparency feature) in order to show all variable values for both plots.
Dot density graph
The dot density graph is a graphical arrangement of data points which is designed to display the density of data. Data is graphed vertically or horizontally as histograms with data points displaced to prevent overlap. Plots look like they have been prepared by an artist with data points either touching or nestled together. Points may be displaced from one another or overlapped to create a grape-bunch effect. SigmaPlot's implementation allows the comparison of multiple dot density plots with additional descriptive plots that include mean lines, median lines, box plots and percentile lines. The graph below shows three dot density plots each with their associated box plot.
This is only a selection of all the news in SigmaPlot 12. Read more about all the news here: http://www.sigmaplot.com/products/sigmaplot/features.php
Detailed information about SigmaPlot
SigmaPlot 11 Brochure
SigmaPlot 11 Statistic User Guide
SigmaPlot 11 User Guide
SigmaPlot 12 Product page
System requirements
Windows 7:
1 GHz 32-bit (x86) or 64-bit (x64) Processor
1 GB of System Memory
100 MB of Available Hard Disk Space
CD-ROM Drive
800x600 SVGA/256 Color Display or better
Internet Explorer Version 6 or better
Windows XP:
500 MegaHertz (MHz) Processor
256 MB of System Memory
100 MB of available Hard Disk Space
CD-ROM Drive
800x600 SVGA/256 Color Display or better
Internet Explorer Version 6 or better
Software requirements
Windows XP, Windows Vista, Windows 7; Internet Explorer 6 or higher
Office 2003 or higher (for Excel Integration, Paste to Powerpoint macro, and Insert Graphs into Word macro)
Add-On Modules for SigmaPlot
Electrophysiology Module
Directly Read Your Electrophysiology Data into SigmaPlot
The Electrophysiology Module is an add-on to SigmaPlot that allows you to directly import your acquired data into SigmaPlot without the need for an additional data acquisition program. The module saves you the time and effort of manually entering data into SigmaPlot so you can start your analysis and graphing right away. Use the easy point-and-click interface to import your data from commonly used file formats including Axon Instruments ABF files, Bruxton Corporation's Acquire format and HEKA electronik's Pulse format. The file formats appear as a convenient drop down list in SigmaPlot.
Ligand Binding Module
Fit your radioligand and dose response studies with replicate data
Choose from over 10 built-in equations, create a new equation or modify and existing equation to gain insight on the number of binding sites, and their affinity and accessibility for various drugs. Automatically generate a plot of mean response versus concentration and include the best-fit lines for each compound.
SigmaPlot 12 License options
- Single user
- Network
- Concurrent (SIMUS) network
- Volume licenses
- Site licenses
Perpetual with maintenance or annual (lease) licenses for all licenseoptions.
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SigmaPlot Demo
Download fully functional 30-day trial software of SigmaPlot 12.
Installation notes
Windows 7 Users
Once you download the trial (SPW_WebInstall.exe) please right click this file and run the application as Administrator.
All other Operating System
Once the file is downloaded, double click on the file to start the installation process.
When asked for, use the serial number 775250001 during the installation process.
Uninstall Notes
From the start menu choose settings, control panel, add/remove programs. Choose SigmaPlot from the list, Click Add/Remove. You will be prompted with message asking: "do you really want to remove...": Select 'Yes'. Windows will automatically uninstall the software
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Support and Resources for SigmaPlot
We provide technical support for all our customers of SigmaPlot. Use the Contact us in the menu (panel to the left), or send an email to support@alfasoft.se. You can also call us at +46 (0) 31 - 60 43 80 to talk to one of our support assistants.
When contacting our support department please describe your problem as detailed as possible. Remember to always inform about the productversion and what operating system you are using (platform and version).
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