Known as the ‘Scientific Graphing Standard’, SigmaPlot 12 is the easy-to-use scientific graphing and
statistical data analysis software package for scientists and engineers who need to create exact, publicationquality
graphs that best communicates their research results for presentations, technical publications or the
web. With advanced curve fitting, a vector-based programming language, macro capability and over 50
frequently use statistical tests, SigmaPlot has the analytical features necessary to extract the important
information from your research data. Then with over 100 graph types and a user interface which allows
detailed manipulation of every graph object you can create the exact graph to present your results.
SigmaPlot 12 has advisory statistical routines and enhances SigmaPlot into a complete statistical analysis
and technical graphing suite. SigmaPlot’s award-winning interface and intuitive wizard technology guides
users step-by-step through the graph creation and data analysis process. More than 300,000 researchers
have used SigmaPlot because it provides the flexibility to create compelling graphs you simply can't get
from a basic spreadsheet package. Users love the ability to interactively modify any small detail of the final
graph to make it the perfect presentation of their data.
New graph object and property browser. Object selection in the graph is displayed in the object window and the object’s properties displayed in the property window.
Dynamic Update provides immediate graph redraw with property change. No "Ok" or "Apply" button click required.
Property group styles may be copied and applied across like groups thus simplifying property specification.
New window show/hide buttons for improved window real-estate management.>> More
Quick Transforms list in Property Browser Object Window.
Quicker worksheet window scrolling using mouse wheel.
Formatted text in worksheet cells. Text in worksheets can contain super and subscripts, Greek letters, etc. plus they can be also used within tick labels. >> More
Weight variables may now be specified quite generally as functions of the parameters. Therefore these weights will change at each iteration. In special cases, this includes weighting by predicted values, weighting as a function of residuals (for robust regression) and weighting as a function of the parameters.
Parameter covariance matrix and parameter confidence intervals added to nonlinear regression reports.
Implicit function curve fitting gives the ability to compute predicted values that are defined implicitly by the fit model. >> More
Deming regression allows for errors in both x and y variables – a technique used in method comparison.
Normal distribution comparison with graph and statistics for preliminary quality control analysis.
Parallel line analysis to determine if linear regression slopes and intercepts are different.
Bland-Altman graph and statistics for method comparison.
Improvements in post-hoc testing for multi-factor ANOVAs including P-values for Dunnett’s test and more accurate statistics for multiple comparisons in 3-Way ANOVA >> More
