a. Analysis of Ligand Binding Data - Competition, saturation and dose-response studies may be analyzed with SigmaPlot Version 7.0 and this macro. Multiple replicate data sets are fit using an equation selected from a list of ten – and you may add your own. Graphical results, EC50 values and a statistical report are produced.
b. Analyzing Dissolution Test Data with SigmaPlot’s Excel Spreadsheet - An Excel worksheet analyzes up to 12 vessel by 6 sample time dissolution test data. Publication quality graphs of the results are simultaneously created.
c. Shelf Life Time Analysis
d. Data Smoothing - Three real-world examples with increasing variability show the usefulness of SigmaPlot’s data smoothing algorithms to visualize the information in the data. [top]
i. Computing Shelf Life Time with SigmaPlot - An exact computation of shelf life time is computed and graph created. Four designs are available – lower specification only, upper specification only, lower and upper specification and degradant analysis.
ii. Validation of the Shelf Life Macro - Confirms accuracy of the Shelf Life Macro
e. Controlled Release Analysis
i. Fitting Controlled Release and Dissolution Data - Five controlled release models for analysis of drug dissolution data are implemented as a SigmaPlot fit library. One or all models may be easily fitted to your data.
ii. Explicit Function Approximation
iii. Create These Functions
iv. Modify Equations in the SigmaPlot Fit Library
f. Global Analysis of Concentration Response Curves - Global curve fitting without data concatenation is demonstrated.
g. Global Curve Fit of Enzyme Kinetics Data - A demonstration of simultaneous fitting of multiple functions to multiple data sets with shared parameters.
h. Global Curve Fitting for Ka and Kd from Sedimentation - A global analysis of ultracentrifuge radial macromolecule concentration gradients.
i. Global Curve Fitting - Dose Response Parallelism
j. Curve Fitting / Regression
k. Piecewise Nonlinear Regression - A four-segment piecewise linear equation is fit to rowwise replicate data.
l. Weight Functions in Nonlinear Regression
m. Parameter Confidence Intervals in Reports
n. Implicit Function Curve Fitting
o. ROC Curves Analysis
p. Standard Curves Analysis