See what New Features can SigmaPlot do for you?
Graphing software that makes data visualization easy
Publish your charts and graphs anywhere
SigmaPlot fit your data easily and accurately
Get more statistical capabilities when you combine SigmaStat with SigmaPlot
SigmaPlot Hardware and Software Requirement
Obtain the latest SigmaPlot updates and patches to enhance the effective and efficiency of the software package
STATISTICS

Major test

  • Cox Regression
Minor tests
  • Odds Ratio Statistic
  • Relative Risk Statistic
  • One Sample t-test
  • Shapiro-Wilk Normality Test
New Result Graphs
  • Anova Profile Plots
  • Cox Regression Plots (Cumulative Hazard, Log Log Survival)
24 New Probability Transforms
  • Gamma, Weibull, Cauchy, Error, LogNormal, Exponential, Logistic, LogLogistic
More informative Anova messages

Regression Wizard
  • Linear and nonlinear regressions
  • Over 100 built-in, graphically-illustrated equations
  • Marquardt-Levenberg algorithm with up to 10 independent variables and 25 parameters
  • Define constraints, tolerance, step size and iterations
  • Automatically determines your initial parameters
  • Writes a complete statistical report to your SigmaPlot Notebook
  • Automatically graphs your results on new or existing graphs
  • Edit code so you can customize the SigmaPlot library of functions or create your own
  • Specify the range for the predicted values output by curve-fitter
  • Automatic Linear Regressions
  • Up to 10th order with confidence and prediction intervals and regression statistics
  • Column Statistics Generated Automatically
  • Size, sum, mean, minimum, maximum, standard deviation, standard error, skewness, minimum positive, number of missing values, and 95% & 99% confidence intervals

Automatic Linear Regressions

  • Up to 10th order with confidence and prediction intervals and regression statistics

Column Statistics Generated Automatically

  • Size, sum, mean, minimum, maximum, standard deviation, standard error, skewness, minimum positive, number of missing values, and 95% & 99% confidence intervals
Dynamic Curve Fitting
  • Converged - Those fitsfthat satisfied the convergence criterion.
  • Singular Solutions - Those convergent fitsfwhose covariance matrix is singular.
  • Ill-Conditioned Solutions - Those convergent fits whose covariance matrix is ill-conditioned (to machine precision).
  • Evaluation Failures - Fits that failed to converge due to an evaluation error of the fit equation induced by certain (out of domain) parameter values.
  • Iterations Exceeding - Fits that failed to converge after the iteration limit was reached. This user specified limit is inserted into the brackets above.
  • Inner-Loop Failures - Fits where the Levenberg-Marquardt parameter has increased above a prescribed value when searching for a parameter direction to decrease the residual sum of squares.
Global Curve Fitting
  • Converged - Those fits that satisfied the convergence criterion.
  • Singular Solutions - Those convergent fits whose covariance matrix is singular.
  • Ill-Conditioned Solutions - Those convergent ffits whose covariance matrix is ill-conditioned (to machine precision).
  • Evaluation Failures - Fits that failed to converge due to an evaluation error of the fit equation induced by certain (out of domain) parameter values.
  • Iterations Exceeding - Fits that failed to converge after the iteration limit was reached. This user specified limit is inserted into the brackets above.
  • Inner-Loop Failures - Fits where the Levenberg-Marquardt parameter has increased above a prescribed value when searching for a parameter direction to decrease the residual sum of squares.
New Features added in SigmaPlot 11

 

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