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ndCurveMaster for Windows

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Editors’ Review

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ndCurveMaster automatically fits nonlinear regression equations to datasets with unlimited input variables. It uses machine learning and heuristic techniques to discover complex relationships and generate optimal results for analysis.

  • Pros

    • Fits equations with unlimited input variables and their combinations
    • Uses heuristic techniques and machine learning for curve fitting
    • Assesses variable significance with ANOVA and p-test analysis
    • Detects multicollinearity and overfitting with built-in validation tools
    • Exports results to Excel, Python, C/C++, and Pascal

  • Cons

    No Cons available

Icon of program: ndCurveMaster

ndCurveMaster for Windows

Download now

Used ndCurveMaster for Windows?

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Full Specifications

GENERAL
Release
Latest update
Version
8.6
OPERATING SYSTEMS
Platform
Windows
Operating System
  • Windows 7
  • Windows Server
  • Windows 8
  • Windows 10
  • Windows 11
Additional Requirements
None
POPULARITY
Total Downloads
106
Downloads Last Week
1
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Developer’s Description

By SigmaLab
Find optimum equations to describe empirical data.
ndCurveMaster is unique multivariable curve fitting program that automatically fits a set of ideal nonlinear regression equations to your datasets, using an unlimited number of predictors (input variables). For example, when using a dataset with six input variables (x1, x2, ... x6) and output Y, this tool can find optimal multivariable (multidimensional) equation like: Y = a0 + a1 · exp(x1)^-0.5 + a2 · ln(x2)^8 + a3 · exp(x3)^1.5 + a4 · x4^4.1 + a5 · x5^5 + a6 · x6^5.2By utilizing machine learning numerical methods, ndCurveMaster automatically fits the most suitable nonlinear regression functions for your dataset and discovers the relationships between the inputs and output. It offers rapid generation of high quality results that can be effortlessly exported to Excel, Python, C/C++, and Pascal for further analysis.Unique Features of ndCurveMaster:- Discover the best nonlinear regression equation with an unlimited number of input variables and their combinations.- Utilize the latest scientific solutions, including heuristic techniques and machine learning algorithms for curve fitting.- Analyze data and assess the significance of variables using ANOVA, p-test, and collinearity.- Detect multicollinearity and overfitting through test set validation, collinearity analysis, Pearson correlation matrix, and more.- Evaluate result quality using performance criteria.- Achieve rapid generation of high-quality results and significant time savings.
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