ndCurveMaster

Data Analysis Software and Curve Fitting Tool for Researchers

Curve fitting is a crucial data analysis technique that models relationships in data for interpretation and prediction across multiple scientific and technological fields. It involves finding the mathematical function, from a simple straight line to complex nonlinear regressions, that best describes the relationship between variables in a dataset. Finding this function manually using only an Excel spreadsheet can be time-consuming and inefficient, as shown in the tutorial at this link. So, for faster and more accurate results with complex multivariate nonlinear regression equations, we have developed our innovative software, ndCurveMaster and ndCurveMaster 2D.

ndCurveMaster: Multivariable Curve Fitting Software

ndCurveMaster supports curve fitting across multiple dimensions (or input variables), from 2D up to an unlimited number of dimensions (nD), and is capable of handling data of any complexity. This flexibility in managing multiple input variables makes it a unique multivariable curve fitting tool on the market. For example, it can automatically derive an equation for a dataset with six input variables (from x1 to x6) and an output variable Y, as shown here: Y = a0 + a1 · exp(x1)-0.5 + a2 · ln(x2)8 + ... + a6 · x65.2 . This precision ensures accurate matching with measured values.

Using heuristic techniques and machine learning algorithms, ndCurveMaster automatically finds the most suitable nonlinear functions for your datasets. It supports various types of curve fitting, including linear, polynomial, and nonlinear, and comes equipped with validation and goodness-of-fit tests such as overfitting and multicollinearity detection (VIF and Pearson matrix). Results can be quickly generated and exported to Excel, Python, C/C++, and Pascal for further analysis.

Unique Features of ndCurveMaster:

  • Automated discovery of optimal nonlinear regression equations.
  • Integration of advanced fitting techniques and algorithms.
  • Comprehensive data analysis tools including ANOVA, p-test, and collinearity detection.
  • Robust tests for overfitting and multicollinearity to ensure accuracy.
  • Rapid generation of high-quality results with extensive export options.

Overall,ndCurveMaster is an indispensable tool for researchers, scientists, or students dealing with complex data sets. A free 9-day trial is available.

Discover all features

Download ndCurveMaster

ndCurveMaster 2D: Curve Fitting Software for Free

Are you searching for free curve fitting software? Try ndCurveMaster 2D, a completely freeware tool that is fully functional without any time restrictions. This version is ideal for simpler datasets with a single input variable. Here is an example of a nonlinear regression equation developed using this software:
Y = a0 + a1 · ln5(x) + a2 · x1/2 + a3 · x1.3 + a4 · ln2(x) + … + an · exp(x)

Download ndCurveMaster 2D for Free

What Our Customers Say

"Very nice tool, simple to use and accurate for post-processing of data sets." - Ramon Francesconi

"Worked exactly as intended." - Lujane A.

"Authentic Amazing Services Timely Reply Thanks Folks." - Muhammad Kaleem Ullah

For more objective user comments on ndCurveMaster, visit the ndCurveMaster user comments page, and for ndCurveMaster 2D, check out the ndCurveMaster 2D user comments page.

Scientific Applications

ndCurveMaster has been employed by researchers in numerous scientific disciplines such as medicine, physics, ecology, finance, and engineering, proving its versatility as a curve fitting tool and data analysis software. Below are some notable scientific works that utilized the software to develop nonlinear regression equations:

Medicine:
strong>ndCurveMaster's impact extends to the medical field, notably in the creation of artificial prostate tissue for the simulation of transurethral resection of the prostate, demonstrating its utility in enhancing diagnostic models and therapeutic strategies through simulation and analysis (Ramien et al., 2022).
Physics:
In physics, the software has been instrumental in light pollution studies, particularly in evaluating the impact of artificial light on major astronomical observatories. This underscores its role in analyzing complex environmental data and contributing to the preservation of night sky quality (Falchi et al., 2022).
Ecology:
The software's application in assessing the flow zone indicators in carbonate reservoirs using NMR echo transforms and open-hole log measurements offers insights into environmental processes and the impact of human activities on natural reservoirs, showcasing its utility in ecological research and conservation efforts (Al-Dousari et al., 2021).
Engineering and Environmental Sciences:
strong>ndCurveMaster has supported advancements in engineering, evidenced by its use in determining design formulas for container ships, which aids in reducing CO2 emissions during operation, and in the evaluation of indirect methods for determining the dynamic modulus of asphalt mixtures. These applications highlight ndCurveMaster's contribution to sustainable engineering practices and its role in addressing environmental challenges (Cepowski & Chorab, 2021; Luis, 2021; Szelangiewicz & Żelazny, 2023).
Maritime Engineering:
The software's role in maritime engineering is further exemplified in studies relating container ship operating parameters to fuel consumption, offering insights into efficient ship operation and environmental sustainability (Cepowski & Drozd, 2023).

Bibliography

  1. Cepowski, T., & Chorab, P. (2021, October). Determination of design formulas for container ships at the preliminary design stage using artificial neural network and multiple nonlinear regression. Ocean Engineering, 238, 109727. Link to Publication
  2. Al-Dousari, M. M., Almudhhi, S., & Garrouch, A. A. (2021, September 16). Predicting the flow zone indicator of carbonate reservoirs using NMR echo transforms, and routine open-hole log measurements: Insights from a field case study spanning extreme micro-structure properties. Journal of Engineering Research. Link to Publication
  3. Cepowski, T., & Drozd, A. (2023, October). Measurement-based relationships between container ship operating parameters and fuel consumption. Applied Energy, 347, 121315. Link to Publication
  4. Falchi, F., Ramos, F., Bará, S., Sanhueza, P., Jaque Arancibia, M., Damke, G., & Cinzano, P. (2022, December 16). Light pollution indicators for all the major astronomical observatories. Monthly Notices of the Royal Astronomical Society, 519(1), 26–33. Link to Publication
  5. Luis, F. P. (2021). Evaluación de métodos indirectos para la determinación de módulo dinámico de mezclas asfálticas. Universidad Del Norte. Link to Publication
  6. Ramien, M. F., Biakop Nana, P. C., Radtke, N. S., Knopf, C., Klein, S., & Damiani, C. (2022, August 1). Artificial Prostate Tissue for the Simulation of the Transurethral Resection of the Prostate. Current Directions in Biomedical Engineering, 8(2), 237–240. Link to Publication
  7. Szelangiewicz, T., & Żelazny, K. (2023, June 20). Reducing CO2 Emissions during the Operation of Unmanned Transport Vessels with Diesel Engines. Energies, 16(12), 4818. Link to Publication