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Download software tagged by modeling

The most popular program: Quick Slide Show 2.00
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We recommend: Kintecus 6.80
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XLSTAT 2022.4.5 by ADDINSOFT INC
2023-01-21
XLSTAT is a complete data analysis and statistics add-in for Excel. It includes regression (linear, logistic, nonlinear), multivariate data analysis, parametric tests, non parametric tests, ANOVA, ANCOVA, forecasting methods.
XLSTAT 2019.1.2 by ADDINSOFT
2019-03-28
XLSTAT is a complete data analysis and statistics add-in for Excel. It includes regression (linear, logistic, nonlinear), multivariate data analysis, parametric tests, non parametric tests, ANOVA, ANCOVA, forecasting methods.
Kintecus 6.80 by Ianni Consulting
2019-03-14
Modeling software to simulate the chemical kinetics and equilibrium of combustion, nuclear, catalyst reactor (CSTR) and enzyme reactions. Software can also fit/regress any entered parameters against chemical data and perform sensitivity analysis.
CurvFit 6.7 by Optimal Designs Enterprise
2024-03-05
A curve fitting program: Lorentzian, Sine, Exponential & Power series are available models to match your data. A Lorentzian series is recommended for real data especially for multiple peaked data. Another improved productivity example.
CurvFit 6.210 by Optimal Designs Enterprise
2019-05-11
A curve fitting program: Lorentzian, Sine, Exponential & Power series are available models to match your data. A Lorentzian series is recommended for real data especially for multiple peaked data. Another improved productivity example.
NumXL 1.68.7.1 by Spider Financial Corp
2024-03-01
NumXL is a Microsoft Excel add-in for econometrics and financial time series analysis, designed to make financial modeling and time series easier to manage. NumXL allows you to easily make forecasts, back-track and analyze them.
Blob Studio 3.03 by Pixarra Inc
2020-10-07
Focus is on creation of individual objects that you might use in other other art software. Simple streamlined interface for modeling objects and painting them with included brushes.
2024-01-26
EMCD addresses data gaps by employing methods like Multiple Linear Regression, Artificial Neural Networks, and interpolation. It enhances precision through Autocorrelation, identifying optimal lag and incorporating lagged data for modeling.
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