Download ndCurveMaster (Mac) – Download & Review Free

ndCurveMaster is a data analysis tool developed to help you find the best equation that describes a set of empirical data. The utility relies on linear regressions and heuristic techniques when it comes to fitting curves.

Load data sets and rely on the utility to generate suitable equations

To get started, you need to load the data you want to process via the appropriate ndCurveMaster menu. The utility is able to work with EU or UK/USA CSV files: they use different decimal and value separators).

ndCurveMaster detects the included variables and offers you the possibility to select the X and Y variables you want to load to the app. Moreover, the app can also add combinations of X variables and offers you the possibility to set the significance level.

You will be able to see statistical information based on the data within the ndCurveMaster main window, while also being able to review the database or see the automatically generated graphs.

Rely on the built-in techniques to find the best equation models

ndCurveMaster comes with an AutoFit button that evaluates the data and employs linear regression methods and heuristic techniques in order to find the best models that fit your data. In each case, you get to see the coefficients, parameters, or errors.

Note that you get to refine the results by adding or removing equations and then running the AutoFit function again. The models can be exported to CSV files, or you can copy the information directly to the printer.

What’s more, the project comes with an online documentation that takes you through all the major functionalities and also includes video tutorials. In addition, you get to review the collection of equations that are built-into the ndCurveMaster application.

Data analysis tool that tries to fit variables into an optimal equation

ndCurveMaster is able to load an unlimited number of variables from CSV files and applies built-in equations and fitting techniques in an attempt to find a model that best fits the data set.