DataFit

DataFit is a science and engineering tool that simplifies the tasks of data plotting, regression analysis (curve fitting) and statistical analysis. What sets DataFit apart from similar curve fitting and regression programs is its ease of use. With the combination of the intuitive interface, online help and wide range of features, it is a tool that is used effectively by both beginners and experts.

• Intuitive Graphical Interface – most curve fitting and regression software packages take a programming type approach, meaning that you tell the program what to do in a pseudo-code programming language. This approach forces you to remember many commands, and the proper command syntax. With DataFit, the program is driven by a well-designed graphical interface, which makes it much easier to learn and use. Remember, the goal your are striving for is accurate regression analysis, not learning a new proprietary programming language.
• Non-graphical execution – DataFit can also be run non-graphically from other programs. Simply create a configuration template (either on your own or through the interface), save it, and call DataFit from your program. You can choose handshaking options, such as DDE (Dynamic Data Exchange), progress indicators, and message box displays. Macros support parameterization, which allow you to quickly batch process hundreds of datasets in a single run.
• Online help and "tool-tip" text – With context sensitive help and "tool-tip" text, the answers to your questions are always close by. Straightforward data entry – Data can be entered by typing it into a standard spreadsheet interface, by importing it from ASCII files created by other applications, or by cutting and pasting it from other Windows based applications.
• ODBC Database Connectivity – Data can be imported directly from any ODBC (Open Database Connectivity) data sources. These sources include Microsoft Access, dBase, Excel, Oracle, Paradox, Visual FoxPro and SQL Server.
• Multivariate linear or nonlinear regression – DataFit can solve linear and nonlinear regression models with up to 20 independent variables.