Software Downloads for "Nonlinear Regression Line"

NLREG performs nonlinear regression and curve fitting. NLREG fits a mathematical function whose form you specify to a set of data values. Virtually any type of function can be fitted. NLREG includes a programming language similar to C that you use for describing the function to be fitted. Two dimensional line and three dimensional surface plots can be generated. The programming language includes a large selection of built-in library functions (sin, cos, log, exp, etc.) that makes it easy to describe complicated models.

The software system solves constrained nonlinear parameter estimation problems. Synonyms are data fitting, nonlinear regression, parameter identification, curve and surface fitting. The numerical methods are based on highly efficient Gauss-Newton-SQP algorithms. Model functions are defined in a modeling language called PCOMP and are interpreted and evaluated during runtime.

General: Arbitrary fitting criteria depending on parameters to be estimated, additional nonlinear equality or inequality constraints, and optionally on a second independent model variable

Numerical routines: Gauss-Newton-type SQP methods, automatic differentiation, alternative norms (sum of absolute values, maximum of absolute values), confidence intervals for estimated parameters, correlation and covariance matrix, identification of significance...

The system solves constrained nonlinear parameter estimation (nonlinear regression, data, curve and surface fitting) problems based on ODEs, DAEs, PDEs, PDAEs, steady-state systems, Laplace equations, and analytical functions. A statistical analysis provides confidence intervals, correlation and covariance matrix, identification of significant parameters, and allows optimum experimental design. Model equations are defined in a modeling language called PCOMP and are interpreted and evaluated during runtime.

The art of fitting a nonlinear regression model often starts with choosing a model form. This submission is an attempt to teach the reader a simple but general paradigm for their models as a sum of fundamental shapes that are then shifted and scaled to fit the data. I've included a bestiary of fundamental forms, each of which has been plotted. Each form also has a description of some fundamental characteristics, such as limits and other special values. Who might wish to read this submission? Anyone who is interested in fitting an empirical model to their (1-d) data, although many of the ideas in here are applicable to problems in higher dimensions too.

  • Platform: Matlab, Scripts
  • Publisher: John D'Errico
  • Date: 04-04-2013
  • Size: 41 KB

DataFitting is a powerful statistical analysis program that performs linear and nonlinear regression analysis (i.e. curve fitting). DataFitting determines the values of parameters for an equation, whose form you specify, that cause the equation to best fit a set of data values. DataFitting can handle linear, polynomial, exponential, and general nonlinear functions.

DataFitting performs true nonlinear regression analysis, it does not transform the function into a linear form. As a result, it can handle functions that are impossible to linearize such as:

y = (a - c) * exp(-b * x) + c

Quickly Find the Best Equations that Describe Your Data:
DataFitting gives students, teachers, engineers, researchers and other professionals the power to find the ideal model for even the most complex data, by putting...

DataFitting is a powerful statistical analysis program that performs linear and nonlinear regression analysis (i.e. curve fitting). DataFitting determines the values of parameters for an equation, whose form you specify, that cause the equation to best fit a set of data values. DataFitting can handle linear, polynomial, exponential, and general nonlinear functions. DataFitting performs true nonlinear regression analysis, it does not transform the function into a linear form. As a result, it can handle functions that are impossible to linearize such as: y = (a - c) * exp(-b * x) + c Quickly Find the Best Equations that Describe Your Data: DataFitting gives students, teachers, engineers, researchers and other professionals the power to find the ideal model for even the most complex data, by putting a large number of equations at...

LAB Fit is a software for Windows developed for treatment and analysis of experimental data. At LABFit program you are able to: 1) Treat similar data (grouped and ungrouped dataset, one or two samples); 2) Treat non-similar data; 3) Determine propagated error (error propagation up to eight independent variables); 4) Plot 2D and 3D graph (normal, parametric, imported dataset, contour of maps); 5) Execute math calculations (system of linear equations, roots of function, non-linear equation, ordinary differential equation - ODE up to fifth order, system of differential equations - up to five first order ODE, calculator, etc); 6) Extract data (x;y) from a 2D graph (digitizing); 7) Curve Fit (nonlinear regression - least squares method, Levenberg-Marquardt algorithm - with almost 500 functions at the library, with 1 and 2 independent...

EqPlot plots 2D graphs from complex equations. The application comprises algebraic, trigonometric, hyperbolic and transcendental functions. EqPlot can be used to verify the results of nonlinear regression analysis program. Graphically Review Equations: EqPlot gives engineers and researchers the power to graphically review equations, by putting a large number of equations at their fingertips. Up to ten equations could be plotted at the same time, so that intersections and domains could be studied visually.

A pH Calculator, a Virtual Titrator, a Real Titration Data Analyzer, a Distribution Diagram Generator - that's CurTiPot, the all-in-one freeware to learn, teach and work with chemical equilibrium of acids, bases, salts and buffers at home, classroom, interactive "dry lab" or research laboratory. Features of this powerful suite of MS Excel spreadsheets embrace: - pH calculation of any aqueous solution of strong and weak acids, bases, salts, buffers and mixtures of up to 7 polyprotic systems (>40 species), with activity coefficient estimation; - Simulation and overlay of acid-base titration curves of any complexity, with or without random errors in pH and/or volume.

UltimaCalc is a scientific and mathematical calculator designed to occupy minimum screen area, making it immediately available for use. UltimaCalc can stay on top of other windows. Type a calculation as plain text, evaluate it, maybe edit it and re-calculate. Has a comprehensive context-sensitive help system. Calculates to 38 digit precision. The display can be limited to just 8, 12 or 16 digits, and digits grouped for readability. Two 'scientific' view modes show numbers always in exponent format.

  • Platform: Windows
  • Publisher: UltimaCalc
  • Date: 14-06-2006
  • Size: 3112 KB

Implement data analysis and multivariate statistical analysis. 1. Probability analysis. 2. Compute descriptive statistics of selected data - compute probability density function values, cumulative density function values, quantile values, means and variances. 3. Frequency analysis. 4. Compare means- one sample t test, independent-samples t test and paired-samples t test. 5. Compare variances. 6. Variance analysis - one-way ANOVA and two-way ANOVA. 7. Univariate linear regression and multivariate linear regression.

  • Platform: Windows
  • Publisher: GraphNow
  • Date: 01-12-2008
  • Size: 2216 KB

FITTER is a regression Add-In for MS Excel. It estimates any user-defined model that may be entered in ordinary algebraic notation as a set of explicit, implicit and differential equations. FITTER uses the analytic calculations of derivatives and a special optimization algorithm which provides a high accuracy for the nonlinear models. All calculations are performed in a DLL library created using C++, which provides high speed processing. FITTER allows you to include prior knowledge about parameters and accuracy of measurement in addition to experimental data.

  • Platform: Windows
  • Publisher: Polycert Ltd
  • Date: 21-03-2011
  • Size: 605 KB

mex c++ interface to call Eureqa server directly from matlab The code enables symbolic regression with possible user-defined operators (such as +, -, *, /, ^, exp, log, sin, cos, abs, tan). Applications range from a detection of hidden data relationships to nonlinear regression of different kinds to feature selection for machine learning algorithms..

  • Platform: Matlab, Scripts
  • Publisher: Johannes Jenkner
  • Date: 11-02-2013
  • Size: 10 KB

DataFit is a tool used to perform nonlinear regression (curve fitting), statistical analysis and data plotting. What sets DataFit apart from similar curve fitting and regression programs is its ease of use. DataFit is driven by a well-designed graphical interface, so there are no complicated instructions to remember and no programs to write. Data entry is achieved through a standard spreadsheet interface, which supports ASCII and ODBC data source import, as well as cutting and pasting data from the clipboard.

CurveExpert Professional is a cross-platform solution for curve fitting and data analysis. Data can be modelled using a toolbox of linear regression models, nonlinear regression models, smoothing methods, or various kinds of splines. Over 60 models are built-in, but custom regression models may also be defined by the user. Full-featured publication-quality graphing capability allows thorough examination of the curve fit. The process of finding the best fit can be automated by letting CurveExpert compare your data to each model to choose the best curve.

  • Platform: WinOther
  • Publisher: Daniel G. Hyams
  • Date: 25-05-2012
  • Size: 21299 KB

Passing & Bablok described this procedure in the 1983. This procedure is ideal if you want to compare 2 different methods (instrument) which measure the same chemical analyte in the same sample. Classical linear regression method assume that variables X and Y are normal distributed and with a measurement error costant over the range of concentrations. However, in method comparison studies we generally find that the distribution of measurements is not normal and that variance of errors is not costant.

  • Platform: Matlab, Scripts
  • Publisher: Andrea Padoan
  • Date: 22-03-2013
  • Size: 584 KB

MANCOVAN provides a suite of tools for testing for group, group-group interaction, covariate, covariate-covariate interaction, and group-covariate interaction effects in the context of a multivariate response and it does so without using the Statistics Toolbox. Because MANCOVAN represents such a general model, it can be used for ANOVA, ANOVAN, ANCOVA, ANCOVAN, MANOVA, MANOVAN, and MANCOVA as well without loss of power or precision. In addition to MANCOVAN, this suite of tools includes MSTEPWISE for multivariate stepwise regression, MT for t-tests among levels of a group or for the slope of the regression line associated with a covariate, a variety of functions for creating and using custom design matrices, and plenty of examples.

  • Platform: Matlab, Scripts
  • Publisher: William Gruner
  • Date: 08-04-2013
  • Size: 5704 KB

A contour of a 2D region is defined by an ordered set of points where the neighboring elements contain the neighboring points. Such representation can be obtained with many techniques such as boundary tracing and chain codes. (In a simple 2D point set or a curve the points do not have to lie in a specific order.) The contour smoothing is done by projecting all the contour points onto the local regression line. For each point, N neighboring points which lie on the contour are sampled on each side and a local regression line is computed.

  • Platform: Matlab, Scripts
  • Publisher: Tolga Birdal
  • Date: 05-05-2013
  • Size: 10 KB

Model II regression should be used when the two variables in the regression equation are random and subject to error, i.e. not controlled by the researcher. Model I regression using ordinary least squares underestimates the slope of the linear relationship between the variables when they both contain error. According to Sokal and Rohlf (1995), the subject of Model II regression is one on which research and controversy are continuing and definitive recommendations are difficult to make. RMAREGRESS is a Model II procedure.

Model II regression should be used when the two variables in the regression equation are random and subject to error, i.e. not controlled by the researcher. Model I regression using ordinary least squares underestimates the slope of the linear relationship between the variables when they both contain error. According to Sokal and Rohlf (1995), the subject of Model II regression is one on which research and controversy are continuing and definitive recommendations are difficult to make. BARTREGRESS is a Model II procedure.

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