Software Listing: Linear Regression

The non-linear regression problem (univariate or multivariate) is easily posed using a graphical user interface (GUI) that solves the problem using one of the following solvers: - nlinfit: only univariate problems. - lsqnonlin: can deal with multivariate problems (more than one dependent fitting variable, ydata is a matrix). - patternsearch: this solver is useful to obtain a good start point, before using nlinfit or lsqonolin; this way, the global minimum is determined easier. Data is introduced in the GUI as vector or matrix from the workspace. The model to be fitted must be written in an M-file in vectorized form: ypred = model(x,xdata) ypred is a column vector (univariate problem) or matrix (multivariate problem) with the model response (observations in rows).

• Platform: Matlab, Scripts

Calculates slope and intercept for linear regression of data with errors in X and Y. The errors can be specified as varying point to point, as can the correlation of the errors in X and Y. The uncertainty in the slope and intercept are also estimated. This follows the method in D. York, N. Evensen, M. Martinez, J. Delgado "Unified equations for the slope, intercept, and standard errors of the best straight line" Am. J. Phys. 72 (3) March 2004. The package includes an example and a Monte Carlo simulation verifying the estimated uncertainties. For more info, visit http://blog.nutaksas.com.

• Platform: Matlab, Scripts

Orthogonal Linear Regression in 3D-space by using Principal Components Analysis This is a wrapper function to some pieces of the code from the Statistics Toolbox demo titled "Fitting an Orthogonal Regression Using Principal Components Analysis" (http://www.mathworks.com/products/statisti...thoregdemo.html), which is Copyright by the MathWorks, Inc. Input parameters: - XData: input data block -- x: axis - YData: input data block -- y: axis - ZData: input data block -- z: axis - geometry: type of approximation ('line','plane') - visualization: figure ('on','off') -- default is 'on' - sod: show orthogonal distances ('on','off') -- default is 'on' Return parameters: - Err: error of approximation - sum of orthogonal distances - N: normal vector for plane, direction vector for line - P: point on plane or line in 3D space...

• Publisher: Ivo Petras
• Date: 13-01-2013
• Size: 10 KB
• Platform: Matlab, Scripts
• Statsar
• Price: 495
• The Statsar statistics library allows you to add high-performance statistics calculations to your .NET platform applications. The object-oriented library was designed and implemented by numerical experts with proven expertise in the financial industry. Providing a simple and intuitive object model, the library allows you to rapidly analyze your data by importing familiar data objects such as ADO.NET data tables. A powerful and robust CSV reader is also included with the component, allowing you to work with existing data files. Bind to virtually any data source including standard data objects, arrays, lists or your own object model.

• Platform: Win2000, Windows Server, WinOther

Quantile Regression USAGE: [p,stats]=quantreg(x,y,tau[,order,nboot]); INPUTS: x,y: data that is fitted. (x and y should be columns) Note: that if x is a matrix with several columns then multiple linear regression is used and the "order" argument is not used. tau: quantile used in regression. order: polynomial order. (default=1) nboot: number of bootstrap surrogates used in statistical inference.(default=200) stats is a structure with the following fields: .pse: standard error on p. (not independent) .pboot: the bootstrapped polynomial coefficients. .yfitci: 95% confidence interval on polyval(p,x) Note: uses bootstrap on residuals for statistical inference.

• Platform: Matlab, Scripts
• PIL
• Price: 0.00
• This Simulink library is a collection of blocks that perform Parameter Identification through the most rewarded frequency and time domain linear regression methods. It works in Matlab 5.3.1 as well as in later versions. Main examples are: -) Recursive Least Squares (RLS). -) Simple Windowed Regression (LLS). -) Local Weighted Regression (LWR). -) Fourier Transform Regression (FTR). Two example on Linear and Nonlinear Aircraft Parameter Identification are included in the library. IMPORTANT, all of these blocks REQUIRE SMXL (the Simulink Matrix Library) freely available in the File exchange section of the MATLAB Central website.

• Platform: Matlab, Scripts

This routine calculates the median squared error of a linear function, and can be used with fminsearch as a robust linear regression (see help LinearMedianSquaredError). It should be very easy to extend the example code to handle nonlinear functions and to minimize other error functions (least absolute error, for instance)..

• Platform: Matlab, Scripts

Helps choose a Box-Cox power transformation for a multivariate linear regression. Assume you are looking at the residuals of [b,bint,r] = regress(y,X) and it seems a transformation is in place. Use: boxcoxlm(y,X) to find the best lambda for a Box-Cox power transformation (y^lambda, or log(y) for lambda=0) The function will also plot the Maximum Log-Likelihood as a function of lambda, and a 95% confidence region for the best value of lambda More control can be obtained using: [LambdaHat,LambdaInterval]=boxcoxlm(y,X,PlotLogLike,LambdaValues,alpha) which allows ommiting the plot, a different region or precision, and a different alpha value for the confidence interval.

• Publisher: Hovav Dror
• Date: 26-01-2013
• Size: 10 KB
• Platform: Matlab, Scripts
• mregress
• Performs multiple linear regression. Includes option for setting the y-intercept to zero. Returns the F-statistic, p-value for the F, t-distribution for the coefficients, and covariance matrix for the regression..

• Publisher: Tony Reina
• Date: 04-03-2013
• Size: 10 KB
• Platform: Matlab, Scripts
• Linear Least Squares
• Price: 0.00
• This application calculates the angular and linear coefficients of a linear regression considering the Linear Least Squares methodology..

• Platform: Android 2.x, Android 3.x, Android 4.4, Android 4.x
• Rt-Plot
• Price: 50
• Rt-Plot is a tool to generate Cartesian X/Y-plots from scientific data. You can enter and calculate tabular data. View the changing graphs, including linear and non linear regression, interpolation, differentiation and integration, during entering. Rt-Plot enables you to create plots fast and easily. The options can be changed interactively. A powerful reporting module generates ready to publish documents..

• Publisher: Rt-Science
• Date: 16-05-2003
• Size: 7442 KB
• Platform: WinOther

ESBPCS-Stats is a subset of ESBPCS (ESB Professional Computation Suite) containing Components and Routines for Statistical Analysis and Matrix/Vector Manipulation in Borland Delphi and C++ Builder. This subset is ideal for people who just want the Stats and/or Matrix/Vector parts of ESBPCS, though you can upgrade to the full version at any time. Also includes Components and routines covering Probability Distributions, Linear Regression, Hypothesis Analysis, Equation Solving and more. The subset includes a good collection of Edits, SpinEdits, ComboBoxes, Memos, CheckBoxes, RadioGroups, CheckGroups as well as a huge collection of routines.

• Platform: Win2000, Windows Server, WinOther

Statistical Analysis and Inference Software for Windows covering everything from Average, Mode and Variance through to Hypothesis Analysis, Time Series and Linear Regression. Includes Online Help, Tutorials, Graphs, Summaries, Import/Export, Customisable Interface, Calculator, Live Spell Check, Install/Uninstall and much more. - Single, Dual (paired and unpaired) and Multiple Data Analysis (Multivariate analysis not in Lite Version). - Data can be either for Sample or Population. - Data can be Time Based for Time Series Analysis. - Data can be entered as Raw Data, Grouped Data, or as Summary Data.

• Platform: Win2000, Windows Server, Windows Vista, WinOther
• NMath Matrix
• NMath Matrix is an advanced matrix manipulation library that extends NMath Core to include structured sparse matrix classes (triangular, symmetric, Hermitian, banded, tridiagonal, symmetric banded, and Hermitian banded), factorizations (LU, Bunch-Kaufman, and Cholesky), orthogonal decompositions (QR and SVD), advanced least squares classes (Cholesky, QR, and SVD), and solutions to symmetric, Hermitian, and nonsymmetric eigenvalue problems.Fully compliant with the Microsoft Common Language Specification, all NMath routines are callable from any .NET language, including C# and Visual Basic.NET. For most computations, NMath libraries use proven, highly optimized versions of public domain linear algebra subroutine libraries, such as the BLAS and LAPACK.

• Platform: Win2000, WinOther
• NMath Core
• NMath Core contains foundational classes for object-oriented numerics on the .NET platform. Product features include: Single- and double-precision complex number classes; full-featured vector and matrix classes for single- and double-precision floating point numbers and single- and double-precision complex numbers; flexible indexing using slices and ranges; cubic spline interpolation; extension of standard mathematical functions, such as Cos(), Sqrt(), and Exp(), to work with vectors, matrices, and complex number classes; LU factorization for a matrix, as well as functions for solving linear systems, computing determinants, inverses, and condition numbers; least squares solutions; random number generation from various probability distributions, including the uniform, normal, Poisson, gamma, binomial, exponential, Pareto, and log normal...

• Platform: Win2000, WinOther
• PDL-Stats
• Price: 0.00
• Statistics modules in Perl Data Language, with a quick-start guide for non-PDL people. They make the PDL shell work like R, but with PDL threading (fast automatic iteration) of procedures including t-test, linear regression, and k-means clustering..

• Platform: WinOther
• statsmodels
• Price: 0.00
• Statistical models with python using numpy and scipy. Currently covers linear regression (with ordinary, generalized and weighted least squares), robust linear regression, and generalized linear model..

• Platform: WinOther
• MyRegressionINV
• MYREGRINV: Resolve a calibration problem (inverse regression problem) that is: to estimate mean value and confidence interval of x since y. This function computes a least-square linear regression using the supplied calibration points and then computes the X values for a supplied y observed vector. This routine use MYREGR function. If it is not present on the computer, MyregrINV will try to download it from FEX. References: Sokal R.R. and Rohlf F.J. 2003 BIOMETRY. The Principles and Practice of Statistics in Biological Research (3rd ed., 8th printing, Freeman and Company, New York, XIX + 887 p.

• Platform: Matlab, Scripts
• MyRegrComp
• MYREGCOMP: Compare two linear regression. This function compares two least-square linear regression. Tests are implementes as reported by Stanton A. Glantz book "Primers of biostatistics". This routine uses MYREGR function. If it is not present on the computer, MyregrINV will try to download it from FEX..

• Platform: Matlab, Scripts
• arsoswod
• Comparision of simple linear regression equations without data. As well as the before file arsos.m this procedure is suffice to test the homogeneity of k regression coefficients (Ho: b1 = b2 =...= bk). It do not needs to input data, but the sample statistics as sample size, regression coefficients, means and variances. The variability among the regression coefficients requires the F-statistic. If the null hypothesis is rejected, it can proceeds with a Tukey's q multiple comparision test to determine which of the k slopes differ from which other. If the null hypothesis is not rejected, the file test through a F-statistic whether they have equal elevations.

• Platform: Matlab, Scripts

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