Curvilinear Regression Coefficients

This code implements the 1D polynomial regression method. It uses the least square method for the finding of regression polynomial coefficents. Outputs of the script are polynomial regression coefficients, residuals, the sum of squared errors, the determination index and the graphical comparison... Platforms: Matlab

Performs linear regression using the Least Squares method. Determines the regression coefficients, the generalized correlation coefficient and the standard error of estimate. Does multivariate regression. Displays 2D and 3D plots. Platforms: Windows

This quantity measures how much the entire regression function changes when the i-th observation is deleted. Should be comparable to F_p,n-p: if the 'p-value' of D_i is 50 percent or more, then the i-th point is likely influential: investigate this point further. Cook's distance (D_i) is an... Platforms: Matlab

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... Platforms: Matlab

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... Platforms: Matlab

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... Platforms: Matlab

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... Platforms: Matlab

The bootstrap is a way of estimating the variability of a statistic from a single data set by resampling it independently and with equal probabilities (Monte Carlo resampling). Allows the estimation of measures where the underlying distribution is unknown or where sample sizes are small. Their... Platforms: Matlab

The Multiple Regression Forecasting model provides a solid basis for identifying value drivers and forecasting data. While it utilises a range of commonly employed statistical measures to test the validity of the analysis, results are summarized in text for ease of use. Once relationships have... Platforms: Windows

CurveFitter program performs statistical regression analysis to estimate the values of parameters for linear, multivariate, polynomial, exponential and nonlinear functions. The regression analysis determines the values of the parameters that cause the function to best fit the observed data that... Platforms: Windows, Other

Regression mAKEr project is a simple modular application for data investigation. This is an application to help user (mathematician) to make regression between series of data, draw grpahics, and export them into various formats by means of common graphics packages (i.e., gnuplot, plotutils). It... Platforms: *nix

The Multiple Regression Analysis and Forecasting template enables the confident identification of value drivers and forecasting business plan or scientific data. The multiple regression process utilizes commonly employed statistical measures to test the validity of the analysis and results are... Platforms: Windows, Mac, Windows 8, Windows 7

Sagata Multiple Regression software offers the power of a professional regression package with the ease and comfort of a Microsoft Excel interface.Features include:Qualitative/Categorical Factors - often inputs or factors in model fitting are qualitative or categorical in nature, e.g., the type... Platforms: Windows

The model relies on the theory of strength of least square curve fitting and auto regression techniques. Requirements: - MATLAB 7.5 or higher Platforms: Windows, Mac, *nix, Matlab, BSD Solaris

A = rc2lpc(K):K is vector of reflection coefficients: K = [k1, k2, ..., kp].A is vector of linear prediction coefficients: A = [1, a_p(1), a_p(2),..., a_p(p)]If the second input argument is passed, i.e.,A = rc2lpc(K,'matrix'),then A is the matrix:[1, 0, 0, 0,..............,0; 1,... Platforms: Matlab

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.... Platforms: Matlab

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... Platforms: Matlab

Programs and utility functions using the Symbolic Math Toolbox are provided to analyze vectors and tensors in general curvilinear coordinates. The programs perform the following functions: 1) runcoord plots intersecting coordinate surfaces for typical coordinate systems 2) runmetric computes and... Platforms: Matlab

M-files for unimodality (or monotonically) constrained least squares regression. Platforms: Matlab

INPUTS: x - vector row with 'x' values y - vector row with 'y' values r - expected 'x'-coordinate of break point if r is empty it is calculated during the optimisation p - if p is equal to 1 the fit is plotted OUTPUT: th - estimated paremeters of the regression lines y_1 = th(1) + th(2) * x y_2 =... Platforms: Matlab