The following Matlab project contains the source code and Matlab examples used for linear regression with statistics for multiple category data. Takes six column vectors, a description, x label and y label and plots the data and outputs all the statistics (r squared, OLS slope, RMA slope, 95% CI intervals).

The following Matlab project contains the source code and Matlab examples used for outlier test on an analysis of regression based on the externally studentized residual, r-student. . Among the considerations in the use of analysis of regression, outliers or bad values can seriously disturb the least-squares fit.

The following Matlab project contains the source code and Matlab examples used for nonlinear regression shapes. The art of fitting a nonlinear regression model often starts with choosing a model form.

The following Matlab project contains the source code and Matlab examples used for noise variance estimation from a signal vector or array . Some curve fitting or smoothing tools can benefit from knowledge of the noise variance to expect on your data.

The following Matlab project contains the source code and Matlab examples used for nonlinear least square optimization through parameter estimation using the unscented kalman filter. The Kalman filter can be interpreted as a feedback approach to minimize the least equare error.

The following Matlab project contains the source code and Matlab examples used for goodness of fit (modified).  GFIT2 Computes goodness of fit for regression model  USAGE:        [gf] = gfit2(t,y)        [gf] = gfit2(t,y,gFitMeasure)        [gf] = gfit2(t,y,gFitMeasure,options)  INPUT:            t: matrix or vector of target values for regression model            y: matrix or vector of output from regression model.

The following Matlab project contains the source code and Matlab examples used for linear regression with errors in x and y. Calculates slope and intercept for linear regression of data with errors in X and Y.

The following Matlab project contains the source code and Matlab examples used for two phase linear regression model.    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 = th(3) + th(4) * x        r - the estimated break point

The following Matlab project contains the source code and Matlab examples used for non parametric regression using kernels to estimate density function of residuals. . It will first to a simple regression using least squares to get a good start value.

The following Matlab project contains the source code and Matlab examples used for ranged major axis regression. . Model II regression should be used when the two variables in the regression equation are random and subject to error, i.

The following Matlab project contains the source code and Matlab examples used for major axis regression (principal axis regression). . Model II regression should be used when the two variables in the regression equation are random and subject to error, i.

The following Matlab project contains the source code and Matlab examples used for noise estimation from rician noise corrupted images. Estimates the noise standard deviation from an (MRI) image (2D) corrupted with Rician noise based on the skewness of the distribution.

The following Matlab project contains the source code and Matlab examples used for quantreg quantile regression . Quantile Regression   USAGE: [p,stats]=quantreg(x,y,tau[,order,nboot]);      INPUTS:     x,y: data that is fitted.

The following Matlab project contains the source code and Matlab examples used for linear deming regression. [ b sigma2_x x_est y_est stats] = deming(x,y,lambda,alpha) deming() performs a linear Deming regression to find the linear coefficients:                     y = b(1) + b(2)*x under the assumptions that x and y *both* contain measurement error with measurement error variance related as lambda = sigma2_y/sigma2_x (sigma2_x and sigma2_y is the measurement error variance of the x and y variables, respectively).

The following Matlab project contains the source code and Matlab examples used for theil–sen estimator. the Theil–Sen estimator, also known as Sen's slope estimator,slope selection,the single median method, or the Kendall robust line-fit method, is a method for robust linear regression that chooses the median slope among all lines through pairs of two-dimensional sample points.

The following Matlab project contains the source code and Matlab examples used for regression outliers. This function accepts two (vector of) variables for which a bivariate linear regression analysis is meant to be performed, and removes the outliers from both variables.

The following Matlab project contains the source code and Matlab examples used for robust multivariate regression using the student-t distribution . The function mvsregress performs regression on multivariate data using the Student-t distribution. Its usage syntax is similar to that of the Statistics Toolbox function mvregress that does regression with the normal distribution. The contribution includes a user manual.

The following Matlab project contains the source code and Matlab examples used for solution of one or more nonlinear equations in the least squares sense. . The function is an improved version of the function LMFnlsq widely tested on the nonlinear regression, curve fitting and identification problems.

The following Matlab project contains the source code and Matlab examples used for noise variance estimation. Suppose that you have a signal Y (Y can be a time series, a parametric surface or a volumetric data series) corrupted by a Gaussian noise with unknown variance.

The following Matlab project contains the source code and Matlab examples used for negative binomial regression. Performs Negative-Binomial regression. Regression coefficients are updated using IRLS, and the dispersion parameter is estimated via Chi^2 dampening. See Hardin, J.W. and Hilbe, J.M. Generalized Linear Models and Extensions. 3rd Ed. p. 251-254. for more information.

The following Matlab project contains the source code and Matlab examples used for function for multivariate robust linear regression with missing data. . Function for multivariate robust linear regression with missing data.

The following Matlab project contains the source code and Matlab examples used for robust linear regression. [slope,intercept] = RLINFIT(x,y) returns the coefficient estimates (slope and intercept) for a robust linear regression of the responses in y on the predictors in x.

The following Matlab project contains the source code and Matlab examples used for geometric mean regression (reduced major axis regression). . Model II regression should be used when the two variables in the regression equation are random and subject to error, i.

The following Matlab project contains the source code and Matlab examples used for interval prediction of a single value for a geometric mean regression-reduced major axis regression. . Model II regression should be used when the two variables in the regression equation are random and subject to error, i.

The following Matlab project contains the source code and Matlab examples used for robust fit of simple linear regression model (ignoring nans). . Simplest outlier-robust alternative to polyfit(x,y,1)

The following Matlab project contains the source code and Matlab examples used for window utilities. This zip file contains functions related to apodization and symmetric window generation: 1) atomwin - Window based on atomic functions 2) barthewin - Barcilon-Temes window 3) conneswin - Connes window 4) coshwin - Hyperbolic cosine window 5) coswin - Minimum sidelobe cosine window 6) dchebwin - Dolph-Chebyshev window 7) denkwin - Denk window 8) dslepwin - Discrete Prolate Spheroidal Sequences (DPSS) window

The following Matlab project contains the source code and Matlab examples used for find the p value and coefficients for linear regression . Input is x and y output is regression coefficients, degrees og freedom and p-value for slope

The following Matlab project contains the source code and Matlab examples used for linear regression with multiple variables . Use features of house to predict housing prices

The following Matlab project contains the source code and Matlab examples used for multi channel physiological signal estimation (physionet 2010 challenge entry). Combination of gradient adaptive laguerre lattice filters and Kalman filter for the estimation of a missing signal in a multichannel record.

The following Matlab project contains the source code and Matlab examples used for channel estimation using ls and mmse estimators. I do not know why nobody submitted a simulation for the channel estimation using the MMSE. However, I simulated the OFDM system with channel estimation comparison between the LS and the MMSE estimators.