# Outlier test on an analysis of regression based on the externally studentized residual, r-student. in matlab

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.

# Nonlinear regression shapes in matlab

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.

# Rma1multap in matlab

The following Matlab project contains the source code and Matlab examples used for rma1multap. One-sample repeated measures is used to analyze the relationship between the independent variable and dependent variable when:(1) the dependent variable is quantitative in nature and is measured on a level that at least approximates interval characteristics, (2) the independent variable is within-subjects in nature, and (3) the independent variable has three or more levels.

# Orthogonal linear regression in matlab

The following Matlab project contains the source code and Matlab examples used for orthogonal linear regression. LINORTFIT2(X,Y) finds the coefficients of a 1st-order polynomial that best fits the data (X,Y) in an ORTHOGONAL least-squares sense.

# Goodness of fit (modified) in matlab

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.

# One-way analysis of means with heteroscedasticity. in matlab

The following Matlab project contains the source code and Matlab examples used for one-way analysis of means with heteroscedasticity. . Analysis of Means (ANOM) is a statistical procedure for troubleshooting industrial processes and analyzing the results of experimental designs with factors at fixed levels.

# Two phase linear regression model in matlab

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

# Tsls (2sls) in matlab

The following Matlab project contains the source code and Matlab examples used for tsls (2sls). % Original model: y = X*beta + u % We are concerned that a regressor in X % could be endogenous.

# Non parametric regression using kernels to estimate density function of residuals. in matlab

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.

# Solve complex curve fit problem with parameter pooling & stratification by nonlinear least-squares. in matlab

The following Matlab project contains the source code and Matlab examples used for solve complex curve fit problem with parameter pooling & stratification by nonlinear least-squares. . This example solves a complex curve fitting problem that involves parameter pooling and stratification using a nonlinear least-squares approach. This example also takes advantage of some new language features with MATLAB 7.  * Anonymous functions  * Nested functions

# Quantreg quantile regression in matlab

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.

# Resolve a calibration problem that is: to estimate mean value and confidence interval of x since y in matlab

The following Matlab project contains the source code and Matlab examples used for resolve a calibration problem that is: to estimate mean value and confidence interval of x since y . MYREGRINV: Resolve a calibration problem (inverse regression problem) that is: to estimate mean value and confidence interval of x since y.

# Regstats enhanced. robust std.errors; loops on a matrix of responses, 'onlydata' model. in matlab

The following Matlab project contains the source code and Matlab examples used for regstats enhanced. robust std.errors; loops on a matrix of responses, 'onlydata' model. . [ADDITION_1] This enhanced version of regstats has implemented several methods to estimate robust standard errors for coefficients:  - HC0: White robust t statistics (Eicker,1963,1967; Huber,1967; White,1980)  - HC1: With dfe correction (Hinkley,1977)  - HC2: White weighted by 1-h (MacKinnon & White,1985)  - HC3: White weighted by (1-h)^2 (Davidson & MacKinnon,1993)

# Linear deming regression in matlab

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).

# Pls regression or discriminant analysis, with leave one out cross validation and prediction. in matlab

The following Matlab project contains the source code and Matlab examples used for pls regression or discriminant analysis, with leave one out cross validation and prediction.. Leave-one-out cross-validation for PLS regression or discriminant analysis pls_cv = plscv(x,y,vl,'da') input: x (samples x descriptors) for cross-validation y (samples x variables) for regression or   (samples x classes) for discriminant analysis.

# Fitting 2d line to a set of points in matlab

The following Matlab project contains the source code and Matlab examples used for fitting 2d line to a set of points. [a, b, c, fitness] = ORTHO2DLINEFIT(x, y) This function finds the best fitting parameters of a 2D line with 'ax+by+c=0' equation and it returns the value of the sum of the squares of the distances of the n points from the line as fitness score.

# Weighted nonlinear curve fit script with plotter in matlab

The following Matlab project contains the source code and Matlab examples used for weighted nonlinear curve fit script with plotter. This weighted nonlinear fit script fulfills the requirements of physicists, chemists and other quantative scientists, who need a fitting script for the everyday use.

# Regression outliers in matlab

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.

# Johansen's multivariate analysis of variance procedure under heteroscedasticity. in matlab

The following Matlab project contains the source code and Matlab examples used for johansen's multivariate analysis of variance procedure under heteroscedasticity. . The multivariate analysis of variance (MANOVA) is the statistical procedure used to comparing the mean vectors of several multivariate normal populations.

# Robust multivariate regression using the student-t distribution in matlab

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.

# Polynomial curve fitting in matlab

The following Matlab project contains the source code and Matlab examples used for polynomial curve fitting. Polynomial Fitting By Khaled Sharif Description: This function will take two sets of data of equal length and attempt to fit them to polynomials using the polyfit function varying the degree of polynomials from 1 to 100 and choosing the one that gives the least average deviation from the points.

# Solution of one or more nonlinear equations in the least squares sense. in matlab

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.

# Negative binomial regression in matlab

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.

# Function for multivariate robust linear regression with missing data. in matlab

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.

# Ols with newey west and hansen hodrick se in matlab

The following Matlab project contains the source code and Matlab examples used for ols with newey west and hansen hodrick se. % PURPOSE: computes OLS and reports Robust SE, and Newey-West and Hansen-Hodrick adjusted heteroscedastic-serial consistent standard errors.

# Robust linear regression in matlab

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.

# Regression through least square(normal equations) in matlab

The following Matlab project contains the source code and Matlab examples used for regression through least square(normal equations). It can be used for curve fitting

# Robust smoothing for 1 d to n d data (easy version of smoothn) in matlab

The following Matlab project contains the source code and Matlab examples used for robust smoothing for 1 d to n d data (easy version of smoothn). EZSMOOTHN provides a fast, automatized and robust discretized spline smoothing for real or complex data of arbitrary dimension.

# Geometric mean regression (reduced major axis regression). in matlab

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.

# Interval prediction of a single value for a geometric mean regression-reduced major axis regression. in matlab

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.

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