Dummy Variable Regression

This is the local linear version of the kernel smoothing regression function: http://www.mathworks.com/matlabcentral/fil...objectType=FILEThe local linear estimator improves the regression behaviour near the edges of the region over which the data have been collected.

This function generates a vector of triangular distributed continuous random variable. By specifying minimum value(a), maximum value(b), mode(c), and number of variables to be generated(n), the function gives a vector of random variables as output (X). The command syntax is X=trirnd(a,c,b,n).You...

This GUI demo shows how a random variable, X, is transformed to a new random variable, Z, by a function z=f(x). The random variable X can have a uniform probability density function (pdf), a Gaussian pdf, or ?Other pdf? given by a user specified MATLAB expression. A pseudo-random number generator...


Platforms: Matlab

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

This submission demonstrates how to write a level-2 M-file S-function with a variable sample time.The block samples and holds an input signal were the inter-sample period is specified by another input signal (and may vary from time period to time period).Each step size must be positive or an...


Platforms: Matlab

Leave-one-out cross-validation for PLS regression or discriminant analysispls_cv = plscv(x,y,vl,'da')input:x (samples x descriptors) for cross-validationy (samples x variables) for regression or (samples x classes) for discriminant analysis. Classes numbers must be >0.vl (1 x 1) number of latent...


Platforms: Matlab

Forward stepwise model selection algorithm: Variables are sequentially added to the active set of variables. The procedure does not involve any tests of statistical significance of the potential covariates; instead, it produces a ranking that corresponds to the order of variables as they enter...


Platforms: Matlab

Kernel regression is a power full tool for smoothing, image and signal processing, etc. However, it is computationally expensive when it is extented for multivariant cases. The efficiency can be improved by only using neighbors within the effective range arond a regression point. To improve the...


Platforms: Matlab

Functions for fitting least suqares regression problems under nonnegativity constraints. Optimized version of the Lawson & Hanson approach


Platforms: Matlab

Plot data in a figure, and then interactively choose regression area. The result from polyfit is returned.Example:x=1:1:10;y=sin(x);f=figure; plot( x,y );[p,h] = figreg( f, 2 );%fit a second order polynom to selected region in plot


Platforms: Matlab

The main advantages of the Hausdorff regression are that one should not specify the direction of the errors (vertical, horizontal, orthogonal ... with the ordinary least squares) and that it is less sensitive with respect to the square of the distance.This is the original idea. Any contribution...


Platforms: Matlab

This function generates random Bernoulli variables. There is often a need to generated a random variable that has Bernoulli distribution. This function generates Bernoulli random variables 0 and 1.


Platforms: Matlab

I needed a way to visualize a subset of a 2D variable derived from a 2D dataset, _together_ with the original data and in its original coordinates. Since a plain 3D plot decouples all this info (and becomes illegible for data containing very noisy regions) I decided to show the calculated 2D...


Platforms: Matlab

It finds a (near) optimal solution to a variation of the M-TSP (that has a variable number of salesmen) by setting up a GA to search for the shortest route (least distance needed for the salesmen to travel to each city exactly once and return to their starting locations).Each salesman travels to...


Platforms: Matlab

The model relies on the theory of strength of least square curve fitting and auto regression techniques.


Platforms: Matlab

Drupal 7 has a new and efficient way of loading module and theme settings from the database. The new loader requires all settings to be stored in the current (serialized) format.An unfortunate side effect is that any left-over settings from modules that did not clean up when they were...


Platforms: PHP

Math::Symbolic::Variable is a Perl module which implements variables in symbolic calculations. SYNOPSIS use Math::Symbolic::Variable; my $var1 = Math::Symbolic::Variable->new('name'); $var1->value(5); my $var2 = Math::Symbolic::Variable->new('x', 2); my $var3 =...


Platforms: *nix

Powerful but easy-to-use graphing, curve fitting and calculating software. Graphs Cartesian (Y(x) and X(y)), polar and parametric functions, graphs of tables, equations (implicit functions), inequalities and slope fields. Up to 100 graphs in one window. Calculus features: regression analysis,...


Platforms: Windows

The Budget Tool Business Excel provides an effective means to generate a budget for up to five years. Full flexibility allows you to add Revenue, Variable Cost, and Fixed Cost categories and lines to suit any business. Operation is via an easy to use toolbar and function buttons. The budget can...


Platforms: Windows, Other

XLSTAT is a complete analysis and statistics add-in for Excel. It has been developed since 1993. XLSTAT includes more than 240 features in general or field-specific solutions. The use of Excel as an interface makes XLSTAT a user-friendly and highly efficient statistical and multivariate data...


Platforms: Windows, Windows 8, Windows 7, Windows Server