This program takes as input a collection of input variables called “predictors” and a collection of output variables called “targets” which are arranged in a spreadsheet such that each row of the spreadsheet corresponds to a distinct data record. Two spreadsheets with this format are input to this program. One spreadsheet specifies the “training data”, while one spreadsheet specifies the “test data”. The best minimum mean-square error solution is then computed using a pseudo-inverse least squares solution. Prediction errors for both the “training data” and “test data” are reported based upon estimating the parameters of the linear regression model using the training data. Specific details of this implementation can be found by visiting Podcast Episode 13 as well as examining the MATLAB Source code used to generate this executable.
IMPORTANT! This is Windows executable code. In order for this to execute you must have already installed the MATLAB Component Run-Time Library Installer for Windows Version 2011b. If you have already installed this library, then it is not necessary to re-install the library on your machine.
