Authors: Elizabeth A. Cudney; Steven M. Corns; Protyusha DasNeogi
Addresses: Engineering Management and Systems Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA ' Engineering Management and Systems Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA ' Engineering Management and Systems Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA
Abstract: This paper compares three methods of predicting the changes in ozone concentration: linear regression, classification and regression tree (CART) analysis, and the T-method. Using linear regression on these results, a linear equation defining the change of the independent variable versus the dependent variables is created. The strength of the relationship is assessed using the R-squared value and adjusted R-squared value. Classification and regression tree analysis uses a tree-building methodology to generate decision rules, using patterns from historical data obtained on both the dependent variable and the independent or 'predictor' variables to create a prediction model. The T-method is used to calculate an overall prediction based on the dynamic signal-to-noise ratio to obtain an overall estimate of the true value of the output for each signal member. It was found that for this nearly directly correlated dataset the T-method performed comparably to linear regression and was a better predictor than the CART method.
Keywords: T-method; prediction modelling; linear regression; classification; regression tree; CART; ozone concentration changes; adjusted R-squared values; dynamic SNR; signal-to-noise ratio.
International Journal of Quality Engineering and Technology, 2013 Vol.3 No.4, pp.332 - 347
Available online: 12 Aug 2013 *Full-text access for editors Access for subscribers Purchase this article Comment on this article