Multiple lagged models, not significant

In this study, the application of Artificial Neural Networks (ANN) and Multiple Regression analysis (MR) to forecast long-term seasonal spring rainfall in Victoria, Australia was investigated using lagged El Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) as potential predictors. The use of dual (combined lagged ENSO-IOD) input sets for calibrating and validating ANN and MR Models is proposed to investigate the simultaneous effect of past values of these two major climate modes on long-term spring rainfall prediction. The MR models that did not violate the limits of statistical significance and multicollinearity were selected for future spring rainfall forecast. The ANN was developed in the form of multilayer perceptron using Levenberg-Marquardt algorithm. The errors of the testing sets for ANN models are generally lower compared to multiple regression models. This PDF file contains the non-significant multiple lagged models for this study. These have been generated in SPSS. The document is 58 pages long and contains 23 regression models, each with the following tables: Variables entered/removed; Model summary; ANOVA; Coefficients; Collinearity diagnostics; and Residuals statistics.