Applied regression analysis and other multivariable methods
Applied regression analysis and other multivariable methods
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The objective of this paper is to apply time series analysis and multiple regression method to ozone data in order to obtain the optimal statistical model for forecasting next day ozone level. The best estimated model is then used to produce one-step ahead point and interval estimates of future values of the ozone series. Ozone data is analyzed using time series analysis, which resulted in an Auto Regressive Moving Average, ARMA (20, 2) with Mean Absolute Percentage Error (MAPE)=42%. Applying multiple regression method and examination of several possible contributing factors, showed that Wind speed, Mixing height where the complex chemical reactions that produce ozone take place, current and predicted next day temperatures and current ozone concentration are influential on the next day ozone concentration levels. Diagnostics tests and statistics including R-square, residual analysis and Durbin-Watson Statistic (DW) were applied in order to select the best fitted model and finally the best prediction model was found using MAPE and Mean Absolute Deviation (MAD) as predictive criteria. Regression analysis of this data, using tomorrow's and today's maximum temperature, today's wind speed and tomorrow's maximum height at 10 am, as explanatory variables results in R-square of 50.7% with MAD=12.423, MAPE=30% and DW=1.66