Regression analysis of ozone data

The objective of this paper is to apply multiple regression techniques to ozone data in order to predict next day ozone levels. 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. These variables were then considered as explanatory variables in regression models. Subsequently, diagnostics tests and statistics including R-square residual analysis and Durbin-Watson statistic were applied in order to select the best fitted model and finally the best prediction model was found using mean absolute percentage error (MAPE) and mean absolute deviation (MAD) as predictive criteria.