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Regression Analysis of Ozone Data

Abdollahian, M. and Foroughi, Roya (2005):
Regression Analysis of Ozone Data.
IEEE Computer Society, Los Alamitos, Calif., In: IEEE International Conference on Information Technology: Coding and Computing 2005. Proceedings Vol. I, pp. 174-178, [Conference or Workshop Item]

Abstract

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.

Item Type: Conference or Workshop Item
Erschienen: 2005
Creators: Abdollahian, M. and Foroughi, Roya
Title: Regression Analysis of Ozone Data
Language: English
Abstract:

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.

Publisher: IEEE Computer Society, Los Alamitos, Calif.
Uncontrolled Keywords: Regression analysis, Statistics, Forecasting theory
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Event Title: IEEE International Conference on Information Technology: Coding and Computing 2005. Proceedings Vol. I
Date Deposited: 16 Apr 2018 09:04
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