A Principal Component Regression Model, for Forecasting Daily Peak Ambient Ground Level Ozone Concentrations, in the Presence of Multicollinearity Amongst Precursor Air Pollutants and Local Meteorological Conditions: A Case Study of Maun

W. M. Thupeng; T. Mothupi; B. Mokgweetsi; Baitshephi Mashabe; T. Sediadie

A Principal Component Regression Model, for Forecasting Daily Peak Ambient Ground Level Ozone Concentrations, in the Presence of Multicollinearity Amongst Precursor Air Pollutants and Local Meteorological Conditions: A Case Study of Maun

W. M. Thupeng
, T. Mothupi
, B. Mokgweetsi
, Baitshephi Mashabe
, T. Sediadie

Mathematics & Statistical Sciences

Research output: Contribution to journal › Article › peer-review

Original language	English
Journal	International Journal of Applied Mathematics & Statistical Sciences ( IJAMSS )
Volume	7
Issue number	1
Publication status	Published - 2018

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https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3128175

Cite this

Thupeng, W. M., Mothupi, T., Mokgweetsi, B., Mashabe, B., & Sediadie, T. (2018). A Principal Component Regression Model, for Forecasting Daily Peak Ambient Ground Level Ozone Concentrations, in the Presence of Multicollinearity Amongst Precursor Air Pollutants and Local Meteorological Conditions: A Case Study of Maun. International Journal of Applied Mathematics & Statistical Sciences ( IJAMSS ), 7(1). https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3128175

Thupeng, W. M. ; Mothupi, T. ; Mokgweetsi, B. et al. / A Principal Component Regression Model, for Forecasting Daily Peak Ambient Ground Level Ozone Concentrations, in the Presence of Multicollinearity Amongst Precursor Air Pollutants and Local Meteorological Conditions: A Case Study of Maun. In: International Journal of Applied Mathematics & Statistical Sciences ( IJAMSS ). 2018 ; Vol. 7, No. 1.

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title = "A Principal Component Regression Model, for Forecasting Daily Peak Ambient Ground Level Ozone Concentrations, in the Presence of Multicollinearity Amongst Precursor Air Pollutants and Local Meteorological Conditions: A Case Study of Maun",

author = "Thupeng, \{W. M.\} and T. Mothupi and B. Mokgweetsi and Baitshephi Mashabe and T. Sediadie",

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Thupeng, WM, Mothupi, T, Mokgweetsi, B, Mashabe, B & Sediadie, T 2018, 'A Principal Component Regression Model, for Forecasting Daily Peak Ambient Ground Level Ozone Concentrations, in the Presence of Multicollinearity Amongst Precursor Air Pollutants and Local Meteorological Conditions: A Case Study of Maun', International Journal of Applied Mathematics & Statistical Sciences ( IJAMSS ), vol. 7, no. 1. <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3128175>

A Principal Component Regression Model, for Forecasting Daily Peak Ambient Ground Level Ozone Concentrations, in the Presence of Multicollinearity Amongst Precursor Air Pollutants and Local Meteorological Conditions: A Case Study of Maun. / Thupeng, W. M.; Mothupi, T.; Mokgweetsi, B. et al.
In: International Journal of Applied Mathematics & Statistical Sciences ( IJAMSS ), Vol. 7, No. 1, 2018.

Research output: Contribution to journal › Article › peer-review

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T1 - A Principal Component Regression Model, for Forecasting Daily Peak Ambient Ground Level Ozone Concentrations, in the Presence of Multicollinearity Amongst Precursor Air Pollutants and Local Meteorological Conditions: A Case Study of Maun

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AU - Mothupi, T.

AU - Mokgweetsi, B.

AU - Mashabe, Baitshephi

AU - Sediadie, T.

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JO - International Journal of Applied Mathematics & Statistical Sciences ( IJAMSS )

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Thupeng WM, Mothupi T, Mokgweetsi B, Mashabe B, Sediadie T. A Principal Component Regression Model, for Forecasting Daily Peak Ambient Ground Level Ozone Concentrations, in the Presence of Multicollinearity Amongst Precursor Air Pollutants and Local Meteorological Conditions: A Case Study of Maun. International Journal of Applied Mathematics & Statistical Sciences ( IJAMSS ). 2018;7(1).