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

Research output: Contribution to journalArticle

Original languageEnglish
JournalInternational Journal of Applied Mathematics & Statistical Sciences ( IJAMSS )
Volume7
Issue number1
Publication statusPublished - 2018

Cite this

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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",
year = "2018",
language = "English",
volume = "7",
journal = "International Journal of Applied Mathematics & Statistical Sciences ( IJAMSS )",
number = "1",

}

TY - JOUR

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

AU - Thupeng, W. M.

AU - Mothupi, T.

AU - Mokgweetsi, B.

AU - Mashabe, Baitshephi

AU - Sediadie, T.

PY - 2018

Y1 - 2018

M3 - Article

VL - 7

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

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

IS - 1

ER -