A Weighted Multiple Regression Model to Predict Rainfall Patterns: Principal Component Analysis approach | Zendy

Retius Chifurira | Zendy; Delson Chikobvu | Zendy

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A Weighted Multiple Regression Model to Predict Rainfall Patterns: Principal Component Analysis approach

Author(s) -

Retius Chifurira,

Delson Chikobvu

Publication year - 2014

Publication title -

mediterranean journal of social sciences

Language(s) - English

Resource type - Journals

eISSN - 2039-9340

pISSN - 2039-2117

DOI - 10.5901/mjss.2014.v5n7p34

Subject(s) - principal component analysis , heteroscedasticity , statistics , regression analysis , regression , mathematics , econometrics , climatology , geology

In this study, a multiple regression models developed to explain and predict mean annual rainfall in Zimbabwe. Principal component analysis is used to construct orthogonal climatic factors which influence rainfall patterns in Zimbabwe. The aim of the study is to develop a simple but reliable tool to predict annual rainfall one year in advance using Darwin Sea Level Pressure (Darwin SLP) value of a particular month and a component of Southern Oscillation Index (SOI) which is not explained by Darwin SLP. A weighted multiple regression approach is used to control for heteroscedasticity in the error terms. The model developed has a reasonable fit at the 5%statistical significance level can easily be used to predict mean annual rainfall at least a year in advance. DOI: 10.5901/mjss.2014.v5n7p34

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