Sains Ma1aysiana 25(1): 145-160 (1996)                                                                           Sains Matematik/

                                                                                                                                              Mathematical Sciences

Detection of Influential Observations in

Principle Component Regression

Mokhtar b. Abdullah

Jabatan Statistik

Fakulti Sains Matematik

Universiti Kebangsaan Malaysia

43600 UKM Bangi Selangor D.E. Malaysia

ABSTRACT

Multicollinearity that may exist among explanatory variables in a regression model can make the regression coefficients insignificant and difficult to interpret. Principal component regression (PCR) is an effective way for solving multicollinearity in regression analysis. The existence of multicollinearity mayor may not be induced by the presence of influential observations. This paper discusses some diagnostic methods for identifying influential observations in the PCR. A data set on water quality of New York Rivers was considered to illustrate the methods. 

ABSTRAK

Multikolinearan yang wujud di kalangan pembolehubah penerang dalam model regresi boleh menyebabkan pekali regresi tidak bererti dan sukar untuk ditafsirkan. Regresi komponen utama (PCR) merupakan cara yang berkesan bagi menyelesaikan masalah multikolinearan dalam analisis regresi. Kewujudan multikolinearan mungkin disebabkan oleh data terpencil yang berpengaruh. Kertas ini membincangkan beberapa kaedah pengecaman bagi mengenalpasti data berpengaruh dalam PCR. Data tentang kualiti air di beberapa batang sungai di New York digunakan untuk memperihalkan kaedah pengecaman yang disarankan.

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