TY  - JOUR
T1  - Overcoming the Multicollinearity by Using Principal
Component Regression in Economic Growth Model
AU - Nahar, Julita AU - Purwani, Sri AU - Supian, Sudradjat AU - Khonsa Syahidah, Fatimah 
JO  - Journal of Engineering and Applied Sciences
VL  - 13
IS  - 2
SP  - 286
EP  - 290
PY  - 2018
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2018.286.290
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2018.286.290
KW  - Principal Component Regression (PCR)
KW  -economic growth
KW  -multicollinearity
KW  -prosperity
KW  -assumptions
KW  -emphasis
AB  - Development of a developing country is essentially aimed at improving the welfare and prosperity
of its people. National or regional development puts more emphasis on development in the economic field. In
the implementation of their economic development factors that influence it should be considered. One measure
of the success can be seen from the economic growth. In modeling the economic growth we are often
constrained by models that do not meet the assumptions, one of which is multicollinearity. This occurs because
the data obtained is taken from uncontrollable circumstances. The existence of these cases can cause difficulty
in separating the influence of each independent variable on the response variable, so, we need a method to
solve it. One method that can be used is Principal Component Regression (PCR). PCR is one method that has
been developed to overcome the problem of multicollinearity. PCR is a regression analysis of the variables in
response to the principal components that are not correlated with each other, where each principal component
is a linear combination of all predictor variables.
ER  -