<![CDATA[The fact that ordinary least square (OLS) regression analysis of production function is constrained by multicolinearity problem is evident. The multicolinearity inherently exists because in production process certain factors of production are used in relatively fixed proportion. The existence of multicolinearity causes such consequences as the estimation of unknown regression parameters be relatively imprecise and t test leading to the conclusion that parameter values are not significantly different from zero. This study aimed to apply the principal component regression procedure as an alternative way to solve multicollinearity problem on the OLS regression analysis of production function. By the principal component regression, it was found that production factor of land, seed, fertilizer as well as family and hired labour significantly influenced the rice production. The elasticity of production of those factors showed their existance was in the region II of production curve. It suggested that the quantity use of those factors should be increased to get a maximum production of rice. Meanwhile, the quantity of chemical pesticide should be decreasingly used because of overutilization. This fact showed by its elasticity of production which was negative so that it was located in the region III of production curve.]]>
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