This paper investigates the relationship between the amount of the rules and the performance of the rule-based machine translation system. We keep adding more rules into the system and observe successive changes of the translation quality. Evaluations on translation quality reveal that the more the rules, the better the translation quality. A linear regression analysis shows that a positive linear relationship exists between the translation quality and the amount of the rules. We use this linear model to make prediction and test the prediction with newly developed rules. Experimental results indicate that the linear model effectively predicts the possible performance that the rule-based machine translation system may achieve with more rules added.