Statistical Quantitative (SQ) rule plays an important and useful role in data mining. Centralized algorithms have been presented for SQ rules mining. However, the algorithms cannot be easily applied to mining SQ rules on distributed data, where privacy of parties becomes great concerns. This paper considers the problem of mining SQ rules without revealing the private information of parties who compute jointly and share distributed data. The issue is an area of Privacy-Preserving Data Mining (PPDM) research. Based on several basic tools for PPDM, including secure sum, secure mean and secure frequent itemsets, this paper presents two algorithms to accomplish privacy-preserving SQ rules mining over horizontally partitioned data. One is to securely compute confidence intervals for testing the significance of rules; the other is to securely discover SQ rules. Besides, the analysis of the correctness, the security and the complexity of our algorithms are provided.