In the present work, a new regression method based on wavelet analysis and multivariate analysis is proposed. Referred to as wavelet coefficient regression (WCR), the proposed method can build a statistical model that relates operation profiles with product quality in a batch process. In WCR, selected wavelet coefficients of operation profiles are used as input variables of a statistical model, and thus time-related information such as timing of manipulation can be successfully modeled. In addition, by integrating multivariate analysis and wavelet analysis, WCR can cope with correlation of input variables. As a result, WCR enables us to build an accurate statistical model of a batch process. On the basis of WCR, a data-driven method for improving product quality in a batch process is also proposed. The proposed method can determine operation profiles that can achieve the desired product quality and optimize the operation profiles under a given performance index and various constraints. The usefulness of the proposed WCR and quality improvement method is demonstrated through a case study of lysine production based on a semi-batch fermentation process.