Abstract: Modern organizations rapidly accumulate massive data arrays related to HR processes, but making informed managerial decisions requires more than systematization — it necessitates identifying relationships between indicators and assessing their impact on personnel policy effectiveness. Domestic business virtually lacks real practices of using correlation and regression analysis of HR indicators, and there is a shortage of clear methodological procedures for its application. The research objective is to reveal the possibilities of applying correlation and regression analysis of HR indicators in management decision support systems to identify key factors influencing personnel effectiveness. The study employs the method of systematizing scientific sources to generalize theoretical foundations of correlation and regression analysis in HR, the classification method to differentiate HR indicators into dependent and independent variables, the generalization method to formulate a theoretical model, the modeling method to construct schemes for integrating analysis results into decision support systems, and the graphical method for model visualization. The study systematizes dependent and independent HR indicators for correlation and regression analysis: employee retention rate, productivity, recruitment efficiency, internal staff mobility, engagement and satisfaction, as well as influencing factors (compensation, training, corporate culture, workload, etc.). Industry-specific applications of different indicators are identified for IT companies, manufacturing sector, trade, banking, and corporate companies. A theoretical model for universal application of correlation and regression analysis of HR indicators is proposed, including five sequential stages: calculation and differentiation of indicators into dependent and independent; statistical analysis with correlation coefficient calculation and regression model construction; assessment of analyzed indicators’ impact on personnel policy and company performance; industry adaptation of obtained data to organizational specifics; adoption of statistically substantiated managerial decisions. Forms of integrating correlation and regression analysis results into personnel management practice are revealed: analytical reports, scenario forecasting models, recommendations for HR process optimization, and information-analytical dashboards for data visualization. The possibilities of combining correlation and regression analysis with artificial intelligence technologies to create predictive models of HR process development are substantiated. The practical significance of research results lies in their applicability for statistical substantiation of managerial decisions on optimizing personnel training programs, adjusting motivation systems, workforce planning, strategic talent management, and reducing staff turnover risks.