Cost Stickiness Prediction using Regression Analysis

This study explores the phenomenon of cost stickiness and its prediction using regression analysis techniques. Cost stickiness refers to the asymmetrical response of costs to changes in activity levels, where costs tend to increase more rapidly with increases in activity than they decrease with decreases in activity. Utilizing historical financial data, we conducted regression analysis to identify the determinants of cost stickiness and develop predictive models. Our analysis revealed significant relationships between independent variables such as sales revenue, operating expenses, capital expenditures, industry type, economic indicators, and firm size, and cost stickiness. Positive coefficients for sales revenue and capital expenditures suggest that higher levels of these variables lead to increased cost stickiness, while negative coefficients for operating expenses indicate the opposite effect. These findings have important implications for managerial decision-making in cost management and resource allocation. Our study contributes to the understanding of cost stickiness dynamics and provides insights for improving cost management strategies in organizations.

Keywords:

cost stickiness , regression analysis , predictive modeling , financial data , managerial decision-making