Predicting Cost Stickiness in Auditing, accounting and Finance through Regression Analysis

Cost stickiness, where costs increase more readily than they decrease in response to changes in sales volume, presents significant challenges for financial forecasting, budgeting, and decision-making. This study employs regression analysis to predict cost stickiness, providing valuable insights for auditors and financial managers. The research begins with a comprehensive overview of cost stickiness, followed by a review of existing literature on the subject. A detailed methodology outlines the use of regression analysis, including data collection, variable selection, and model development. A case study is conducted using a real-world dataset to demonstrate the practical application of the regression model in predicting cost stickiness. The results highlight significant predictors of cost stickiness and their implications for auditing and finance, including risk assessment, strategic planning, and improved budgeting and forecasting accuracy. The study concludes with a summary of findings, practical recommendations, and suggestions for future research, emphasizing the importance of understanding and predicting cost stickiness in financial management.

Keywords:

cost stickiness , regression analysis , predictive modeling , financial data , managerial decision-making , Predictive Modeling and Financial Reporting.