Linear Regression Analysis of cost effectiveness in risk management
Group 8
Abstract
This research study explores the possibility that relate cost effectiveness to management’s philosophy of controlling the company’s exposure to various property and casualty losses, after adjusting for company effects such as size and industry type. Using data provided by Professor Joan Schmit and are discussed in more detail in the paper, “Cost effectiveness of risk management practices,” Schmit and Roth (1990). The data are from a questionnaire that was sent to 374 risk managers of large U.S.-based organizations. Through primary analysis, we hypnotized that control variable CAP, control variable INDCOST, and control variable CENTRAL are positively associated with cost effectiveness, and control variable ASSUME, control variables SIZELOG, and control variables SOPH are negatively associated with cost effectiveness. By analyzing the qualities and quantities characteristics of the data, utilizing R to build linear regression models and compare them through R squares and residual analysis to decide the best one, and finally use the model to do interpretation and hypothesis tests to support our hypothesis and findings.

Introduction 1. Research Background and Purposes In corporate business world, risk is the main cause of uncertainty in any organization. Thus, companies increasingly focus more on identifying risks and managing them before they even affect the business. The ability to manage risk will help companies act more confidently on cost reductions and future business decisions. Risks can come from various sources, such as uncertainty in financial markets, threats from project, regulatory issues, and credit risk etc. Once risks have been identified and assessed, all techniques to manage the risk fall into one or more of these four major categories: Avoidance