Regression analysis for equipment auditing
Purpose - To develop, test and implement a sampling strategy for equipment auditing for a Design/methodology/approach - Regression analysis is applied to auditing of equipment for a large US corporation. Empirical data and test data sets are used to evaluate the efficacy of using regression for auditing and to determine reasonable and efficient sample sizes to be employed across more than 5,000 locations. Findings - Regression is a viable and useful method for equipment auditing when there is anticipated high correlation between pre- and post-audit equipment value. Recommended sample size is dependent upon the size of the location as measured by total pieces of equipment. Decision rules combining acceptable tolerance limits, desired confidence level and sample size are provided. Research limitations/implications - The method, recommended sample sizes and decision rules are particularly applicable to instances where high correlation is expected between pre- and post-audit equipment values. Standard regression assumptions are not all met in all instances, especially with small sample sizes. Practical implications - The regression approach and model, sample size recommendations and decision rules for passing or failing an equipment audit described herein have been implemented at a Originality/value - This paper provides a practical and useful regression-based approach to sampling for equipment auditing. Recommended sample sizes and decision rules for passing or failing the audit are explicitly defined.
Regression as an Analytical Procedure: Are Adjustments for Autocorrelation Really Necessary?
Auditors who use regression analysis when performing analytical procedures may be exposed to the problem of autocorrelation, especially if small-interval time-series data are used. The existence of autocorrelation may bias the standard error of the estimate, which is used in calculating the upper precision limit. This limit determines the acceptance or rejection of an account balance as not materially misstated. Using real-world data and the STAR decision rule, the results of this study suggest that transforming autocorrelated models increases effectiveness (capability of detecting material errors) only slightly, if at all. The results, however, did indicate that efficiency may increase significantly (reduced Type I errors), and that the more severe the autocorrelation, the more likely that efficiency will increase. Therefore, auditors have the most to gain in terms of audit-time savings when models are more severely affected by autocorrelation since fewer incorrect rejections requiring unnecessary additional audit attention would occur with successful transformation.
Implementation of Tourism Satellite Account: Assessing the Contribution of Tourism to the Croatian Economy
Compiling the indicators of tourism consumption and the output of tourism industries, Tourism Satellite Account (TSA) provides a framework for internationally harmonized measurements of direct macroeconomic contribution of tourism. Ivandic and Marusic provide a detailed methodological approach for the transfer of TSA theoretical concepts to the assessment of tourism economic contribution on the case of the Croatia. Giving that the TSA compilation is still characterized by a surprisingly wide range of ‘styles’ used, the chapter contributes to a better understanding of the issues related to measurement of direct tourism impacts. It also gives a set of baseline criteria for resource allocation within tourism policy and the further evaluation of its effectiveness.