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.
Analytical review based on statistics on good and poor financial performance of LPD in Bangli regency.
This study aims to apply analytical review model to analyze the influence of GCG, accounting conservatism, financial distress models and company size on good and poor financial performance of LPD in Bangli Regency. Ordinal regression analysis is used to perform analytical review, so that obtained the influence and relationship between variables to be considered further audit. Respondents in this study were LPDs in Bangli Regency, which amounted to 159 LPDs of that number 100 LPDs were determined as randomly selected samples. The test results found GCG and company size have a significant effect on both the good and poor financial performance, while the conservatism and financial distress model has no significant effect. The influence of the four variables on the overall financial performance of 58.8%, while the remaining 41.2% influenced by other variables. Size, FDM and accounting conservatism are variables, which are further recommended to be audited.
An empirical study of regression analysis as an analytical procedure
. A newly issued AICPA auditing standard focuses attention on analytical procedures. Regression analysis has been shown to be a useful audit tool and is used to a limited extent in practice. This study compares a univariate regression-based decision rule with that of exponential smoothing. The effect on the regression-based decision method when additional input information is included to develop a multivariate model is also evaluated. Comparisons are accomplished by seeding various error patterns into the audit period data and evaluating the results of the various models. 
 
 
 
The results indicate that the regression-based decision model was at least as efficient and effective as the exponential smoothing-based model. Additional input information into the univariate regression model to develop a multivariate model did improve auditor decisions for some types of accounts but did not significantly affect the number of incorrect rejections and/or acceptances for other types. The multivariate model did improve the achieved precision of the univariate model but still did not reach desired levels. 
 
 
 
Resume. Dans un Auditing Standards Procedures qu'il publiait recemment, l'AICPA se penche sur les procedes analytiques. L'analyse de regression s'est revelee un instrument de verification utile et son emploi dans la pratique est modere, Les auteurs comparent une regle de decision fondee sur une regression comportant une seule variable aleatoire avec celle du lissage exponentiel. L'incidence d'un supplement d'information a l'entree sur la methode de decision fondee sur la regression permet de mettre au point un modele a plusieurs variables aleatoires, que les auteurs evaluent egalement. Les comparaisons sont realisees en introduisant divers scenarios d'erreur dans les donnees de la periode soumise a la verification. 
 
 
 
Les resultats de l'etude indiquent que le modele de decision fonde sur la regression est au moins aussi efficient et efficace que le modele fonde sur le lissage exponentiel. L'introduction d'un supplement d'information dans le modele de regression a une seule variable aleatoire de maniere a creer un modele a plusieurs variables aleatoires a de fait ameliore les decisions du verificateur pour certains types de comptes, mais n'a pas eu d'incidence significative sur le nombre d'erreurs de premiere et de seconde especes pour d'autres types de comptes. La performance du modele a plusieurs variables aleatoires est de fait superieure a celle du modele a une seule variable aleatoire, sans toutefois permettre d'obtenir les niveaux de precision souhaites.