Abstract This article focuses on how regression analysis, coupled with Bayesian statistical procedures, can be used to provide assistance to auditors in selecting accounts for investigation. Auditors characteristically express their opinion on corporate financial statements on the basis of an examination of only a small portion of the underlying data. Often the auditor relies on little more than informed judgment as to the specific data that he should review. In examining some accounts, notably inventory and accounts receivable, statistical sampling methods are sometimes used to select the data to be studied in detail. Frequently however, the auditor must operate with relatively little guidance as to which particular accounts or subaccounts merit comprehensive review. The purpose of this article is to demonstrate how regression analysis, coupled with Bayesian statistical procedures, can be used to provide the auditor with assistance in selecting those accounts for investigation that are most likely to result in significant audit findings.
Deaken et al. (Tue,) studied this question.