This note extends the analysis of line-item error taints and error rates originally presented in Johnson, Leitch and Neter [1981] by first considering the distribution of dollar-unit taints, the relevant distribution when simple random sampling is applied to monetary units. Next, empirical evidence on the relation between the taint amount and book amount is presented. Finally, empirical findings on the magnitudes of dollar-unit error rates are provided.

[This note extends the analysis of line-item error taints and error rates originally presented in Johnson, Leitch and Neter [1981] by first considering the distribution of dollar-unit taints, the relevant distribution when simple random sampling is applied to monetary units. Next, empirical evidence on the relation between the taint amount and book amount is presented. Finally, empirical findings on the magnitudes of dollar-unit error rates are provided.]

Auditors require empirical information about the characteristics of errors in audit populations. In this paper, the error characteristics in 55 accounts receivable and 26 inventory audits are examined. First, the error rates present in these audits are analyzed, and the balance between overstatement and understatement errors is examined. The distributions of the error amounts and error taintings are then studied, as well as the relation between error amounts and book amounts. Some major findings are contrary to auditors' expectations, emphasizing the need for replication studies to further explore this area.

This paper presents a statistical sampling approach based on the multinomial distribution for obtaining a bound for either total population understatement or overstatement errors or both. The approach is nonparametric in nature and, unlike most currently used techniques, it has known characteristics so that the auditor is assured of the specified confidence level regardless of the nature of the population and the nature of the error pattern. Results are presented which show the multinomial bound to give tighter bounds than the Stringer bound in all instances stud]ed. The behavior of the multinomial bound is stud[ed with respect to the effects of sample size and error patterns.

A modification of the multinomial bound has been developed which enables the auditor to obtain bounds for substantially larger numbers of errors in audit samples than was possible previously. In this paper, the characteristics of this modified bound are examined and compared with those of the widely used Stringer bound. It is found that the modified multinomial bound is usually considerably tighter than the Stringer bound and should be useful in many audit applications. The confidence level of the modified multinomial bound is also investigated by sampling simulation, and it is found that the level exceeds or is close to the nominal level for all populations studied.

The article presents the authors' reply to comments on lognormal Cost-Volume-Profit (CVP) model. CVP model allows for dependent relationships among the input variables, large coefficients of variation and it permits a rigorous derivation of the distribution of the output random variable, profit. Authors feel that the model is an attractive and justifiable alternative to the normal model proposed by researchers R.K. Jaedicke and A.A. Robichek. The model is based on assumptions that quantity and contribution margin are lognormally distributed random variables and fixed costs are deterministic. Authors state that the richness of the model is reduced considerably due to the grouping of price and variable cost and the deterministic assumptions for fixed cost. Authors' second comment pertains to the desirability and intuitiveness of the lognormal assumptions. They discuss the relationship between the coefficient of variation and skewness, deriving the mathematical relationship in an equation.

Presents a study which developed a procedure for approximating the distribution of profit in a Cost-Volume-Profit (CVP) model using a log normal approach. Overview of the Jaedicke-Robichek model; Log normal model for CVP analysis; Comparison of normal and log normal models.