عنوان مقاله [English]
This empirical study has been done with the aim of developing auditing knowledge and the efficiency of its operations when using the statistical analytical procedures.
In this research, eight alternative models have been evaluated, including five regression models, one time-series model (census X-11) and two nonstatistical models (martingale and sub martingale). Both financial and nonfinancial data were collected from a sample of petrochemical companies for the period of March 1998 through March 2001. The information was used to predict sales revenue and production costs in account balances.
According to the results, regression models have better performance for predicting account balances in performing analytical auditing procedures in comparison with other models.
Logarthmic regression has been evaluated as the best statistical analytical procedure. The a foresaid procedure has a constant performance in sample companies of the industry. In performing statistical-analytical procedure, monthly models perform better
than seasonal ones. Pooled models have a better ability for prediction than single company models.
Furthermore, the resuets of this research show incremental benefits of using non-financial variables in performing statistical analytical procedures in auditing.