Data & Analytics / Introductory

Payroll Regression Analysis

KPMG MADA Program Team

July 2025

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Summary

This case study is designed to apply the basic concepts of regression analysis to an auditing problem around payroll. Students will use a regression analysis tool of their choice (i.e. Microsoft Excel, Alteryx, R, Python etc.) to determine if there is an association between headcount and payroll expense. Students will investigate and explain any identified structural breaks or anomalies in the data. Students will then review company documents to determine if there is a factor they can consider and incorporate into the analysis to normalize the data, resulting in a stronger association between headcount and payroll expense. Students will also use the updated model to then predict future payroll expense. Students should be familiar with the fundamental principles of regression analysis.

Content

Students will play the role of an auditor recently engaged to conduct the audit of Code Craft Solutions, a mid-sized technology company, specializing in software development and IT consulting services, for the year ended December 31, 2024. Based on risk assessment procedures, there might be an association between the number of employees (headcount) and payroll expense, but students need to confirm this by performing a regression analysis as part of the substantive procedures around payroll expense.

The case study includes payroll expense detail over 36 periods, headcount data, Board of Director meeting minutes, and projected headcount data. Faculty solutions are provided using Microsoft Excel as well as Microsoft Power BI.

The learning objectives of this assignment are as follows:

Demonstrate an understanding of the fundamental principles of regression analysis.
Demonstrate an understanding of how to apply linear regression analysis to auditing.
Use a regression analysis tool to analyze payroll data, applying their knowledge to determine if there is an association between headcount and payroll expense.
Identify any structural breaks or changes in the payroll data and use analytical skills to investigate and explain the reasons behind these anomalies.
Enhance ability to interpret and communicate statistical results.