In an age of Big Data, statistical analysis is becoming an increasingly powerful tool for accountants. Taking a course in introductory statistics will help every accountant improve their efficiency and help their clients make better decisions.
How Accountants Use Statistics
Accountants who perform audits benefit greatly from understanding and using statistical analysis. For example, when conducting a reliability assessment, one of the accountant’s first tasks is to gather evidence. Auditors know that the easiest way to do this is by looking at a portion of the whole, rather than gathering every bit of data available. Statistically representative samples are preferred in this area as they help auditors work more efficiently and objectively.
Accounting standards are front and center when managers determine retirement and other benefits. Accountants set premium adjustments to account for future risk and account for artificial fluctuations in short-term interest rates using statistical models and methods. Recently, accountants and others with the American Benefits Council used historical statistical data to develop policy recommendations to help control defined benefit plans and promote retirement security.
Jacks-of-all-trades, controllers typically work for a single company, overseeing all of its finances including cost analyses, budget reports and forecasting, as well as giving financial analysis and advice to the head of the organization. Having a thorough understanding of the statistical principles used in creating analyses and forecasts, controllers ensure that their organization operates profitably and efficiently.
Accountants use statistics to forecast consumption, earnings, cash flow and book value. Simply put as accounting for the future, forecasting involves an amount of guesswork about the future – and when people guess, they frequently make errors. Having a thorough understanding of the distribution and metrics for evaluating that error, accountants are better able to more efficiently make predictions about the future.
The detectives of the accounting world, forensic accountants use accounting and legal principles to ferret out financial fraud and deceit. With today’s incredibly complicated financial instruments like credit default swaps and collateral debt obligations, forensic accountants need to understand how statistical principles were used to value and anticipate risk in those securitization products.
With foreclosures at near record levels, anticipating and predicting the risks associated with any given loan has never been more important. Mortgage underwriters assess that risk, and, therefore, need to have a thorough understanding of statistics in order to set a premium price that is reasonable for the borrower and profitable for the lender.
Hand in glove with forecasting is risk management. Accountants are frequently required to specify a premium that reflects the risk, or range of error, with any given forecast. Known as the discount rate, accountants often use statistical principles, such as correlation and distribution, to anticipate this risk and account for it when setting a valuation. More recently, accountants are using more sophisticated statistical techniques, such as co-variance and beta models, to limit valuation error.
Statistical Accounting Resources for Professionals
Accountants can find the latest research on applied and pure statistical analysis in accounting from Contemporary Accounting Research and the Journal of Financial and Strategic Decisions.The latest statistical data is available from FedStats.
Statistical Accounting Resources for Students
Would-be accounting students can get started on their exploration of statistics with these free and open introductory statistics material online:
Coursera, which offers courses from top universities like Duke, CalTech, Johns Hopkins, Columbia and Princeton, has a variety of statistics courses including Statistics One and Introduction to Computational Finance and Financial Econometrics. A little more complex, the latter course offers students instruction in computing asset returns, covariance, characteristics of distributions, autocorrelation, descriptive statistics, risk budgeting, hypothesis testing and standard errors of estimates.
The Massachusetts Institute of Technology shares its course materials online at MIT Open Courseware. Among these materials, readers will find the full coursework from MIT’s Introduction to Probability and Statistics course. In addition to text materials, students can download lecture notes and exams. This course covers probability models and distributions, random variables, statistical estimation and testing, confidence intervals and linear regression.
With Udacity’s Introduction to Statistics, students learn to visualize data relationships, estimate, determine probability, examine distribution including the normal distribution and outliers, hypothesis testing and confidence intervals and linear regression.