Accounting Analytics Exam 2 flashcards [ch 5-8]

What type of analysis is the focus of Chapter 8 in the AMPS model?

Predictive Analytics

What does predictive analytics address?

"Will it happen in the future?" by assessing likelihoods and probabilities.

What are the three broad categories of predictive analytics?

Classification, regression, and forecasting using time series analytics.

How is classification used in predictive analytics?

It assigns data into predefined categories, such as high-risk vs. low-risk customers.

What is an example of classification in accounting?

Identifying whether a transaction is fraudulent or not based on historical data.

How does regression help in predictive analytics?

It estimates relationships between variables to predict numerical outcomes.

What is an example of regression analysis in accounting?

Predicting future sales revenue based on past advertising expenses.

What is the difference between classification and regression?

Classification predicts categories (e.g., yes/no), while regression predicts continuous values (e.g., sales figures).

What is time series analysis?

A predictive technique that examines historical data to identify trends and make future forecasts.

What are examples of time series analysis in accounting?

Forecasting future stock prices, sales trends, or seasonal revenue fluctuations.

What is a common tool used for predictive analytics in Excel?

The Data Analysis Toolpak for regression and forecasting models.

What are some machine learning techniques used in predictive analytics?

Decision trees, neural networks, and support vector machines.

What is overfitting in predictive analytics?

A model that is too complex and fits the training data well but does not generalize to new data.

What is the purpose of training and testing datasets in predictive modeling?

To ensure a model learns from historical data (training) and performs well on unseen data (testing).

How can predictive analytics help in fraud detection?

By identifying patterns in financial transactions that indicate fraudulent activity.

What is an example of predictive analytics in auditing?

Using historical data to predict which accounts are most likely to have misstatements.

Why is predictive analytics important in financial decision-making?

It helps businesses anticipate risks, optimize investments, and improve strategic planning.

How does sentiment analysis relate to predictive analytics?

It analyzes customer reviews or market sentiment to predict stock price movements or sales trends.

What is a key takeaway about predictive analytics?

It leverages historical data and statistical techniques to make informed predictions about future events.

T/F

Increase in receivables as compared to last period is a factor predicting financial statement fraud (consistent with Beneish).

True

Which predictive analytics technique would be used to predict whether a firm is likely to have a material misstatement in the coming year?

classification

Which predictive analytics technique would be used to predict the interest rate given from a lender (bank) to a borrower?

regression

According to Altman’s Z, the greater amount of long-term profitability generally leads to:

lower risk of bankruptcy.

If a company has an Altman Z-score of 1.7, the company would be classified in the:

distress zone, or at significant risk of bankruptcy.

If a company has an Altman Z-score of 3.1, the company would be classified in the:

safe zone, not currently at risk of bankruptcy.

According to the text, GAAP does not prefer which type of accounting since it is inferior at forecasting future performance?

cash basis

Which is more persistent in predicting future amounts of the variable based on current amounts of that same variable?

sales

The persistence of operating income as compared to cash flows from operations is consistent with GAAP requiring the use of:

accrual-basis accounting for financial reporting purposes.

Predicting which companies are likely to have misstated financial statements is an example of the predictive analytics technique of:

classification

What phase of the AMPS model is focused on in Chapter 5?

Perform the Analysis

What are the four types of questions in data analytics?

What happened? Why did it happen? Will it happen in the future? What should we do?

What is descriptive analytics?

Analytics that characterize, summarize, and organize data to answer "What happened?"

What are examples of descriptive analytics questions?

Did we make a profit last year? How long have accounts receivable been outstanding?

What is diagnostic analytics?

Analytics that investigate underlying causes of past results to answer "Why did it happen?"

What are examples of diagnostic analytics questions?

Why did advertising expense increase, but sales fall? Why did overall tax increase even though net income did not?

What is predictive analytics?

Analytics that assess likelihood or probability to answer "Will it happen in the future?"

What are examples of predictive analytics questions?

What is the chance the company will go bankrupt? Can the IRS find tax evaders using predictive techniques?

What is prescriptive analytics?

Analytics that identify the best possible options given constraints to answer "What should we do?"

What are examples of prescriptive analytics questions?

How can revenues be maximized in a trade war? Should the company lease or own its office building?

What are two broad types of diagnostic analytics?

Identifying anomalies/outliers and performing drill-down analytics

What are the three broad categories of predictive analytics?

Classification, regression, and forecasting using time series analytics

What are common tools used in prescriptive analytics?

Sensitivity analysis, capital budgeting, marginal analysis, goal-seek analytics

What is a population in statistics?

A group of phenomena having something in common

What is a sample in statistics?

A subset of a population selected to represent that population

What are the types of probability distributions?

Normal (bell curve), uniform (equal likelihood), Poisson (skewed right)

What is the null hypothesis in hypothesis testing?

Assumes no significant difference between two samples or populations

What are common significance level (alpha) thresholds?

5% (0.05) and 1% (0.01)

What is the general equation format for regression analysis?

y = f(x), where y is the dependent variable and x is the independent variable

What is an example of dependent and independent variables in regression?

Dependent: sales revenue; Independent: advertising expense

What Excel tool is used for regression and forecasting?

Data Analysis Toolpak

If the alpha threshold is 0.05, and the p-value on a t-test comparing one company's ROA to the industry average ROA is 0.045, what do you conclude about the null hypothesis that the company's ROA is the same as the industry?

Reject the null hypothesis

If the alpha threshold is 0.10 and the p-value on a t-test comparing one company's ROA to the industry average ROA is 0.11, what do you conclude about the null hypothesis that the company's ROA is the same as the industry?

Fail to reject the null hypothesis

If your IQ is 130, the average population IQ is 100, and the standard deviation is 15, what is your z-score?

2 because (130-100)/15

Which type of question does predictive analytics address?

Will it happen in the future?

Which type of question does diagnostic analytics address?

Why did it happen?

What type of analytics addresses questions of “What happened?”?

Descriptive analytics

What type of analytics would address the question of whether a company will go bankrupt?

Predictive analytics

If we wanted to know what level of sales would be needed to break even given the current economic environment, we would call that ________blank analytics.

prescriptive

What type of analytics would primarily summarize facts and compute sums, averages, counts, etc.?

Descriptive analytics

What type of analytics would primarily use what-if analysis, scenario manager, and goal seek analysis?

Prescriptive analytics

What type of analytics would include Benford’s law to assess whether fraud had occurred?

Diagnostic analytics

What kind of analytics is generally used to forecast future sales, earnings, and cash flows from operating activities?

Time series

What kind of analytics is generally used to predict whether a company’s financial statements are fraudulent?

Classification

Which tool is generally associated with prescriptive analytics?

What-if scenario

Which statistic tells us how many standard deviations (σ), a data point (or observation), x_i, is from its population mean, μ?

z-score

Which distribution is a probability distribution where all outcomes are equally likely?

Uniform distribution

If p-value < = alpha threshold, you should:

reject the null hypothesis (i.e., significant result).

If we run a regression where y (college completion rate) = f (factors potentially predicting college completion rate), what is the dependent variable?

College completion rate

A ________blank t-test has more power than a ________blank t-test.

one-tailed; two-tailed

If the critical value for a one-tailed t-test at the α = 0.05 level is 1.645, the t-statistic needed to reject the null hypothesis of no difference would be ________blank.

greater than 1.645

What type of analysis is the focus of Chapter 7 in the AMPS model?

Diagnostic Analytics

What does diagnostic analytics address?

"Why did it happen?" and "What are the reasons for past results?"

How does diagnostic analytics differ from descriptive analytics?

Descriptive analytics explains what happened, while diagnostic analytics investigates why it happened.

What are the two broad types of diagnostic analytics?

Identifying anomalies/outliers and finding previously unknown linkages, patterns, or relationships.

Why do accountants expect specific financial outcomes?

Because deviations from expected outcomes indicate issues that require further investigation.

What is management by exception?

A managerial style where management focuses on addressing significant deviations from expectations.

What are examples of anomalies in accounting?

Unexpected changes in revenue, unusual expense fluctuations, or unexplained discrepancies in transactions.

What are some common tools used in diagnostic analytics?

Sequence checks, fuzzy matching, duplicate transaction detection, variance analysis, bank reconciliations, and Benford’s Law.

What does Benford’s Law help detect?

Potential fraud by analyzing the frequency of first digits in numerical data.

What is the purpose of variance analysis in diagnostic analytics?

To identify differences between expected and actual performance, such as unfavorable labor rate variances.

What is fuzzy matching used for in diagnostic analytics?

To detect similarities in data, such as comparing vendor and employee addresses for potential fraud.

What are Type I and Type II errors in fuzzy matching?

Type I: Too strict, leading to false positives. Type II: Too loose, leading to missed detections.

What famous example demonstrates the power of finding unknown patterns in data?

Moneyball – using analytics to identify undervalued baseball talent.

Why is drill-down analysis useful in diagnostic analytics?

It helps identify specific causes of financial anomalies, such as the highest outstanding receivables or most profitable customers.

How can hypothesis testing be used in diagnostic analytics?

To compare means or relationships, such as analyzing if male executives are paid more than female executives.

What is an example of a hypothesis testing question in accounting?

Are Nordstrom’s sales returns higher during the holiday season compared to non-holiday seasons?

How does regression analysis help in diagnostic analytics?

It identifies relationships between variables, such as how advertising expenses impact sales revenue.

What are the dependent and independent variables in a regression analyzing advertising expenses and sales revenue?

Dependent: Sales revenue. Independent: Advertising expenses.

What is an example of how diagnostic analytics is used in financial decision-making?

Investigating the most and least profitable products to determine whether to promote, adjust, or discontinue them.

What is a key takeaway about diagnostic analytics?

It helps uncover the reasons behind financial results, making it essential for fraud detection, financial analysis, and managerial decision-making.

According to Benford’s law, what is the expectation for the percentage of times that the first digit of a number is a 9?

4.58%

What is an example of a cash reconciling item for an item recorded in the general ledger but not yet recorded in the bank statement?

Outstanding checks

Which cash reconciling item could potentially affect either the bank statement or the general ledger?

Error

Which of the following can potentially find gaps in the check numbers written?

Sequence check

Diagnostic analytics which uncovers previously unknown linkages, patterns, and relationships between variables is known as which of the following?

drill-down, detailed

The appropriate test to compare one sample to another sample to see if one is greater than another in some way is called a(n) ________

two-sample t-test of a difference in means

The appropriate test to see if independent variables are related to a dependent variable is called a(n) ________

regression

is used to help find vendors that might have a similar address as an employee.

Fuzzy matching

In fuzzy matching, if an exact match (tolerance = 100%) is required, what is the likely consequence for finding potential matches?

Only exactly matches, no potential matches

Management accounting uses variance analysis to explain what and why something happened in the cost of producing products. It is done by comparing actual outcomes to expected, or standard, costs. What is the expectation when the price of raw materials unexpectedly increases?

Unfavorable price variance