ACTG 310 - Exam 2

Chapters 4, 6, 9

Qualitative Data

Categorical data. All you can do is count group, or rank.

• Can be nominal or Ordinal

Nominal Data

The least sophisticated type of qualitative data. You can count, group, and take a proportion

• Ex: gender

Ordinal Data

Qualitative data that can be ranked

• Ex: gold, silver, bronze

Proportion Data

Qualitative data that shows the makeup of each category

Quantitative data

More complex than qualitative data because the differences between data points are meaningful

Ratio Data

The most sophisticated data type, quantitative data that can be counted and grouped. Defines 0 as

• ex: cash

Interval Data

Quantitative data where 0 is just another number

• Ex: temperature

Discrete data

Quantitative data, only whole numbers

Continuous data

Quantitative data that can show numbers with decimals

Distributions

describe the mean, median, and standard deviation of the data

Normal distribution

A type of distribution where the mean, median, and mode are all equal, so half of all observations are above the mean and half are below.

standardization

the method used for comparing two datasets that follow the normal distribution

Declarative Visualizations

Made when the aim of your project is to “declare” or present your findings to an audience

Exploratory Visualizations

Made when the lines between steps PAC are not as clearly divided as they are in a declarative visualization project

• usually a test plan directly in a visualization software

Bar charts, pie charts, stacked bar chart, tree map, heat map, symbol map, word clouds

When you want to show qualitative data:

Line charts, box and whisker, scatter plots, filled geographic maps

When you want to show quantitative data:

Financial Statement Audit

an objective examination and evaluation of the financial statements of an organization to make sure that the financial records are a fair and accurate representation of the transactions they claim to represent

Nature

represents why auditors perform audit procedures

Extent

indicates how much auditors can test (think scope)

Timing

How often the testing procedure should be run

Process Mining

Analysis technique of business processes used to diagnose problems and suggest improvements where greater efficiency may be applied

Descriptive Analytics - Audit

summarize activity or master data on specific attributes

• age analysis, sorting, summary statistics, and sampling, fuzzy matching

Diagnostic analytics -audit

look for correlations or patterns of interest

• Ex: z-score, Benford’s law, drill-down, clustering

Prescriptive analytics - audit

make recommendations based on past data

• ex: applied statistics, what if analysis, AI

Predictive Analytics - audit

Help auditors discover hidden patterns linked to abnormal behavior

• Regression, probability, classification

Computer-assisted audit techniques (CAAT)

automated scripts that can be used to validate data, test controls, and enable substantive testing of transaction details or account balances and generate supporting evidence for the audit

Fuzzy Matching

Process that finds matches that may be less than 100 percent by finding correspondences between portions of the text or other entries

Benford’s law

The principle that in any large, randomly produces set of natural numbers, there is an expected distribution of the leading digit of 1 being most common, then 2, and so on

Sentiment analysis

enables the evaluation of text for distributions of words that may be classified as positive or negative outcomes or to look for potential bias

Data Warehouse

a repository of data accumulated from internal and external sources, including financial data, to help management decision making

Data mart

a subset of the data warehouse focused on a specific department to assist and support its needed data requirements

• Ex: tax data mart

tax cost, tax risk, tax efficiency/effectiveness, sustainability, and permanent differences

Tax KPIs focus on tax management: