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Exam (elaborations)

Data Analytics for Accounting, 3rd Edition – Vernon Richardson | Complete Solutions & Instructor’s Manual (Chapters 1–11)

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This document contains the full solutions and instructor’s manual for Data Analytics for Accounting, 3rd Edition by Vernon Richardson. It covers all chapters (1–11) with verified answers to multiple-choice questions, discussion and analysis responses, and complete worked-out solutions to practice problems. The content is aligned with the study guide and provides detailed explanations, examples, and problem-solving steps useful for both teaching and exam preparation.

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Institution
Data Analytics for Accounting, 3rd Edition
Module
Data Analytics for Accounting, 3rd Edition

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Uploaded on
September 16, 2025
Number of pages
117
Written in
2025/2026
Type
Exam (elaborations)
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Questions & answers

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Data Analytics for Accounting, 3rd Edition by Vernon
Richardson| SOLUTION & INSTRUCTOR’S MANUAL
Chapter 1-11| All Chapters Containing Verified Questions &
Solutions for the Study Guide



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, Chapter 1

Solutions to Multiple Choice Questions
1. (LO 1-1) Big Data is often described by the four Vs, or

a. volume, velocity, veracity, and variability.

b. volume, velocity, veracity, and variety.

c. volume, volatility, veracity, and variability.

d. variability, velocity, veracity, and variety.

Accurate Answer: b



2. LO 1-4) Which data approach attempts to assign each unit in a population into a small set of
classes (or groups) where the unit best fits?

a. Regression

b. Similarity matching

c. Co-occurrence grouping

d. Classification

Accurate Answer: d



3. (LO 1-4) Which data approach attempts to identify similar individuals based on data known about
them?

a. Classification

b. Regression

c. Similarity matching

d. Data reduction

Accurate Answer: c



4. (LO 1-4) Which data approach attempts to predict connections between two data items?

a. Profiling
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b. Classification

c. Link prediction
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,d. Regression

Accurate Answer: c



5. (LO 1-6) Which of these terms is defined as being a central repository of descriptions for all of the
data attributes of the dataset?

a. Big Data

b. Data warehouse

c. Data dictionary

d. Data Analytics

Accurate Answer: c



6. (LO 1-5) Which skills were not emphasized that analytic-minded accountants should have?

a. Developed an analytics mindset

b. Data scrubbing and data preparation

c. Classification of test approaches

d. Statistical data analysis competency

Accurate Answer: c



7. (LO 1-5) In which areas were skills not emphasized for analytic-minded accountants?

a. Data quality

b. Descriptive data analysis

c. Data visualization and data reporting

d. Data and systems analysis and design

Accurate Answer: d



8. (LO 1-4) The IMPACT cycle includes all except the following steps:

a. perform test plan.
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b. visualize the data.
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, c. master the data.

d. track outcomes.

Accurate Answer: b



9. (LO 1-4) The IMPACT cycle specifically includes all except the following steps:

a. data preparation.

b. communicate insights.

c. address and refine results.

d. perform test plan.

Accurate Answer: a



10. LO 1-1) By the year 2024, the volume of data created, captured, copied, and consumed
worldwide will be 149 .

a. zettabytes

b. petabytes

c. exabytes

d. yottabytes

Accurate Answer: a



Solutions to Discussion and Analysis Questions
1. The accounting function is one of being an information provider. To the extent that data is
available to address accounting questions, be they tax, managerial, audit or financial questions. With
such rich available data, and software tools to prepare and analyze the data, data analytics will
continue to be an important tool for accountants to use.

2. Data analytics is defined as the process of evaluating data with the purpose of drawing conclusions
to address business questions. Indeed, effective Data Analytics provides a way to search through
large structured and unstructured data to identify unknown patterns or relationships.

A university might learn from the analyzing the demographics of its current set of students in order to
attract its future student recruits. Did they come from cities or high schools that were close by? Were
their parents alumni of the university? Did they score high on certain parts of the ACT? Were those
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offered a scholarship more likely to attend, etc.? Was social media effective in attracting new,
potentially stronger students? By analyzing this type of data, previously unknown patterns will
emerge that will make recruiting students more effective.
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