Verified Test Bank Data Analytics Chapter.17

Accounting Information Systems, 1e (Savage)

Chapter 17 Data Analytics

1) Five data analytics-related job titles were discussed in the text. Which of the following job titles would best fit the following description: "Builds the technological infrastructure and architecture for gathering, growing, and storing raw data."

A) Data engineer

B) Data scientist

C) Statistician

D) Data analyst

Diff: 2

Learning Objective: 17.1 Identify career opportunities for accounting professionals working with data.

Section Reference: Types of Analysts

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

2) Five data analytics-related job titles were discussed in the text. Which of the following job titles would best fit the following description: "Designs and programs algorithms to collect and analyze data and perform predictive analytics."

A) Data engineer

B) Data scientist

C) Statistician

D) Data analyst

Diff: 2

Learning Objective: 17.1 Identify career opportunities for accounting professionals working with data.

Section Reference: Types of Analysts

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

3) Five data analytics-related job titles were discussed in the text. Which of the following job titles would best fit the following description: "The role is math oriented and focuses on collection and interpretation of quantitative data using defined scientific methods."

A) Data engineer

B) Data scientist

C) Statistician

D) Data analyst

Diff: 2

Learning Objective: 17.1 Identify career opportunities for accounting professionals working with data.

Section Reference: Types of Analysts

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

4) Five data analytics-related job titles were discussed in the text. Which of the following job titles would best fit the following description: "Collects, manipulates, and analyzes data across a business."

A) Data engineer

B) Data scientist

C) Statistician

D) Data analyst

Diff: 2

Learning Objective: 17.1 Identify career opportunities for accounting professionals working with data.

Section Reference: Types of Analysts

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

5) Five data analytics-related job titles were discussed in the text. Which of the following job titles would best fit the following description: "Possesses a deep understanding of business processes and can evaluate them, analyze key metrics, and provide strategic recommendations."

A) Business analyst

B) Data scientist

C) Statistician

D) Data analyst

Diff: 2

Learning Objective: 17.1 Identify career opportunities for accounting professionals working with data.

Section Reference: Types of Analysts

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

6) Five data analytics-related job titles were discussed in the text. Which of the following job titles would need an expert understanding of database administration?

A) Data engineer

B) Data scientist

C) Statistician

D) Data analyst

Diff: 2

Learning Objective: 17.1 Identify career opportunities for accounting professionals working with data.

Section Reference: Types of Analysts

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

7) Five data analytics-related job titles were discussed in the text. Which of the following job titles would need an expert understanding of data modeling?

A) Business analyst

B) Data scientist

C) Statistician

D) Data analyst

Diff: 2

Learning Objective: 17.1 Identify career opportunities for accounting professionals working with data.

Section Reference: Types of Analysts

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

8) Five data analytics-related job titles were discussed in the text. Which of the following job titles would need an expert understanding of data?

A) Data engineer

B) Data scientist

C) Statistician

D) Data analyst

Diff: 2

Learning Objective: 17.1 Identify career opportunities for accounting professionals working with data.

Section Reference: Types of Analysts

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

9) Five data analytics-related job titles were discussed in the text. Which of the following job titles would need an expert understanding of visualization?

A) Data engineer

B) Data scientist

C) Statistician

D) Business intelligence analyst

Diff: 2

Learning Objective: 17.1 Identify career opportunities for accounting professionals working with data.

Section Reference: Types of Analysts

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

10) There are four widely used categories of data analysis. Which two looks at the past and uses historical data to learn more about what occurred and why it occurred?

A) Descriptive and Diagnostic

B) Predictive and Prescriptive

C) Descriptive and Prescriptive

D) Diagnostic and Predictive

Diff: 1

Learning Objective: 17.1 Identify career opportunities for accounting professionals working with data.

Section Reference: Types of Analytics

AACSB: None

Bloom's: Knowledge

AICPA: AC: Technology and Tools

11) There are four widely used categories of data analysis. Which two analyze historical data and predict future events, providing recommendations on what the business should do?

A) Descriptive and Diagnostic

B) Predictive and Prescriptive

C) Descriptive and Prescriptive

D) Diagnostic and Predictive

Diff: 1

Learning Objective: 17.1 Identify career opportunities for accounting professionals working with data.

Section Reference: Types of Analytics

AACSB: None

Bloom's: Knowledge

AICPA: AC: Technology and Tools

12) There are four widely used categories of data analytics. Which one tells us what has happened?

A) Descriptive analytics

B) Diagnostic analytics

C) Predictive analytics

D) Prescriptive analytics

Diff: 1

Learning Objective: 17.1 Identify career opportunities for accounting professionals working with data.

Section Reference: Types of Analytics

AACSB: None

Bloom's: Knowledge

AICPA: AC: Technology and Tools

13) There are four widely used categories of data analytics. Which one tells us why something happened?

A) Descriptive analytics

B) Diagnostic analytics

C) Predictive analytics

D) Prescriptive analytics

Diff: 1

Learning Objective: 17.1 Identify career opportunities for accounting professionals working with data.

Section Reference: Types of Analytics

AACSB: None

Bloom's: Knowledge

AICPA: AC: Technology and Tools

14) There are four widely used categories of data analytics. Which one uses statistical modeling and algorithms to predict what is likely to happen?

A) Descriptive analytics

B) Diagnostic analytics

C) Predictive analytics

D) Prescriptive analytics

Diff: 1

Learning Objective: 17.1 Identify career opportunities for accounting professionals working with data.

Section Reference: Types of Analytics

AACSB: None

Bloom's: Knowledge

AICPA: AC: Technology and Tools

15) There are four widely used categories of data analytics. Which one identifies what we should do?

A) Descriptive analytics

B) Diagnostic analytics

C) Predictive analytics

D) Prescriptive analytics

Diff: 1

Learning Objective: 17.1 Identify career opportunities for accounting professionals working with data.

Section Reference: Types of Analytics

AACSB: None

Bloom's: Knowledge

AICPA: AC: Technology and Tools

16) There are four widely used categories of data analytics. Choose the best description for descriptive analytics.

A) Looks at historical data and condenses it into smaller, more meaningful bits of information

B) Drills down to a granular level

C) Provides powerful tools that assist in decision making and inform future actions

D) Requires advanced programming

Diff: 2

Learning Objective: 17.1 Identify career opportunities for accounting professionals working with data.

Section Reference: Types of Analytics

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

17) There are four widely used categories of data analytics. Choose the best description for diagnostic analytics.

A) Looks at historical data and condenses it into smaller, more meaningful bits of information

B) Drills down to a granular level

C) Provides powerful tools that assist in decision making and inform future actions

D) Requires advanced programming

Diff: 2

Learning Objective: 17.1 Identify career opportunities for accounting professionals working with data.

Section Reference: Types of Analytics

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

18) There are four widely used categories of data analytics. Choose the best description for predictive analytics.

A) Looks at historical data and condenses it into smaller, more meaningful bits of information

B) Drills down to a granular level

C) Provides powerful tools that assist in decision making and inform future actions

D) Requires advanced programming

Diff: 2

Learning Objective: 17.1 Identify career opportunities for accounting professionals working with data.

Section Reference: Types of Analytics

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

19) There are four widely used categories of data analytics. Choose the best description for prescriptive analytics.

A) Looks at historical data and condenses it into smaller, more meaningful bits of information

B) Drills down to a granular level

C) Provides powerful tools that assist in decision making and inform future actions

D) Requires advanced programming

Diff: 2

Learning Objective: 17.1 Identify career opportunities for accounting professionals working with data.

Section Reference: Types of Analytics

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

20) Machine Learning (ML) uses algorithms and statistical models to train an AI system through patterns and trends in data sets. ML programs systems to perform tasks without explicit instructions and is a popular application of AI in data analytics. There are three machine learning approaches. Choose the best definition for supervised learning.

A) Uses labeled data sets to train the algorithm to classify data or predict outcomes from a data set

B) Uses algorithms to analyze unlabeled data sets for hidden patterns

C) Focuses on decision making by rewarding desired behaviors and/or punishing undesired behaviors

D) This hybrid approach may require human intervention.

Diff: 2

Learning Objective: 17.1 Identify career opportunities for accounting professionals working with data.

Section Reference: Machine Learning (ML)

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

21) Machine Learning (ML) uses algorithms and statistical models to train an AI system through patterns and trends in data sets. ML programs systems to perform tasks without explicit instructions and is a popular application of AI in data analytics. There are three machine learning approaches. Choose the best definition for unsupervised learning.

A) Uses labeled data sets to train the algorithm to classify data or predict outcomes from a data set

B) Uses algorithms to analyze unlabeled data sets for hidden patterns

C) Focuses on decision making by rewarding desired behaviors and/or punishing undesired behaviors

D) This hybrid approach may require human intervention.

Diff: 2

Learning Objective: 17.1 Identify career opportunities for accounting professionals working with data.

Section Reference: Machine Learning (ML)

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

22) Machine Learning (ML) uses algorithms and statistical models to train an AI system through patterns and trends in data sets. ML programs systems to perform tasks without explicit instructions and is a popular application of AI in data analytics. There are three machine learning approaches. Choose the best definition for reinforcement learning.

A) Uses labeled data sets to train the algorithm to classify data or predict outcomes from a data set

B) Uses algorithms to analyze unlabeled data sets for hidden patterns

C) Focuses on decision making by rewarding desired behaviors and/or punishing undesired behaviors

D) Before it can begin, human intervention is necessary to label the data set.

Diff: 2

Learning Objective: 17.1 Identify career opportunities for accounting professionals working with data.

Section Reference: Machine Learning (ML)

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

23) Machine Learning (ML) uses algorithms and statistical models to train an AI system through patterns and trends in data sets. ML programs systems to perform tasks without explicit instructions and is a popular application of AI in data analytics. There are three machine learning approaches. Monte Carlo simulation is an example of which type of ML?

A) Supervised learning

B) Unsupervised learning

C) Reinforcement learning

D) Refurbished learning

Diff: 2

Learning Objective: 17.1 Identify career opportunities for accounting professionals working with data.

Section Reference: Machine Learning (ML)

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

24) The first step in analyzing a data set is learning about its data. Exploratory data analytics techniques reveal the key characteristics of a data set. Exploratory data analytics techniques help us identify three key factors of a data set. Which of the key factors is the descriptive components within a data set?

A) Categorical values

B) Quantitative values

C) Patterns

D) Anomalies

Diff: 2

Learning Objective: 17.2 Describe data analytics techniques that can explore data.

Section Reference: Introduction

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

25) The first step in analyzing a data set is learning about its data. Exploratory data analytics techniques reveal the key characteristics of a data set. Exploratory data analytics techniques help us identify three key factors of a data set. Which of the key factors is the numeric data points that can be summed, counted, or otherwise analyzed using mathematical operations?

A) Categorical values

B) Quantitative values

C) Patterns

D) Anomalies

Diff: 2

Learning Objective: 17.2 Describe data analytics techniques that can explore data.

Section Reference: Introduction

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

26) The first step in analyzing a data set is learning about its data. Exploratory data analytics techniques reveal the key characteristics of a data set. Exploratory data analytics techniques help us identify three key factors of a data set. Which of the key factors is recurring or similar values, either categorical or quantitative?

A) Categorical values

B) Quantitative values

C) Patterns

D) Anomalies

Diff: 2

Learning Objective: 17.2 Describe data analytics techniques that can explore data.

Section Reference: Introduction

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

27) The first step in analyzing a data set is learning about its data. Besides discovering patterns, exploration can find unexpected data points. What are these data points called that fall outside the data set's norm?

A) Categorical values

B) Quantitative values

C) Patterns

D) Anomalies

Diff: 2

Learning Objective: 17.2 Describe data analytics techniques that can explore data.

Section Reference: Introduction

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

28) You are presented with a data set of 6 test scores: 47, 46, 46, 44, 43 and 26. What is the average test score?

A) 46

B) 44

C) 42

D) 26

Diff: 2

Learning Objective: 17.2 Describe data analytics techniques that can explore data.

Section Reference: Introduction

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

29) You are presented with a data set of 6 test scores: 47, 46, 46, 44, 43 and 26. Which grade is an outlier?

A) 46

B) 44

C) 42

D) 26

Diff: 2

Learning Objective: 17.2 Describe data analytics techniques that can explore data.

Section Reference: Introduction

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

30) You are presented with a data set of 10 test scores: 99, 85, 84, 83, 83, 81, 80, 80, 79, and 66. What is the average test score?

A) 82

B) 99

C) 83

D) 81

Diff: 2

Learning Objective: 17.2 Describe data analytics techniques that can explore data.

Section Reference: Introduction

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

31) You are presented with a data set of 10 test scores: 99, 85, 84, 83, 83, 81, 80, 80, 79, and 66. Which grade(s) are an outlier?

A) 82

B) 99 and 66

C) 95

D) 79

Diff: 2

Learning Objective: 17.2 Describe data analytics techniques that can explore data.

Section Reference: Introduction

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

32) Choose from the definitions below the best definition of anomaly detection.

A) Anomaly detection techniques reveal the key characteristics of a data set.

B) Anomaly detection is the numeric data points that can be summed, counted, or otherwise analyzed using mathematical operations.

C) Anomaly detection reveals observations or events that are outside a data set's normal behavior.

D) Anomaly detection is the descriptive components within a data set.

Diff: 2

Learning Objective: 17.2 Describe data analytics techniques that can explore data.

Section Reference: Anomaly Detection

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

33) What is also known as outlier analysis and reveals observations or events that are outside a data set's normal behavior?

A) Quantitative values

B) Categorical values

C) Exploratory data analytics

D) Anomaly detection

Diff: 2

Learning Objective: 17.2 Describe data analytics techniques that can explore data.

Section Reference: Anomaly Detection

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

34) Exploring a new data set and understanding its composition commonly involves the data analytics technique ________, which involves simplifying data to quickly identify the composition of categorical and quantitative values. We compress the data into smaller, easier-to-understand outputs, called data summaries. To do this, we typically group a data set by a specific field, or column.

A) data summarization

B) quantitative values

C) data summaries

D) field, or column

Diff: 1

Learning Objective: 17.2 Describe data analytics techniques that can explore data.

Section Reference: Data Summarization

AACSB: None

Bloom's: Knowledge

AICPA: AC: Technology and Tools

35) Exploring a new data set and understanding its composition commonly involves the data analytics technique data summarization, which involves simplifying data to quickly identify the composition of categorical and ________. We compress the data into smaller, easier-to-understand outputs, called data summaries. To do this, we typically group a data set by a specific field, or column.

A) data summarization

B) quantitative values

C) data summaries

D) field, or column

Diff: 1

Learning Objective: 17.2 Describe data analytics techniques that can explore data.

Section Reference: Data Summarization

AACSB: None

Bloom's: Knowledge

AICPA: AC: Technology and Tools

36) Exploring a new data set and understanding its composition commonly involves the data analytics technique data summarization, which involves simplifying data to quickly identify the composition of categorical and quantitative values. We compress the data into smaller, easier-to-understand outputs, called ________. To do this, we typically group a data set by a specific field, or column.

A) data summarization

B) quantitative values

C) data summaries

D) field, or column

Diff: 1

Learning Objective: 17.2 Describe data analytics techniques that can explore data.

Section Reference: Data Summarization

AACSB: None

Bloom's: Knowledge

AICPA: AC: Technology and Tools

37) Exploring a new data set and understanding its composition commonly involves the data analytics technique data summarization, which involves simplifying data to quickly identify the composition of categorical and quantitative values. We compress the data into smaller, easier-to-understand outputs, called data summaries. To do this, we typically group a data set by a specific

A) data summarization.

B) quantitative values.

C) data summaries.

D) field, or column.

Diff: 1

Learning Objective: 17.2 Describe data analytics techniques that can explore data.

Section Reference: Data Summarization

AACSB: None

Bloom's: Knowledge

AICPA: AC: Technology and Tools

38) What is a popular method of summarizing smaller data sets in Excel?

A) Pivot table

B) Group by

C) Summarize

D) Dragging field names to the appropriate locations

Diff: 1

Learning Objective: 17.2 Describe data analytics techniques that can explore data.

Section Reference: Data Summarization

AACSB: None

Bloom's: Knowledge

AICPA: AC: Technology and Tools

39) What is the first step in most complex data analytics techniques?

A) Data summarization

B) Clustering

C) Classification

D) Anomaly detection

Diff: 1

Learning Objective: 17.2 Describe data analytics techniques that can explore data.

Section Reference: Clustering and Classification

AACSB: None

Bloom's: Knowledge

AICPA: AC: Technology and Tools

40) Clustering, or cluster analysis, is an analytics technique that categorizes data points into groups based on their similarities. The groups, called clusters, are determined by the distance between individual items, which indicates how closely related the data points are. Clustering is a(n) ________ Machine Learning (ML) technique in which the data input contains unlabeled data.

A) supervised

B) unsupervised

C) labeled

D) unlabeled

Diff: 1

Learning Objective: 17.2 Describe data analytics techniques that can explore data.

Section Reference: Clustering and Classification

AACSB: None

Bloom's: Knowledge

AICPA: AC: Technology and Tools

41) Clustering, or cluster analysis, is an analytics technique that categorizes data points into groups based on their similarities. The groups, called clusters, are determined by the distance between individual items, which indicates how closely related the data points are. Clustering is an unsupervised Machine Learning (ML) technique in which the data input contains ________ data.

A) supervised

B) unsupervised

C) labeled

D) unlabeled

Diff: 1

Learning Objective: 17.2 Describe data analytics techniques that can explore data.

Section Reference: Clustering and Classification

AACSB: None

Bloom's: Knowledge

AICPA: AC: Technology and Tools

42) Classification analysis is the categorization of data into groups based on similarities found in a data label that was previously defined. Classification analysis might sound closely related to clustering—and these two types of analysis do have similarities. While they are both methods of categorization, they differ in the type of Machine Learning (ML) they use. Clustering uses

A) unsupervised ML to analyze unlabeled data inputs.

B) supervised ML to analyze labeled data inputs.

C) unsupervised ML to analyze labeled data inputs.

D) supervised ML to analyze unlabeled data inputs.

Diff: 2

Learning Objective: 17.2 Describe data analytics techniques that can explore data.

Section Reference: Clustering and Classification

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

43) Classification analysis is the categorization of data into groups based on similarities found in a data label that was previously defined. Classification analysis might sound closely related to clustering—and these two types of analysis do have similarities. While they are both methods of categorization, they differ in the type of Machine Learning (ML) they use. Classification uses

A) unsupervised ML to analyze unlabeled data inputs.

B) supervised ML to analyze labeled data inputs.

C) unsupervised ML to analyze labeled data inputs.

D) supervised ML to analyze unlabeled data inputs.

Diff: 2

Learning Objective: 17.2 Describe data analytics techniques that can explore data.

Section Reference: Clustering and Classification

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

44) What is the first step in classification?

A) Data summarization

B) Clustering

C) Ensuring that a data set has appropriate data labels

D) Ensuring that a data set is alphabetical

Diff: 2

Learning Objective: 17.2 Describe data analytics techniques that can explore data.

Section Reference: Clustering and Classification

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

45) ________ captures data that occurs in chronological order across a period of time.

A) A time series

B) Seasonality

C) A time trend

D) A dependent variable

Diff: 1

Learning Objective: 17.3 Evaluate data analytics techniques that explain changes over time.

Section Reference: Introduction

AACSB: None

Bloom's: Knowledge

AICPA: AC: Technology and Tools

46) ________ is a consistent movement in the time series data that does not repeat. One example is an increase in revenue through a fiscal year due to a new product launch.

A) A dependent variable

B) A time trend

C) Seasonality

D) Noise

Diff: 1

Learning Objective: 17.3 Evaluate data analytics techniques that explain changes over time.

Section Reference: Introduction

AACSB: None

Bloom's: Knowledge

AICPA: AC: Technology and Tools

47) ________ is a consistent movement in the time series data that repeats on a regular basis. An example is an increase in revenue every June, July and August due to summer beach sales.

A) A dependent variable

B) A time trend

C) Seasonality

D) Noise

Diff: 1

Learning Objective: 17.3 Evaluate data analytics techniques that explain changes over time.

Section Reference: Introduction

AACSB: None

Bloom's: Knowledge

AICPA: AC: Technology and Tools

48) ________ is additional movements in the time series data that cannot be explained as a trend or seasonality. A drastic spike in revenue at the end of February due to a large customer order for a one-time event is an example of

A) A dependent variable

B) A time trend

C) Seasonality

D) Noise

Diff: 1

Learning Objective: 17.3 Evaluate data analytics techniques that explain changes over time.

Section Reference: Introduction

AACSB: None

Bloom's: Knowledge

AICPA: AC: Technology and Tools

49) Linear regression is a statistical technique we use to estimate the relationships between a(n) ________ and one or more independent variables.

A) dependent variable

B) independent variable

C) slope of the line

D) Y-intercept

Diff: 1

Learning Objective: 17.3 Evaluate data analytics techniques that explain changes over time.

Section Reference: Linear Regression

AACSB: None

Bloom's: Knowledge

AICPA: AC: Technology and Tools

50) Linear regression is a statistical technique we use to estimate the relationships between a dependent variable and one or more

A) dependent variables.

B) independent variables.

C) slopes of the line.

D) Y-intercepts.

Diff: 1

Learning Objective: 17.3 Evaluate data analytics techniques that explain changes over time.

Section Reference: Linear Regression

AACSB: None

Bloom's: Knowledge

AICPA: AC: Technology and Tools

51) Choose from the list below the best definition of a dependent variable.

A) A dependent variable is the value to be understood. It is often called the outcome.

B) A dependent variable is the factor that may be influencing the dependent variable. There can be one or more than one, depending on the type of regression performed.

C) A dependent variable is the Y-intercept.

D) A dependent variable is the slope of the line.

Diff: 2

Learning Objective: 17.3 Evaluate data analytics techniques that explain changes over time.

Section Reference: Linear Regression

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

52) Choose from the list below the best definition of an independent variable.

A) An independent variable is the value to be understood. It is often called the outcome.

B) An independent variable is the factor that may be influencing the dependent variable. There can be one or more than one, depending on the type of regression performed.

C) An independent variable is the Y-intercept.

D) An independent variable is the slope of the line.

Diff: 2

Learning Objective: 17.3 Evaluate data analytics techniques that explain changes over time.

Section Reference: Linear Regression

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

53) Maeve is a freshman outfielder on the softball team. Her softball coach tells her that the more hours she practices hitting in the batting cage and on the field, the higher her batting average will be during the season. What is the dependent variable?

A) The number of pitches she faces

B) The spikes she wears

C) Her batting average

D) The hours of practice she does

Diff: 2

Learning Objective: 17.3 Evaluate data analytics techniques that explain changes over time.

Section Reference: Linear Regression

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

54) Maeve is a freshman outfielder on the softball team. Her softball coach tells her that the more hours she practices hitting in the batting cage and on the field, the higher her batting average will be during the season. What is the independent variable?

A) The number of pitches she faces

B) The spikes she wears

C) Her batting average

D) The hours of practice she does

Diff: 2

Learning Objective: 17.3 Evaluate data analytics techniques that explain changes over time.

Section Reference: Linear Regression

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

55) Consider the simple linear regression equation:

Y = A + Bx

What does Y represent?

A) The dependent variable

B) The independent variable

C) The Y-intercept

D) The slope of line

Diff: 2

Learning Objective: 17.3 Evaluate data analytics techniques that explain changes over time.

Section Reference: Linear Regression

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

56) Consider the simple linear regression equation:

Y = A + Bx

What does x represent?

A) The dependent variable

B) The independent variable

C) The Y-intercept

D) The slope of line

Diff: 2

Learning Objective: 17.3 Evaluate data analytics techniques that explain changes over time.

Section Reference: Linear Regression

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

57) Consider the simple linear regression equation:

Y = A + Bx

What does A represent?

A) The dependent variable

B) The independent variable

C) The Y-intercept

D) The slope of line

Diff: 2

Learning Objective: 17.3 Evaluate data analytics techniques that explain changes over time.

Section Reference: Linear Regression

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

58) Consider the simple linear regression equation:

Y = A + Bx

What does B represent?

A) The dependent variable

B) The independent variable

C) The Y-intercept

D) The slope of line

Diff: 2

Learning Objective: 17.3 Evaluate data analytics techniques that explain changes over time.

Section Reference: Linear Regression

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

59) What is the process of estimating future events based on the combination of past and present time series data?

A) Forecasting

B) Confidence interval

C) Break-even analysis

D) Simulation

Diff: 1

Learning Objective: 17.3 Evaluate data analytics techniques that explain changes over time.

Section Reference: Forecasting

AACSB: None

Bloom's: Knowledge

AICPA: AC: Technology and Tools

60) The "lower confidence bound" and "upper confidence bound" are part of what?

A) Forecasting

B) A confidence interval

C) Break-even analysis

D) A simulation

Diff: 1

Learning Objective: 17.3 Evaluate data analytics techniques that explain changes over time.

Section Reference: Forecasting

AACSB: None

Bloom's: Knowledge

AICPA: AC: Technology and Tools

61) The known fixed costs are compared to the estimated variable costs to predict what?

A) Forecasting

B) A confidence interval

C) Break-even analysis

D) A simulation

Diff: 1

Learning Objective: 17.3 Evaluate data analytics techniques that explain changes over time.

Section Reference: Forecasting

AACSB: None

Bloom's: Knowledge

AICPA: AC: Technology and Tools

62) Businesses like to use a ________ confidence interval when they perform forecasting.

A) 25%

B) 35%

C) 75%

D) 95%

Diff: 1

Learning Objective: 17.3 Evaluate data analytics techniques that explain changes over time.

Section Reference: Forecasting

AACSB: None

Bloom's: Knowledge

AICPA: AC: Technology and Tools

63) What uses complex calculations to predict the outcomes and probabilities associated with a decision that influences a data set?

A) Forecasting

B) A confidence interval

C) Break-even analysis

D) A simulation

Diff: 1

Learning Objective: 17.3 Evaluate data analytics techniques that explain changes over time.

Section Reference: Monte Carlo Simulation

AACSB: None

Bloom's: Knowledge

AICPA: AC: Technology and Tools

64) What is a popular simulation that predicts the probability of different outcomes in the presence of many random variables?

A) Monte Carlo simulation

B) Forecasting

C) A confidence interval

D) Break-even analysis

Diff: 1

Learning Objective: 17.3 Evaluate data analytics techniques that explain changes over time.

Section Reference: Monte Carlo Simulation

AACSB: None

Bloom's: Knowledge

AICPA: AC: Technology and Tools

65) Choose from the definitions below the best definition of event log data.

A) Event log data is data about activities in a system and includes the timestamp of when the activities occur.

B) Event log data uses data to show what individuals, systems, and machines are doing in a visual format.

C) Event log data is an analytics technique that visualizes relationships among participants in a data set to learn about the social structure those relationships create.

D) Event log data is a type of network analysis that investigates social structures on social media.

Diff: 2

Learning Objective: 17.4 Summarize advanced data analytics techniques that transform data into insights.

Section Reference: Process Mining

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

66) Choose from the definitions below the best definition of process mining.

A) Process mining is data about activities in a system and includes the timestamp of when the activities occur.

B) Process mining uses event log data to show what individuals, systems, and machines are doing in a visual format.

C) Process mining is an analytics technique that visualizes relationships among participants in a data set to learn about the social structure those relationships create.

D) Process mining is a type of network analysis that investigates social structures on social media.

Diff: 2

Learning Objective: 17.4 Summarize advanced data analytics techniques that transform data into insights.

Section Reference: Process Mining

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

67) Choose from the definitions below the best definition of network analysis.

A) Network analysis is data about activities in a system and includes the timestamp of when the activities occur.

B) Network analysis uses data to show what individuals, systems, and machines are doing in a visual format.

C) Network analysis is an analytics technique that visualizes relationships among participants in a data set to learn about the social structure those relationships create.

D) Network analysis is a type of network analysis that investigates social structures on social media.

Diff: 2

Learning Objective: 17.4 Summarize advanced data analytics techniques that transform data into insights.

Section Reference: Network Analysis

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

68) Choose from the definitions below the best definition of social network analysis.

A) Social network analysis is data about activities in a system and includes the timestamp of when the activities occur.

B) Social network analysis uses event log data to show what individuals, systems, and machines are doing in a visual format.

C) Social network analysis is an analytics technique that visualizes relationships among participants in a data set to learn about the social structure those relationships create.

D) Social network analysis is a type of network analysis that investigates social structures on social media.

Diff: 2

Learning Objective: 17.4 Summarize advanced data analytics techniques that transform data into insights.

Section Reference: Network Analysis

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

69) During the COVID-19 pandemic, researchers began using contact tracing to track people who have been in contact with an infectious disease and map the infected people and everyone they have been near during a certain time. The goal is to isolate infected and exposed individuals to slow the spread of the disease. Contact tracing is an example of what?

A) Process mining

B) Network analysis

C) Geospatial analytics

D) Natural language processing

Diff: 2

Learning Objective: 17.4 Summarize advanced data analytics techniques that transform data into insights.

Section Reference: Network Analysis

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

70) What is a type of network analysis that investigates social structures on social media?

A) Physical network analysis

B) Social category analysis

C) Social network analysis

D) Society network analysis

Diff: 1

Learning Objective: 17.4 Summarize advanced data analytics techniques that transform data into insights.

Section Reference: Network Analysis

AACSB: None

Bloom's: Knowledge

AICPA: AC: Technology and Tools

71) You recently joined Facelink, the latest social network craze. Facelink suggests 3 "friends" for you to link with based on your current connections. What type of network analysis is Facelink practicing?

A) Physical network analysis

B) Social category analysis

C) Social network analysis

D) Society network analysis

Diff: 1

Learning Objective: 17.4 Summarize advanced data analytics techniques that transform data into insights.

Section Reference: Network Analysis

AACSB: None

Bloom's: Knowledge

AICPA: AC: Technology and Tools

72) When you tag a location in a social media post, what type of analytics are you practicing?

A) Process mining

B) Network analysis

C) Geospatial analytics

D) Natural language processing

Diff: 2

Learning Objective: 17.4 Summarize advanced data analytics techniques that transform data into insights.

Section Reference: Geospatial analytics

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

73) When you track your run on a smart watch, what type of analytics are you practicing?

A) Process mining

B) Network analysis

C) Geospatial analytics

D) Natural language processing

Diff: 2

Learning Objective: 17.4 Summarize advanced data analytics techniques that transform data into insights.

Section Reference: Geospatial analytics

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

74) When you request a ride service to drive you from point A to point B, what type of analytics are you practicing?

A) Process mining

B) Network analysis

C) Geospatial analytics

D) Natural language processing

Diff: 2

Learning Objective: 17.4 Summarize advanced data analytics techniques that transform data into insights.

Section Reference: Geospatial analytics

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

75) Choose from the definitions below the best definition of geospatial analytics.

A) Geospatial analytics is data about activities in a system and includes the timestamp of when the activities occur.

B) Geospatial analytics uses data to show what individuals, systems, and machines are doing in a visual format.

C) Geospatial analytics is an analytics technique that visualizes relationships among participants in a data set to learn about the social structure those relationships create.

D) Geospatial analytics is a technique that gathers, transforms, and visualizes geographic data and imagery.

Diff: 2

Learning Objective: 17.4 Summarize advanced data analytics techniques that transform data into insights.

Section Reference: Geospatial analytics

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

76) Choose from the definitions below the best definition of textual analysis.

A) Textual analysis is a category of data analytics techniques used to interpret objects that include words.

B) Textual analysis uses data to show what individuals, systems, and machines are doing in a visual format.

C) Textual analysis is an analytics technique that visualizes relationships among participants in a data set to learn about the social structure those relationships create.

D) Textual analysis is a technique that gathers, transforms, and visualizes geographic data and imagery.

Diff: 2

Learning Objective: 17.4 Summarize advanced data analytics techniques that transform data into insights.

Section Reference: Natural Language Processing (NLP)

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

77) Choose from the definitions below the best definition of natural language processing.

A) Natural language processing uses data to show what individuals, systems, and machines are doing in a visual format.

B) Natural language processing is a form of textual analysis that gathers, processes, and interprets meaning from human language.

C) Natural language processing is an analytics technique that visualizes relationships among participants in a data set to learn about the social structure those relationships create.

D) Natural language processing is a technique that gathers, transforms, and visualizes geographic data and imagery.

Diff: 2

Learning Objective: 17.4 Summarize advanced data analytics techniques that transform data into insights.

Section Reference: Natural Language Processing (NLP)

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

78) Choose from the definitions below the best definition of sentiment analysis.

A) Sentiment analysis uses data to show what individuals, systems, and machines are doing in a visual format.

B) Sentiment analysis is an analytics technique that visualizes relationships among participants in a data set to learn about the social structure those relationships create.

C) Sentiment analysis uses NLP to interpret and classify the emotions underneath language in a text format.

D) Sentiment analysis is a technique that gathers, transforms, and visualizes geographic data and imagery.

Diff: 2

Learning Objective: 17.4 Summarize advanced data analytics techniques that transform data into insights.

Section Reference: Natural Language Processing (NLP)

AACSB: None

Bloom's: Application

AICPA: AC: Technology and Tools

79) RAM Manufacturing LLC is a rapidly growing manufacturer of parts for the automotive industry. Maeve recently completed a double major in accounting and information systems at her university. She has completed three of the four sections of the CPA exam and hopes to sit for the fourth next month. Maeve wrote SQL queries during an internship as an internal audit data analyst and is passionate about turning data into useful information that impacts business operations. She wants to know that her work will result in change for her future employer. RAM Manufacturing currently has several job openings posted on LinkedIn, including:

• Data scientist

• Data analyst

• Business analyst

• Data engineer

These positions are specific to the Finance and Accounting departments. Which jobs do you think Maeve should apply for?

Diff: 3

Learning Objective: 17.1 Identify career opportunities for accounting professionals working with data.

Section Reference: Types of Analysts

AACSB: Analytic

Bloom's: Synthesis

AICPA: AC: Technology and Tools

80) RAM Manufacturing LLC is a rapidly growing manufacturer of parts for the automotive industry. Justin recently graduated with a computer science degree from his university. He enjoyed his database courses and interned for a small manufacturing company as a network and database administrator and enjoyed the work. He is really interested in supporting the infrastructure for gathering, growing, and storing data. RAM Manufacturing currently has several job openings posted on LinkedIn, including:

• Data scientist

• Data analyst

• Business analyst

• Data engineer

These positions are specific to the Finance and Accounting departments. Which jobs do you think Justin should apply for?

Diff: 3

Learning Objective: 17.1 Identify career opportunities for accounting professionals working with data.

Section Reference: Types of Analysts

AACSB: Analytic

Bloom's: Synthesis

AICPA: AC: Technology and Tools

81) Abdul is a newly hired Accounting Information Systems (AIS) professor at Big State University. Abdul just administered his first 100-point exam. Abdul has 10 students registered for his AIS course. The scores on his first exam were 99, 89, 88, 88, 84, 84, 83, 81, 81, and 63. What grade(s) in the data set are outliers?

Diff: 2

Learning Objective: 17.2 Describe data analytics techniques that can explore data.

Section Reference: Introduction

AACSB: Analytic

Bloom's: Application

AICPA: AC: Technology and Tools

82) Anomaly detection, also known as outlier analysis, reveals observations or events that are outside a data set's normal behavior. Anomaly detection is an important data analytic objective. Anomaly detection is applicable to every part of a business. Describe how anomaly detection could be used in the marketing department.

Diff: 3

Learning Objective: 17.2 Describe data analytics techniques that can explore data.

Section Reference: Anomaly Detection

AACSB: Analytic

Bloom's: Synthesis

AICPA: AC: Technology and Tools

83) In cluster analysis, the Machine Learning (ML) algorithm finds similarities between the data points to group them together without human intervention. There are many business uses for clustering. Describe an example of clustering in the accounting field.

Diff: 3

Learning Objective: 17.2 Describe data analytics techniques that can explore data.

Section Reference: Clustering and Classification

AACSB: Analytic

Bloom's: Synthesis

AICPA: AC: Technology and Tools

84) In cluster analysis, the Machine Learning (ML) algorithm finds similarities between the data points to group them together without human intervention. There are many business uses for clustering. Describe an example of clustering in the public health field.

Diff: 3

Learning Objective: 17.2 Describe data analytics techniques that can explore data.

Section Reference: Clustering and Classification

AACSB: Analytic

Bloom's: Synthesis

AICPA: AC: Technology and Tools

85) RAM Software, Inc. is an Inventory Management software company. Justin, a CPA, has recently been hired to oversee sales transactions. The CFO of RAM Software is concerned that some of the sales transactions are fraudulent which increase the commissions of the sales personnel. Justin wants to perform linear regression to identify red flags for fraud in the sales data. The company offers greater customer discounts on their software at the end of each quarter, and even greater discounts at the end of each fiscal year.

Justin's hypothesis is that sales transactions recorded at the end of a quarter, and particularly at the end of the year, are at greater risk of fraud. He will use linear regression to determine if the date of the sales transaction in the quarter or year is related to the percent of fraudulent sales transactions. In Justin's analysis, what is the dependent variable and what is the independent variable?

• The date of the sales transaction in the quarter or year, and

• The percent of fraudulent sales transactions

The percent of fraudulent sales transactions is the dependent variable. The date of the sales transaction in the quarter or year is the independent variable.

Diff: 3

Learning Objective: 17.3 Evaluate data analytics techniques that explain changes over time.

Section Reference: Linear Regression

AACSB: Analytic

Bloom's: Synthesis

AICPA: AC: Technology and Tools

86) Monte Carlo simulation is an important simulation in the business world and can be used in many contexts. Give an example of how Monte Carlo simulation could be used in investment firms?

Diff: 3

Learning Objective: 17.3 Evaluate data analytics techniques that explain changes over time.

Section Reference: Monte Carlo Simulation

AACSB: Analytic

Bloom's: Synthesis

AICPA: AC: Technology and Tools

87) Monte Carlo simulation is an important simulation in the business world and can be used in many contexts. Give an example of how Monte Carlo simulation could be used in personal finances?

Diff: 3

Learning Objective: 17.3 Evaluate data analytics techniques that explain changes over time.

Section Reference: Monte Carlo Simulation

AACSB: Analytic

Bloom's: Synthesis

AICPA: AC: Technology and Tools

88) Monte Carlo simulation is an important simulation in the business world and can be used in many contexts. Give an example of how Monte Carlo simulation could be used in project management?

Diff: 3

Learning Objective: 17.3 Evaluate data analytics techniques that explain changes over time.

Section Reference: Monte Carlo Simulation

AACSB: Analytic

Bloom's: Synthesis

AICPA: AC: Technology and Tools

89) Monte Carlo simulation is an important simulation in the business world and can be used in many contexts. Give an example of how Monte Carlo simulation could be used in cost accounting?

Diff: 3

Learning Objective: 17.3 Evaluate data analytics techniques that explain changes over time.

Section Reference: Monte Carlo Simulation

AACSB: Analytic

Bloom's: Synthesis

AICPA: AC: Technology and Tools

90) Any part of the business that captures event data is a candidate for process mining. Give an example of how process mining could be used in investment firms?

Diff: 3

Learning Objective: 17.4 Summarize advanced data analytics techniques that transform data into insights.

Section Reference: Network Analysis

AACSB: Analytic

Bloom's: Synthesis

AICPA: AC: Technology and Tools

91) Any part of the business that captures event data is a candidate for process mining. Give an example of how process mining could be used in journal entries?

Diff: 3

Learning Objective: 17.4 Summarize advanced data analytics techniques that transform data into insights.

Section Reference: Network Analysis

AACSB: Analytic

Bloom's: Synthesis

AICPA: AC: Technology and Tools

92) Any part of the business that captures event data is a candidate for process mining. Give an example of how process mining could be used in Information Technology (IT)?

Diff: 3

Learning Objective: 17.4 Summarize advanced data analytics techniques that transform data into insights.

Section Reference: Network Analysis

AACSB: Analytic

Bloom's: Synthesis

AICPA: AC: Technology and Tools

93) RAM Manufacturing LLC is a rapidly growing manufacturer of parts for the automotive industry. Maeve recently joined RAM Manufacturing and has been asked to review the A/P invoice approval process. Maeve wants to use process mining to identify any deviations in the A/P invoice approval process. Maeve develops the following process mining visualization, which shows the process path for 5 invoices:

What anomalies can Maeve detect using her process mining analytic?

Diff: 3

Learning Objective: 17.4 Summarize advanced data analytics techniques that transform data into insights.

Section Reference: Network Analysis

AACSB: Analytic

Bloom's: Synthesis

AICPA: AC: Technology and Tools

94) Geospatial analytics is a technique that gathers, transforms, and visualizes geographic data and imagery, including satellite photographs, Global Positioning System (GPS) coordinates, and more. Give an example of how geospatial analytics could be used in the delivery industry.

Diff: 3

Learning Objective: 17.4 Summarize advanced data analytics techniques that transform data into insights.

Section Reference: Geospatial Analytics

AACSB: Analytic

Bloom's: Synthesis

AICPA: AC: Technology and Tools

95) Geospatial analytics is a technique that gathers, transforms, and visualizes geographic data and imagery, including satellite photographs, Global Positioning System (GPS) coordinates, and more. Give an example of how geospatial analytics could be used in the banking industry.

Diff: 3

Learning Objective: 17.4 Summarize advanced data analytics techniques that transform data into insights.

Section Reference: Geospatial Analytics

AACSB: Analytic

Bloom's: Synthesis

AICPA: AC: Technology and Tools

96) Natural Language Processing (NLP) is a form of textual analysis that gathers, processes, and interprets meaning from human language. Sentiment analysis is a complex and advanced data analytics technique. Give an example of how natural language processing (NLP) could be used with social media posts.

Diff: 3

Learning Objective: 17.4 Summarize advanced data analytics techniques that transform data into insights.

Section Reference: Natural Language Processing (NLP)

AACSB: Analytic

Bloom's: Synthesis

AICPA: AC: Technology and Tools

97) Natural language processing (NLP) is a form of textual analysis that gathers, processes, and interprets meaning from human language. Sentiment analysis is a complex and advanced data analytics technique. Give an example of how natural language processing (NLP) could be used with board meeting minutes.

Diff: 3

Learning Objective: 17.4 Summarize advanced data analytics techniques that transform data into insights.

Section Reference: Natural Language Processing (NLP)

AACSB: Analytic

Bloom's: Synthesis

AICPA: AC: Technology and Tools

98) Natural Language Processing (NLP) is a form of textual analysis that gathers, processes, and interprets meaning from human language. Sentiment analysis is a complex and advanced data analytics technique. Give an example of how natural language processing (NLP) could be used with employee satisfaction surveys.

Diff: 3

Learning Objective: 17.4 Summarize advanced data analytics techniques that transform data into insights.

Section Reference: Natural Language Processing (NLP)

AACSB: Analytic

Bloom's: Synthesis

AICPA: AC: Technology and Tools

99) Natural Language Processing (NLP) is a form of textual analysis that gathers, processes, and interprets meaning from human language. Sentiment analysis is a complex and advanced data analytics technique. Give an example of how natural language processing (NLP) could be used with auditing documents.

Diff: 3

Learning Objective: 17.4 Summarize advanced data analytics techniques that transform data into insights.

Section Reference: Natural Language Processing (NLP)

AACSB: Analytic

Bloom's: Synthesis

AICPA: AC: Technology and Tools

100) Natural Language Processing (NLP) is a form of textual analysis that gathers, processes, and interprets meaning from human language. Sentiment analysis is a complex and advanced data analytics technique. Give an example of how natural language processing (NLP) could be used with journal entry analysis.

Diff: 3

Learning Objective: 17.4 Summarize advanced data analytics techniques that transform data into insights.

Section Reference: Natural Language Processing (NLP)

AACSB: Analytic

Bloom's: Synthesis

AICPA: AC: Technology and Tools

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