Ch11 | Data Analytics & Prediction – Test Bank

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Horngren's Cost Accounting: A Managerial Emphasis, 17e, Global Edition by Datar/Rajan

Chapter 11 Data Analytic Thinking and Prediction

Objective 11.1

1) Which of the following best describes data science?

A) Any activity that activity that reveals deeper insight into a dataset.

B) The process of analyzing numerical data to infer conclusions about the whole from those in a representative sample.

C) The use of data analytics to draw conclusions from data.

D) The practice of measuring, analyzing, and interpreting information for managers in pursuit an organization's goals.

Diff: 2

Objective: 1

AACSB: Analytical thinking

2) Which of the following would be the most compelling reasons why data scientists are able to create and train sophisticated algorithms?

A) existence of huge amounts of data

B) inexpensive data storage and Web-based (cloud) computing power

C) inexpensive computers and software

D) networking capabilities and inexpensive servers

Diff: 2

Objective: 1

AACSB: Analytical thinking

3) The ability to accurately predict outcomes can directly impact how an organization develops its:

A) strategy

B) objectives

C) mission

D) plan

Diff: 2

Objective: 1

AACSB: Analytical thinking

4) Data science sits at the intersection of computer science and data skills, math and statistics, and:

A) hardware

B) software

C) networks

D) substantive expertise

Diff: 2

Objective: 1

AACSB: Analytical thinking

5) Management accountants need to understand some of the computer science and statistics tools used in data science so that they:

A) can adapt the accounting information system

B) certify the financial statements with a higher degree of certainty

C) can effectively interact with members of the data science team to create value

D) can comply with generally accepted accounting principles

Diff: 2

Objective: 1

AACSB: Analytical thinking

6) The data science framework is a:

A) four-step decision-making process for applying machine learning techniques to aid decision making

B) two-step process that involves understanding the problem and applying machine learning to aid decision making

C) three-step process for applying machine learning techniques to aid decision making

D) two-step process involving preparing the data and building a model to aid decision making

Diff: 2

Objective: 1

AACSB: Analytical thinking

7) Which of the following is the first step on the data science framework?

A) obtain and explore data

B) understand the problem

C) prepare data

D) build a model

Diff: 2

Objective: 1

AACSB: Analytical thinking

8) Which of the following best describes data analytics?

A) Examining big data and drawing conclusions.

B) Examining raw data and drawing conclusions.

C) Examining data and removing excess "noise" to draw conclusions.

D) Examining big data, removing excess "noise" and organizing the data to draw conclusions.

Diff: 1

Objective: 1

AACSB: Analytical thinking

9) Which of the following actions would address a limiting factor when working with big data in the context of data analytics?

A) utilization of cloud platforms

B) utilization of data analytics software

C) acquisition of state of the art hardware

D) extraction and transformation of large amounts of data.

Diff: 2

Objective: 1

AACSB: Analytical thinking

10) With big data and data analytics techniques, management can discover ________ to identify future opportunity and risks.

A) historical data and decisions

B) cost savings

C) patterns and anomalies

D) opportunity costs

Diff: 2

Objective: 1

AACSB: Analytical thinking

11) The marriage of data science and management accounting can result in in the use of very large datasets to ________ sophisticated algorithmic models.

A) construct

B) formulate

C) exploit

D) train

Diff: 2

Objective: 1

AACSB: Analytical thinking

12) Management accountants need to understand some of the computer science and ________ tools used in data science to work with data analytics in support of management decision making.

A) statistical

B) financial

C) accounting

D) software

Diff: 2

Objective: 1

AACSB: Application of knowledge

13) When management accountants partner with data science professionals to create value for a company, the impact should be:

A) focused on production

B) realized in the supply chain

C) concentrated in the production through distribution segment of the value chain

D) potentially realized across all parts of the value chain

Diff: 2

Objective: 1

AACSB: Application of knowledge

14) Exploratory data analysis could encompass all of the following, EXCEPT:

A) using statistic measures like mean and mode

B) using an Excel model to prepare a forecast

C) embark on activities that reveals deeper insight

D) calculate the difference between the highest and lowest values

Diff: 2

Objective: 1

AACSB: Application of knowledge

15) How does a management accountant gain an understanding of a business problem for which data analytics may help solve?

A) By considering questions that arise from many sources.

B) Knowing everything about the data that he or she anticipates working with.

C) Identifying relationships within the data such as independent and dependent variables.

D) Evaluating the data that can be accessed via exploratory techniques.

Diff: 2

Objective: 1

AACSB: Analytical thinking

16) Which of the following best defines the management accountants' initial data analytics role in facilitating the transformation of data into information that can help add value to an organization?

A) Obtaining and exploring relevant data for decision making.

B) Developing a data exploratory analysis

C) Deciding which questions to ask and what data to gather.

D) Preparing data for analysis.

Diff: 2

Objective: 1

AACSB: Application of knowledge

17) Examining a data set to understand its size and content would be an example of:

A) exploratory data analysis

B) exploratory visualization

C) data dictionary development

D) accessing the potential of data leakage

Diff: 1

Objective: 1

AACSB: Application of knowledge

18) Which of the following would most likely not be the reason for utilizing data analytics?

A) Investigate customer buying patterns.

B) Delve into surprising variances from budget.

C) Forecast future impactful events.

D) Fine-tune direct cost allocations.

Diff: 1

Objective: 1

AACSB: Application of knowledge

19) Exploratory data analysis reveals deeper insight into a dataset.

Diff: 1

Objective: 1

AACSB: Application of knowledge

20) Algorithmic models "learn" from the feedback of experts.

Diff: 1

Objective: 1

AACSB: Application of knowledge

21) Predictive modeling is a data science technique used to make Estimates based on past or current data.

Diff: 1

Objective: 1

AACSB: Application of knowledge

22) A binary outcome implies there are more than two possible outcomes to a certain situation such as a revenue that can produce a profit, a loss, or equal expenses.

Diff: 1

Objective: 1

AACSB: Application of knowledge

23) Calculation of a mean, median and mode on a new data set could be an activity of an overall exploratory analysis as part of a management accountant's role of obtaining and exploring relevant data while working a data analytics process.

Diff: 2

Objective: 1

AACSB: Analytical thinking

24) Numeric analysis is a type of exploratory data analysis.

Diff: 1

Objective: 1

AACSB: Application of knowledge

25) Define exploratory data analysis and give some examples of exploratory data analysis.

Diff: 2

Objective: 1

AACSB: Analytical thinking

26) When data science is depicted at the center of a Venn diagram, the intersecting circles are representative of such things as computer science skill, math and statistics, and substantive knowledge. Discuss what is meant by substantive knowledge (domain knowledge) and why it is a critical piece data analytics as a decision making tool.

Diff: 2

Objective: 1

AACSB: Application of knowledge

27) Explain what is meant by the following statement: "Data analytics allows accountants to assist managers to work with data that is too large and possibly too complex for traditional systems and tools to extract value."

Diff: 2

Objective: 1

AACSB: Application of knowledge

Objective 11.2

1) Which of the following is the proper order (sequence) of the steps in the data science framework?

A = Visualize and communicate insights

B = Deploy the model.

C = Prepare data

D = Obtain and explore data

E = Build a model

F = Gain a business understanding of the problem

G = Evaluate the model

A) E,C, G,F,D,B,A

B) B,D,F,E,C,A,G

C) F,D,C,E,G,A,B

D) F,C,D,G,A,B,E

Diff: 2

Objective: 2

AACSB: Application of knowledge

2) Which of the following would be first and foremost question asked when working within the data science framework?

A) How objective is the data?

B) Can careful and accurate measurement be attained?

C) What might we learn?

D) What are the relevant cost of gathering and analyzing the data?

Diff: 2

Objective: 2

AACSB: Application of knowledge

3) An a priori reason to conduct data analytics would be:

A) thousands of rejected loan applications may reveal low credit scores as the main reason for rejected credit card applications

B) after data is entered into a statistical software package, the numbers reveal a correlation between a loan purpose, noted on thousands of loan applications, and the decision to reject a loan

C) same facts as part but further evidence indicates causation

D) experience seems to indicate that certain phrasing of a loan's applications purpose leads to the rejection of the loan proposal

Diff: 2

Objective: 2

AACSB: Application of knowledge

4) In which phase of the data science framework might data issues such as access, availability, reliability, and timeliness be a consideration?

A) gain understanding of the problem

B) obtain and explore data

C) prepare data

D) visualize insights

Diff: 2

Objective: 2

AACSB: Analytical thinking

5) Which one of the following questions would NOT be asked while performing Step 3: Prepare the Data?

A) What additional data might be needed?

B) How should different variables be measured?

C) What variables should be excluded?

D) How objective is the data?

Diff: 2

Objective: 2

AACSB: Analytical thinking

6) It could be said that auditors have greater confidence when testing for fraud because data analytics enables the analysis of complete:

A) sample

B) datasets

C) value chains

D) transactions

Diff: 2

Objective: 2

AACSB: Analytical thinking

7) Which of the following is the best example of the use of substantive expertise in the "Prepare the Data" step of the data science framework?

A) knowing how the statistical concepts of mean, mode, and median help explain the data

B) understanding how variance and standard deviation are calculated

C) referring to the data dictionary of the data base to understand the concept of annual income and to see if it aligns well with U.S. GAAP

D) understanding the dataset structure of a general ledger database

Diff: 2

Objective: 2

AACSB: Analytical thinking

8) Correcting inconsistencies across the dataset is an example of which of the following?

A) assure that the data is complete

B) validating that the data is objective

C) scrubbing the data

D) extracting the data

Diff: 2

Objective: 2

AACSB: Application of knowledge

9) In the data analytics world, Web scraping usually primarily involves the:

A) process of "cleaning" data before analyzing it

B) analysis of large amounts of complex data derived from the web

C) process of copying data from the web and storing it for retrieval or analysis

D) activities involving scouring the web for information that will help validate internally generated data

Diff: 2

Objective: 2

AACSB: Application of knowledge

10) Which of the following best summarizes the most series a legal or ethical issue faced by management accountants when practicing web scrapping?

A) Scrapping bots might replacing humans and bots lack the qualitative judgements needed to perform this type of data gathering.

B) Web scrapping may violate the terms of use of a particular target web site.

C) Authorization of all web scrapping by an accountant violates specific professional ethical standards.

D) Web scraping processes significantly affect the performance bandwidth of the accountant's company's web server.

Diff: 2

Objective: 2

AACSB: Application of knowledge

11) Management accountants could include many independent features in an analytical model because with technology, it is easy and inexpensive to handle large quantities of data.

Diff: 2

Objective: 2

AACSB: Analytical thinking

12) Data dictionary is information describing the contents, format, and structure of a database and the relationship between its elements.

Diff: 1

Objective: 2

AACSB: Application of knowledge

13) In carrying out the data science framework, data leakage happens when some data is inappropriately disclosed to external parties.

Diff: 1

Objective: 2

AACSB: Application of knowledge

14) A listing of descriptions of the data attributes of a dataset is called a data index and is a reference for both data base administrators and analysts who are building decision support models.

Diff: 1

Objective: 2

AACSB: Application of knowledge

15) Briefly describe what is meant by gaining an understanding of a business problem within the framework of data analytics.

Diff: 2

Objective: 2

AACSB: Application of knowledge

16) A management accountant may be heavily involved in obtaining, exploring, and preparing data for further analysis. Explain what is meant by assuring that the data is relevant and clean.

Diff: 2

Objective: 2

AACSB: Application of knowledge

17) Contrast these two concepts: scrubbing data versus assuring data quality.

Diff: 2

Objective: 2

AACSB: Application of knowledge

Objective 11.3

1) A decision tree would most likely be utilized during which of the following steps?

A) Step 1: Gain a Business Understanding of the Problem

B) Step 2: Obtain and Explore Relevant Data

C) Step 3: Prepare the Data

D) Step 4 Building the Model

Diff: 2

Objective: 3

AACSB: Application of knowledge

2) All of the following describes a decision tree EXCEPT:

A) It is an information flowchart.

B) It is a type of decision model showing possible consequences.

C) It can be "learned" by splitting the source set into subsets by using an algorithm.

D) It is a model that generates very complex rules and perform classifications via numerous and elaborate computations.

Diff: 2

Objective: 3

AACSB: Application of knowledge

3) In constructing a decision tree, which of the following best describes splits?

A) Splits are separate decision trees.

B) Splits are the roots of the decision tree.

C) Splits (branches) are made which result in most homogeneous sub-nodes.

D) Splits are removals of sub-nodes of a decision node as the result of running an algorithm.

Diff: 2

Objective: 3

AACSB: Application of knowledge

4) Strengths of a decision tree model include all of the following except:

A) Save time since only one iteration of the decision tree is necessary.

B) It is a visual representation that users can related to.

C) Can be used to generate understandable rules.

D) They are easily interpretable as a set of questions or business rules.

Diff: 2

Objective: 3

AACSB: Application of knowledge

5) A decision tree is simply a set of cascading:

A) database

B) questions

C) costs

D) revenues

Diff: 2

Objective: 3

AACSB: Application of knowledge

6) In a decision tree, which of the following are indicated by circles?

A) decision nodes

B) leafs

C) cuts

D) terminal nodes

Diff: 2

Objective: 3

AACSB: Application of knowledge

7) A "cut" of a scatter plot created from a decision tree exercise creates a rectangle where decision A is made 12 times and decision B is made 16 times and results in a Gini Impurity of:

A) .4

B) 1

C) .48

D) .6

Diff: 2

Objective: 3

AACSB: Analytical thinking

8) If the first "cut" of a scatter plot results in a rectangle with a Gini Impurity measure is .4 and an algorithm chooses a second "cut" of the same plot results in a Gini Impurity of .32, it could be said that:

A) the second cut has more purity than the first cut

B) the first cut has more purity than the second cut

C) the first cut of .4 means there is a 40% chance of a particular occurrence

D) the second cut of .32 means that there is a 72% chance of a particular occurrence

Diff: 2

Objective: 3

AACSB: Analytical thinking

9) Which of the following attempts to assign each unit in a dataset into a small set of categories?

A) classification

B) regression

C) similarity matching

D) pruning

Diff: 2

Objective: 3

AACSB: Analytical thinking

10) A target variable is a value to be predicted by a model that utilizes feature variables.

Diff: 2

Objective: 3

AACSB: Application of knowledge

11) A logistic regression model to estimate the relationship between independent feature variables and the target variable would result in a straight-line fit through a through a scatter plot of data points.

Diff: 2

Objective: 3

AACSB: Analytical thinking

12) A functional relationship describes precisely how two variables relate.

Diff: 2

Objective: 3

AACSB: Application of knowledge

13) When building a decision tree, you use each attribute to answer a question. The answer to each question decides the next question.

Diff: 2

Objective: 3

AACSB: Application of knowledge

14) Decision trees are a complex, but powerful form of multiple variable analysis and supplement, complement, or substitute for traditional statistical forms of analysis, such as multiple linear regression.

Diff: 2

Objective: 3

AACSB: Application of knowledge

15) Gini Impurity is the probability of incorrectly classifying a randomly chosen element in the dataset if it were randomly labeled according to the class distribution in the dataset.

Diff: 2

Objective: 3

AACSB: Application of knowledge

16) What is the connection between algorithms and decision trees?

Diff: 2

Objective: 3

AACSB: Application of knowledge

17) Explain Gini impurity and what it means if a dataset is mixed.

Diff: 2

Objective: 3

AACSB: Application of knowledge

18) A management accountant is working with data science professionals to develop a decision tree to create predictive analysis of accounts receivable write-offs. In speaking with the management science experts, discuss your knowledge as a domain expertise to help define the functional form of the relationship between feature variables and the target variable.

Diff: 2

Objective: 3

AACSB: Application of knowledge

Objective 11.4

1) In general, the more complex the model, the greater the chance of:

A) overfitting the data

B) underfitting the data

C) pruning the data

D) needing to reduce the amount of data considered

Diff: 1

Objective: 4

AACSB: Application of knowledge

2) Overfitting results in which of the following?

A) information gain

B) increased accuracy

C) increased purity

D) noise capture

Diff: 1

Objective: 4

AACSB: Application of knowledge

3) A solution to overfitting is:

A) iteration

B) pruning

C) underfitting

D) algorithm

Diff: 1

Objective: 4

AACSB: Application of knowledge

4) To choose among models and to decide where to prune, data scientists ________ the model to assess the predictive performance of the mode

A) overfit

B) replicate

C) train

D) cross-validate

Diff: 2

Objective: 4

AACSB: Application of knowledge

5) Pruning to a decision tree is done to:

A) reduce complexity

B) improve predictions

C) shrink a dataset

D) diminish data leakage

Diff: 2

Objective: 4

AACSB: Application of knowledge

6) Refining a decision tree model means to:

A) reduce its complexity

B) increase its complexity

C) decrease its cost

D) ensure the data represents the business context

Diff: 2

Objective: 4

AACSB: Application of knowledge

7) The decision tree is a technique for segmenting the target variable into different ________ based on a set of rules.

A) regions

B) databases

C) datasets

D) cost pools

Diff: 2

Objective: 4

AACSB: Application of knowledge

8) Pruning a decision tree will sharpen the model's predictive powers.

Diff: 1

Objective: 4

AACSB: Application of knowledge

9) Overfitting will most likely increase the predictive power of a model.

Diff: 1

Objective: 4

AACSB: Application of knowledge

10) The decision tree algorithm simply prepares a linear regression.

Diff: 2

Objective: 4

AACSB: Application of knowledge

11) Reading a decision tree involves a series of "If-then-else" statements.

Diff: 2

Objective: 4

AACSB: Application of knowledge

12) One disadvantage of a decision tree is its inflexibility.

Diff: 2

Objective: 4

AACSB: Application of knowledge

13) Overfitting is addressed when a model doesn't match the specific details of a dataset too closely and therefore limits its predictive powers.

Diff: 2

Objective: 4

AACSB: Application of knowledge

14) Decision trees are used to divide data into smaller groups by splitting the data at each branch into two or more groups. However, after splitting the data, the tree may need to be pruned. Briefly describe the pruning process and the benefits of pruning.

Diff: 2

Objective: 4

AACSB: Application of knowledge

15) Explain what us meant by recursive partitioning of an algorithm that is seeking to reduce Gini impurity.

Diff: 2

Objective: 4

AACSB: Application of knowledge

Objective 11.5

1) Cross-validation is the process of:

A) comparing predictions of different models on a new set of data for which the actual outcomes are already known

B) comparing predictions of different models on a new set of data for which the actual outcomes are not yet known

C) comparing actual results to predictions to determine significant variances

D) comparing information on source documents to a trail of information

Diff: 2

Objective: 5

AACSB: Analytical thinking

2) The main point of cross validation is that it:

A) tells you to prune the tree

B) specifies where to

C) gives you an estimate of the performance of your trained model

D) calculates an impurity factor of your trained model when used on different data

Diff: 2

Objective: 5

AACSB: Analytical thinking

3) All of the following are true regarding full versus pruned decision trees EXCEPT:

A) pruning is a technique in machine learning that reduces the size of decision trees

B) pruning removing sections of the tree that provide little power to classify instances

C) pruning reduces the complexity

D) pruning increases overfitting and therefore increases accuracy

Diff: 2

Objective: 5

AACSB: Analytical thinking

4) Comparing the performance of a full decision tree to a pruned one could be done via all of the following approaches EXCEPT:

A) cross-validation using prediction accuracy

B) cross-validation using maximum likelihood value

C) using the present value of likely results

D) using a technique called testing holdout sample

Diff: 2

Objective: 5

AACSB: Analytical thinking

5) Which of the following techniques compares the performance of a full decision tree to its pruned version by utilizing probabilities?

A) cross-validation using prediction accuracy

B) cross-validation using maximum likelihood value

C) A and B both use probabilities

D) using a technique called testing holdout sample

Diff: 2

Objective: 5

AACSB: Analytical thinking

6) Machine learning utilizes algorithms that can:

A) learn from training data

B) provide data scientists with rules of thumb to guide their pruning

C) consistently produce zero bias

D) automatically produce hyperparameters

Diff: 2

Objective: 5

AACSB: Analytical thinking

7) Which of the following is true except:

A) The more complex the model, the lower the bias.

B) The less complex the model, the higher the bias.

C) The less complex the model the lower the variance.

D) The more complex the model the lower the variance.

Diff: 2

Objective: 5

AACSB: Analytical thinking

8) Cross-validation techniques could be used to test prediction accuracy.

Diff: 1

Objective: 5

AACSB: Analytical thinking

9) Cross-validation is used to choose between full and pruned decision trees to improve the prediction accuracy of a model.

Diff: 2

Objective: 5

AACSB: Analytical thinking

10) Cross Validation techniques allows you to alternate between training and testing an algorithm.

Diff: 2

Objective: 5

AACSB: Analytical thinking

11) The goal is to build a fully grown decision tree as that version will be the most accurate.

Diff: 1

Objective: 5

AACSB: Analytical thinking

12) A feedback loop occurs when predicted outputs are reused to train new versions of the model.

Diff: 1

Objective: 5

AACSB: Analytical thinking

13) The benefit of pruning is that it avoids overfitting the model to noise.

Diff: 2

Objective: 5

AACSB: Analytical thinking

14) A hyperparameter is a parameter that can be learned by running the model.

Diff: 2

Objective: 5

AACSB: Analytical thinking

15) The more complex the model, the higher the bias and the lower the variance.

Diff: 2

Objective: 5

AACSB: Analytical thinking

16) When a management accountant and data scientist work with large data sets, can the management accountant rely on data scientists, statistics, and algorithms to make such decisions as to how large to grow a decision tree or if additional pruning is necessary? Explain.

Diff: 2

Objective: 5

AACSB: Analytical thinking

17) Explain the difference between training data sets and test data sets?

Diff: 2

Objective: 5

AACSB: Analytical thinking

18) How might classification be used in building a model, to assist a company's credit granting decision making process (approving or denying) when a potential customer requests a significant line of credit to purchase goods on account?

Diff: 2

Objective: 5

AACSB: Analytical thinking

Objective 11.6

1) Management accountants use their knowledge of account, finance, and general business to judge if the ________ used to make predictions make economic sense.

A) feature variables

B) target variables

C) variances

D) impurities

Diff: 2

Objective: 6

AACSB: Analytical thinking

2) The ________ plots the false positive rate on the x-axis and the true positive rate on the y-axis.

A) confusion matrix

B) gini impurity

C) Receiver-Operating-Characteristic (ROC) Curve

D) hyperparameter

Diff: 2

Objective: 6

AACSB: Analytical thinking

3) All of the following are the of plotting of the Receiver-Operating-Characteristic (ROC) curve except:

A) plots the false positive rate on the x-axis

B) plots the true positive rate on the y-axis

C) the closer the curve comes to a 45-degree diagonal the less accurate the test

D) the closer the curve comes to a 45-degree diagonal the more accurate the test

Diff: 2

Objective: 6

AACSB: Analytical thinking

4) Which of the following can be said about a ROC curve?

A) The more accurate the predictions of a model are, the closer the ROC curve will be to a 45-degree diagonal line on the chart.

B) The more accurate the predictions of a model are, the closer the ROC curve will go up along the y-axis on and then move horizontally across the top of the chart.

C) It only plots the false positives.

D) The further the curve away from the left-hand border of the chart and the further it is from the top border of the ROC space, the more accurate the test.

Diff: 2

Objective: 6

AACSB: Analytical thinking

5) A confusion matrix is a:

A) line plot that allows interpretation of the performance of an algorithm

B) bar chart that allows interpretation of the performance of management

C) table that allows visualization of the performance of an algorithm

D) table that allows visualization of the performance of a management

Diff: 1

Objective: 6

AACSB: Analytical thinking

6) Both the visualization of the insights of data science models can be achieved by which of the following tools?

A) decision tree and ROC curve

B) confusion matrix and Gini impurity

C) confusion matrix and hyperparameter

D) partitions and overfitting

Diff: 1

Objective: 6

AACSB: Analytical thinking

7) Which of the following help managers visualize performance of a model by identifying tradeoffs between false positives and true positives?

A) Confusion Matrix and Decision Tree

B) ROC Curve and Confusion Matrix

C) Decision Tree and ROC Curve

D) Decision Tree and Gini Impurity

Diff: 2

Objective: 6

AACSB: Analytical thinking

8) Management accountants must determine if the results of a model make intuitive sense and reflect underlying economic reality.

Diff: 1

Objective: 6

AACSB: Analytical thinking

9) The Receiver-Operating-Characteristic curve plots the true positive rate on the x-axis and the false positive rate on the y-axis.

Diff: 2

Objective: 6

AACSB: Analytical thinking

10) The closer Receiver-Operating-Characteristic (ROC) curve comes to the 45-degree diagonal of the ROC space (chart), the less accurate the test.

Diff: 2

Objective: 6

AACSB: Analytical thinking

11) A confusion matrix shows just the predicted classifications at a given threshold value.

Diff: 1

Objective: 6

AACSB: Analytical thinking

12) The image of visualization of a decision tree is a series of decision nodes and connecting lines.

Diff: 1

Objective: 6

AACSB: Analytical thinking

13) Both a ROC Curve and a decision tree can help a manager visualize the performance of a model by identifying the tradeoff between false positives and true positives.

Diff: 2

Objective: 6

AACSB: Analytical thinking

14) Describe the purpose the Receiver-Operating-Characteristic (ROC) curve and briefly explain how the curve is interpreted.

Diff: 2

Objective: 6

AACSB: Analytical thinking

15) Data analytics can provide results that provide valuable insights. Explain how data visualization may add more value to data analytics.

Diff: 2

Objective: 6

AACSB: Analytical thinking

Objective 11.7

1) To ________ a data science mode, management accountants must balance quantitative and qualitative assessments.

A) operationalize

B) evaluate

C) judge

D) construct

Diff: 2

Objective: 7

AACSB: Analytical thinking

2) Which of the following is the most important judgement to be made when deploying a model?

A) The model is powerful enough to handle less than perfect data.

B) The data is reasonably adequate and accurate.

C) All data must be verified as accurate.

D) The volume of data is adequate

Diff: 2

Objective: 7

AACSB: Analytical thinking

3) Deployment of a data science model is:

A) working with the model to understand its potential

B) applying the model for prediction using a new data

C) training the model

D) evaluating the magnitude of likelihood values and feature variables

Diff: 2

Objective: 7

AACSB: Analytical thinking

4) The concept of deployment in data science refers to the application of a model for prediction using a new data.

Diff: 2

Objective: 7

AACSB: Analytical thinking

5) How can accountants work to operationalize a data science model to be used to support decision making?

Diff: 2

Objective: 7

AACSB: Analytical thinking

6) Explain the connection that data analytics creates between data, information, and knowledge.

Diff: 2

Objective: 7

AACSB: Analytical thinking

7) Explain how Is the deployment of a data science model and the use of big data and its related technologies can be both an opportunity and a threat to the management accounting profession as business partners.

Data analytics can also help enhance an organization's risk management activities. These technologies do present a risk to management accountants as machine learning, AI, and algorithms could replace human accountants however, management accounting can progress to higher value-added activities by using and exploiting the power of big data. For example, management accountants have a background of working with source data and financial information and can help establish rules to assure high levels of trust in the quality and source of the data. There are also data concerns from a regulatory risk angle that management accountants should help mitigate around issues such as data privacy.

Diff: 2

Objective: 7

AACSB: Analytical thinking