(Test Bank) Data Analytics for Accounting

Richardson, 2

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Test Bank

2nd Edition

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Test Bank for Data Analytics for Accounting, The downloadable file consists of 157 pages, and the total size of the file is 0.99 MB. Purchase now only for highly discounted price.

Author(s): Richardson

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Version 1 1 Chapter 1 Student name:__________ TRUE/FALSE - Write 'T' if the statement is true and 'F' if the statement is false. 1) Data analytics is the process of evaluating data with the purpose of drawing conclusions to address business questions. ⊚ true ⊚ false 2) The process of data analytics aims to transform raw information into data to create value. ⊚ true ⊚ false 3) Data analytics has the potential to transform the manner in which companies run their businesses, however it is not practical in the near future. ⊚ true ⊚ false 4) Auditors can use social media to hear what customers are saying about a company and compare this to inventory obsolescence and other estimates. ⊚ true ⊚ false 5) Data analytics allows auditors to glean insights that are beneficial to the client, without breeching independence. ⊚ true ⊚ false 6) The predictive analytics is an important aspect of data analytics for auditors, but is not applicable for tax accountants.Version 1 2 ⊚ true ⊚ false 7) The I in IMPACT Cycle represents Identify the Question. ⊚ true ⊚ false 8) The M in IMPACT Cycle represents Master the Data. ⊚ true ⊚ false 9) The P in IMPACT Cycle represents Predict the Results. ⊚ true ⊚ false 10) The A in IMPACT Cycle represents Analyze the Data. ⊚ true ⊚ false 11) The C in IMPACT Cycle represents Continuously Track. ⊚ true ⊚ false 12) The T in IMPACT Cycle represents Track Outcomes. ⊚ true ⊚ false 13) The IMPACT cycle is iterative, as insights are gained, outcomes are tracked, and new questions are identified.Version 1 3 ⊚ true ⊚ false 14) Data analytics professionals estimate that they spend between 25 percent and 70 percent of their time cleaning data so it can be analyzed. ⊚ true ⊚ false 15) Data analysis through data manipulation is performing basic analysis to understand the quality of the underlying data and its ability to address the business question. ⊚ true ⊚ false 16) To be proficient in data analysis, accountants need to become data scientists. ⊚ true ⊚ false 17) By developing an analytics mindset, accountants will be able to recognize when and how data analy tics can address business questions. ⊚ true ⊚ false 18) While it is important for accountants to clearly articulate the business problem, drawing appropriate conclusions, based on the data, should be left to statisticians. ⊚ true ⊚ false 19) Analytic -minded accountants should report results of analysis in an accessible way to each varied decision maker and their specific needs.Version 1 4 ⊚ true ⊚ false MULTIPLE CHOICE - Choose the one alternative that bes t completes the statement or answers the question. 20) With a goal to give organizations the information they need to make sound and timely business decisions, data analytics often involves all of the following except : A) technologies. B) statistics. C) growth. D) databases. 21) Patterns discovered from ________ enable businesses to identify opportunities and risks and better plan for ________. A) past archives; the future B) curre nt data; the future C) current data; today D) past archives; today 22) Which of the following best describes the goal of descriptive data analysis: A) recognize what is meant by data quality, be it completeness, reliability or validity B) perform basic analysis to understand the quality of the underlying data and its ability to address the business question C) demonstrate ability to sort, rearrange, merge, and reconfigure data in a manner that allows enhanced analys is D) comprehend the process needed to clean and prepare the data before analysis 23) Which of the following best describes the goal of data quality:Version 1 5 A) recognize what is meant by data quality, be it completeness, reliability or validity B) perform basic analysis to understand the quality of the underlying data and its ability to address the business question C) demonstrate ability to sort, rearrange, merge, and reconfigure data in a manner that allows enha nced analysis D) comprehend the process needed to clean and prepare the data before analysis 24) Which of the following best describes the goal of data manipulation: A) recognize what is meant by data quality, be it completenes s, reliability or validity B) perform basic analysis to understand the quality of the underlying data and its ability to address the business question C) demonstrate ability to sort, rearrange, merge, and reconfigure data in a manner that allows e nhanced analysis D) comprehend the process needed to clean and prepare the data before analysis 25) Which of the following best describes the goal of data scrubbing and data preparation: A) recognize what is meant by data quali ty, be it completeness, reliability or validity B) perform basic analysis to understand the quality of the underlying data and its ability to address the business question C) demonstrate ability to sort, rearrange, merge and reconfigure data in a manner that allows enhanced analysis D) comprehend the process needed to clean and prepare the data before analysis 26) Which of the following best describes the goal of developing an analytics mindset: A) recognize when and h ow data analytics can address business questions B) perform basic analysis to understand the quality of the underlying data and its ability to address the business question C) recognize what is meant by data quality, be it completeness, reliabilit y or validity D) comprehend the process needed to clean and prepare the data before analysis Version 1 6 27) Which of the following best describes the goal of data visualization and data reporting: A) recognize when and how data analytics can address business questions B) perform basic analysis to understand the quality of the underlying data and its ability to address the business question C) recognize what is meant by data quality, be it completeness, reliability or validity D) report results of analysis in an accessible way to each varied decision maker and their specific needs 28) Which of the following best describes the goal of defining and addressing problems through statistical data analysis: A) recognize what is meant by data quality, be it completeness, reliability or validity B) perform basic analysis to understand the quality of the underlying data and its ability to address the business question C) demonstrate ability to sort, rearra nge, merge and reconfigure data in a manner that allows enhanced analysis D) identify and implement an approach that will use statistical data analysis to draw conclusions and make recommendations on a timely basis 29) While accountants d on't need to become data scientists, they must know how to do the following except : A) Clearly articulate the business problem the company is facing B) Communicate with the data scientists about specific data needs and understand the underl ying quality of the data C) Build a data repository D) Comprehend the process needed to clean and prepare the data before analysis 30) Which approach to data analytics attempts to estimate or predict, for each unit, the numerical valu e of some variable using some type of statistical model?Version 1 7 A) Similarity matching. B) Classification. C) Data reduction. D) Regression. 31) Which approach to data analytics attempts to characterize the typical behavior of an individual, group or population by generating summary statistics about the data? A) Similarity matching. B) Profiling. C) Data reduction. D) Regression. 32) Which approach to data analytics attempts to r educe the amount of information that needs to be considered to focus on the most critical items? A) Similarity matching. B) Profiling. C) Data reduction. D) Regression. 33) Which of the following best describes the classification approach to data analytics? A) An attempt to assign each unit (or individual) in a population into a few categories. B) An attempt to identify similar individuals based on data known about them. C) An attemp t to divide individuals into groups in a useful or meaningful way. D) An attempt to discover associations between individuals based on transactions involving them. 34) Which of the following best describes the clustering approach to data analytics?Version 1 8 A) An attempt to assign each unit (or individual) in a population into a few categories. B) An attempt to identify similar individuals based on data known about them. C) An attempt to divide individuals into groups in a usefu l or meaningful way. D) An attempt to discover associations between individuals based on transactions involving them. 35) Which of the following best describes the similarity matching approach to data analytics? A) An attempt t o assign each unit (or individual) in a population into a few categories. B) An attempt to identify similar individuals based on data known about them. C) An attempt to divide individuals into groups in a useful or meaningful way. D) An attemp t to discover associations between individuals based on transactions involving them. 36) Which of the following best describes the regression approach to data analytics? A) An attempt to estimate or predict, for each unit, the nume rical value of some variable using some type of statistical model. B) An attempt to predict a relationship between two data items. C) An attempt to divide individuals into groups in a useful or meaningful way. D) An attempt to discover associa tions between individuals based on transactions involving them. 37) Which of the following best describes the co -occurrence grouping approach to data analytics?Version 1 9 A) An attempt to characterize the typical behavior of an individual, group or population by generating summary statistics about the data. B) An attempt to predict a relationship between two data items. C) An attempt to reduce the amount of information that needs to be considered to focus on the most cri tical items. D) An attempt to discover associations between individuals based on transactions involving them. 38) Which of the following best describes the link prediction approach to data analytics? A) An attempt to characteri ze the typical behavior of an individual, group or population by generating summary statistics about the data. B) An attempt to predict a relationship between two data items. C) An attempt to reduce the amount of information that needs to be consi dered to focus on the most critical items. D) An attempt to discover associations between individuals based on transactions involving them. 39) Which of the following best describes the profiling approach to data analytics? A) An attempt to characterize the typical behavior of an individual, group or population by generating summary statistics about the data. B) An attempt to predict a relationship between two data items. C) An attempt to reduce the amount of informatio n that needs to be considered to focus on the most critical items. D) An attempt to discover associations between individuals based on transactions involving them. 40) Which of the following best describes the data reduction approach to d ata analytics?Version 1 10 A) An attempt to characterize the typical behavior of an individual, group or population by generating summary statistics about the data. B) An attempt to predict a relationship between two data items. C) An attempt to re duce the amount of information that needs to be considered to focus on the most critical items. D) An attempt to discover associations between individuals based on transactions involving them. 41) Which approach to data analytics attempts to discover associations between individuals based on transactions involving them? A) Similarity matching. B) Clustering. C) Co -occurrence grouping. D) Link prediction. 42) Which approach to data analytics attempts to identify similar individuals based on data known about them? A) Similarity matching. B) Clustering. C) Co -occurrence grouping. D) Link prediction. 43) Which approach to data analytics attempts to predict a relationship between two data items? A) Similarity matching. B) Clustering. C) Co -occurrence grouping. D) Link prediction. Version 1 11 44) Which approach to data analytics attempts to divide individuals into groups in a useful or meaningful way? A) Similarity matching. B) Clustering. C) Co -occurrence grouping. D) Link prediction. 45) The IMPACT cycle includes all the following processes except : A) Identify the questions. B) Address and refine results. C) Track outcomes. D) Predict the results. 46) One of the most important aspects of data analytics that impacts tax is: A) predictive analytics. B) co -occurrence grouping. C) simil arity matching. D) data quality. 47) If we are predicting which companies go bankrupt, bankruptcy would be the A) dependent variable B) independent variable C) explanatory variable D) classification variable 48) If a bank uses credit risk score to determine who will receive a loan, the credit risk score would be considered the:Version 1 12 A) dependent variable B) independent variable C) response variable D) classification variable 49) If a bank uses credit risk score to determine who will receive a loan, the variable predicting who will receive a loan would be considered the: A) dependent variable B) independent variable C) determinant vari able D) classification variable 50) The 3V’s of Big Data include all but the following: A) volatility B) variety C) velocity D) volume 51) A recent study from McKinsey Global Institute estimates that Data Analytics could generate up to $3 _______ in value. A) billion B) trillion C) million D) thousand 52) The PwC ’s 6th Annual Digital IQ survey of more than 1.400 leaders from digital business, the area of investment that tops CEOs ’ list of priorities is _____________.Version 1 13 A) information technology B) capital expenditures including hardware and software C) accounting data analytics D) business analytics 53) According to PwC ’s 18th Annual Global CEO survey, _____ percent of chief executive officers put a high value on data analytics. A) 95 B) 85 C) 55 D) 35 54) According to the text, as the debt -to-income ratio increases, there is ____________ chance of a loan getting rejected by the bank. A) a greater B) a lesser C) no effect on the 55) According to the text, as the length of employment increases, there is ____________ chance of a loan getting rejected by the ba nk. A) a greater B) a lesser C) no effect on the 56) According to the text, as the credit score increases, there is ____________ chance of a loan getting rejected by the bank.Version 1 14 A) a greater B) a lesser C) no effect on the ESSAY. Write your answer in the space provided or on a separate sheet of paper. 57) List and describe the eight (8) different approaches to data analytics. 58) List and explain four (4) of the seven (7) data analytic skills needed by analytic -minded accountants: 59) As more and more data is available, some would argue that the role of accounting is changing. While accountants don't need to become data scientists, they must deve lop a base level skill set. Whether they are the Director of Tax for Hewlett Packard or their external auditor, which basic skills are needed by an analytic -minded accountant? 60) What is the regression approach? How might the regre ssion approach be used in auditing?Version 1 15 61) Assume that you have just started a new job as a credit manager for a Fortune 500 company. Using all steps in the IMPACT Cycle, provide examples of tasks that would be performed as part of each step and state the information/data you would need to make a decision if a customer is credit worthy. Version 1 16 Answer Key Test name: ch 1 1) TRUE 2) FALSE 3) FALSE 4) TRUE 5) TRUE 6) FALSE 7) TRUE 8) TRUE 9) FALSE 10) FALSE 11) FALSE 12) TRUE 13) TRUE 14) FALSE 15) FALSE 16) FALSE 17) TRUE 18) FALSE 19) TRUE 20) C 21) A 22) B 23) A 24) C 25) D 26) A Version 1 17 27) D 28) D 29) C 30) D 31) B 32) C 33) A 34) C 35) B 36) A 37) D 38) B 39) A 40) C 41) C 42) A 43) D 44) B 45) D 46) A 47) A 48) B 49) A 50) A 51) B 52) D 53) B 54) A 55) B 56) B Version 1 18 57) Answers may vary slightly! • Classification — An attempt to assign each unit (or individual) in a population into a few categories. • Regression — An attempt to estimate or predict, for each unit, the numerical value of some variable using some type of statistical model. • Similarity matching — An attempt to identify similar individuals based on data known about them. • Clustering — An attempt to divide individuals (like customers) into groups (or clusters) in a useful or meaningful way. • Co-occurrence grouping — An attempt to discover associations between individuals based on transactions involvin g them. • Profiling — An attempt to characterize the "typical" behavior of an individual, group or population by generating summary statistics about the data (including mean, standard deviations, etc.). • Link prediction — An attempt to predict a relatio nship between two data items. • Data reduction — A data approach that attempts to reduce the amount of information that needs to be considered to focus on the most critical items (i.e., highest cost, highest risk, largest IMPACT, etc.). Version 1 19 58) Answers will vary but should include some of these items. • Developing an analytics mindset — recognize when and how data analytics can address business questions. • Data scrubbing and data preparation — comprehend the process needed to clean and prepare the dat a before analysis. • Data quality — recognize what is meant by data quality, be it completeness, reliability or validity. • Descriptive data analysis — perform basic analysis to understand the quality of the underlying data and its ability to address the business question. • Data analysis through data manipulation — demonstrate ability to sort, rearrange, merge and reconfigure data in a manner that allows enhanced analysis. • Defining and addressing problems through statistical data analysis — identify and implement an approach that will use statistical data analysis to draw conclusions and make recommendations on a timely basis. • Data visualization and data reporting — report results of analysis in an accessible way to each varied decision maker and t heir specific needs. Version 1 20 59) Answers will vary! • Clearly articulate the business problem the company is facing, • Communicate with the data scientists about specific data needs and understand the underlying quality of the data. • Draw appropriate conclusions to the business problem based on the data and make recommendations on a timely basis, and • Present their results to individual members of management (CEOs, audit managers, etc.) in an accessible manner to each member. 60) Answers will vary! Regression is an attempt to estimate or predict, for each unit, the numerical value of some variable using some type of statistical model. Given a balance of total accounts receivable held by a client, auditors can use a regres sion analysis to estimate the appropriate level of allowance for doubtful accounts for bad debts. 61) Answers will vary! A potential answer might include: Process: Example or Information need: Identify the Question Who is the customer? What is the credit amount requested? What are the terms of payment? How is their financial performance, relative to their competitors? Master the Data What is their: • Liquidity ratio, • Effectiveness or turnover ratios, Version 1 21 • Financial leverage ratios, • Profitability ratios, • Ratios of debt service. Perform Test Plan Possible approaches • Regression • Profiling • Data reduction Address and Refine Results • Examine correlations • Request additional data • Discuss with colleagues • Rerun analysis Communicate Insights Provide answer (yes/no) to customer. If yes, provide amount of credit limit and payment terms. Track Outcomes • Monitor customer aging and payment trends • Revisit as necessary Version 1 1 Chapter 2 Student name:__________ TRUE/FALSE - Write 'T' if the statement is true and 'F' if the statement is false. 1) Mastering the data requires a firm understanding of what data is available to you and where it is stored, as well as being skilled in the process of extracting, transforming, and loading (ETL). ⊚ true ⊚ false 2) A flat file is a means of storing data in one place, such as in an Excel spreadsheet, as opposed to storing the data in multiple tables, such as in a relational database. ⊚ true ⊚ false 3) A foreign key is an attribute that is required to exist in each table of a relational database and serves as the unique identifier for each record in a table. ⊚ true ⊚ false 4) A primary key is an attribute that is required to exist in each table of a relational database and serves as the unique identif ier for each record in a table. ⊚ true ⊚ false 5) A foreign key is an attribute that exists in relational databases in order to carry out the relationship between two tables. ⊚ true ⊚ false 6) A composite primary key is made up of the three or more primary keys in the tables that it is linking.Version 1 2 ⊚ true ⊚ false 7) Descriptive attributes are attributes that exist in relational databases that are neither primary nor foreign keys. ⊚ true ⊚ false 8) Once you have extracted the data of interest, it will need to be validated for completeness and existence. ⊚ true ⊚ false 9) The M in IMPACT Cycle represents Manipulating the Data. ⊚ true ⊚ false 10) In order to obtain the right data, it is important to have a firm grasp of what data is available and how it is stored. ⊚ true ⊚ false 11) Data normalization can reduce data redundancy and improve data integrity . ⊚ true ⊚ false 12) Much like the IMPACT cycle, requesting data is often an iterative process. ⊚ true ⊚ false Version 1 3 13) Unlike the IMPACT cycle, requesting data is not an iterative process. ⊚ true ⊚ false 14) If the extraction and transformation steps have been done correctly, the loading part of the ETL process should be the simplest step. ⊚ true ⊚ false 15) After obtaining the data and determining the purpose and scope of the data request, the next step is to validate the data. ⊚ true ⊚ false 16) Comparing the number of records that were extracted to the number of records in the source database is an example of validating the data for integrity. ⊚ true ⊚ false 17) Formatting negative numbers is an example of cleaning the data. ⊚ true ⊚ false 18) A template can make communication easier between data requestor and provider. ⊚ true ⊚ false Version 1 4 MULTI PLE CHOICE - Choose the one alternative that best completes the statement or answers the question. 19) Mastering the data can also be described via the ETL process. The ETL process stands for: A) Extract, total, and load data. B) Extract, t ransform, and load data. C) Enter, transform, and load data. D) Enter, total, and load data. 20) All of the following are Audit Data Standards (ADS) developed by the American Institute of Certified Public Accountants except : A) Investments subledger standards B) General Ledger standards C) Procure -to-Pay subledger standards D) Order -to-Cash subledger standards 21) When using [EmployeeID] as the unique identifier of the Employee table, [EmployeeID] is an example of which of the following: A) Foreign key B) Composite key C) Primary key D) Key attribute 22) The objective of data extraction is: A) To validate the data for completeness and integrity B) To load the data into the appropriate tool for analysis C) To identify and obtain the data from the appropriate source D) To identify which approach to data analytics should be used