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MIDTERM (Chap

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MIDTERM (Chap. 1, 2, 3, 4 of the DA textbook)

 

Part 1: Multiple choice questions (MCQs) – 15 questions by 4 points each.

 

1-The purpose of transforming data is to:

1. To load the data into the appropriate tool for analysis.
2. To validate the data for completeness and integrity.
3. To obtain the data from the appropriate source.
4. To identify which data are necessary to complete the analysis.

2-Mastering the data can also be described via the ETL process. The ETL process stands for:

1. Extract, transform, and load data.
2. Enter, total, and load data.
3. Extract, total, and load data.
4. Enter, total, and load data.

3-What are attributes that exist in a relational database that are neither primary nor foreign keys?

1. Nondescript attributes
2. Descriptive attributes
3. Relational table attributes
4. Composite key

4-Why is Supplier ID considered to be a primary key for a Supplier table?

1. It is a 10-digit number.
2. It can either be for a vendor or miscellaneous provider.
3. It is used to identify different supplier categories.
4. It contains a unique identifier for each supplier.

5-In general, the more complex the model, the greater the chance of:

1. Overfitting the data.
2. Underfitting the data.
3. Pruning the data.
4. The need to reduce the amount of data considered.

 

6- _______________ mark(s) the split between one class and another.

       a) Decision trees;

       b) Decision boundaries;

       c) Linear classifiers;

       d) Identified questions.

 

7- _____________ is a set of data used to assess the degree and strength of a predicted relationship.

       a) Structured data;

       b) Training data;

       c) Test data;

       d) Unstructured data.

 

8-The observation that the frequency of leading digits in many real-life sets of numerical data is called:

        a) Leading digits hypothesis;

        b) Moore’s law;

        c) Clustering;

        d) Benford’s law.

 

9-Gold, silver, and bronze medals would be examples of:

        a) Ordinal data;

         b) Nominal data;

         c) Test data;

         d) Structured data.

 

10-Line charts are not recommended for what type of data?

         a) Normalized data;

         b) Continuous data;

         c) Trend lines;

         d) Qualitative data.

 

11- _____________ data would be considered the least sophisticated type of data.

        a) Nominal;

        b) Interval;

        c) Ordinal;

        d) Ratio.

 

12-In the late 1960s, Ed Altman developed a model to predict if a company was at severe risk of going bankrupt. He called his statistic Altman’s Z-score, now a widely used score in finance. Based on the name of the statistic, which statistical distribution would you guess this came from?

       a) Standardized normal distribution;

       b) Normal distribution;

       c) Poisson distribution;

       d) Uniform distribution.

13-Big Data is often described by the three Vs of:

1. Volume, velocity, and validity
2. Volume, velocity, and variety
3. Volume, volatility, and variability
4. Variability, velocity, and variety

14-Which approach to data analytics attempts to predict relationship between two data items?

1. Profiling
2. Link prediction
3. Classification
4. Regression

15-Which of these is defined as being a central repository of descriptions for all the data attributes of the dataset?

1. Big Data
2. Data warehouse
3. Data dictionary
4. Data analytics

 

Part 2: Answers to short questions – 4 questions by 10 points max. each

Please answer each of the four questions below with as many sentences as you judge appropriate.

Question 1: What methods in business and accounting research do you know about? Which research method(s) would be the most appropriate for exploring consumers’ perceptions of data security and confidentiality? Why? Which research methods would be the least appropriate? Why?  

Question 2: Please expand on the meaning of the “M” in the IMPACT model presented in the Data Analytics for Accounting textbook and give some examples.

Question 3: What is the difference between a supervised and an unsupervised approach?

Question 4: Box and whisker plots (or box plots) are particularly useful when showing extreme observations and data outliers. In what situations in accounting would it be important to communicate these data to a reader? Any accounts on the balance sheet or income statement that may be involved?