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Data Analytics for ACCT > Exam 2 - Chapter 5 - Perform/Types of D.A. > Flashcards

Exam 2 - Chapter 5 - Perform/Types of D.A. Flashcards

1
Q

AMPS model;

What does the P represent?

A

Perform the analysis;

Process of evaluating data through analyses to draw conclusions.

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2
Q

What are the four types of accounting data analytics?

A
  1. Descriptive Analytics: What happened/is happening?
  2. Diagnostic Analytics: Why did it happen? Why past results?
  3. Predictive Analytics: Will it happen in the future?
  4. Prescriptive analytics; What should we do/How do we optimize our performance?
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3
Q

Describe descriptive analytics

A

Analytics performed which characterizes, summarizes, and organizes features and properties of data for better understanding.

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4
Q
  • Counts
  • totals
  • sums
  • averages
  • subtotals
  • minimums and maximums
  • standard deviations
  • ratio analytics

What do these analytics have in common?

A

They are all examples of descriptive analytics

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5
Q

What are the two main tools to describe descriptive analytics?

A
  • Excel: Pivot Tables and D.A.
  • Histograms
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6
Q

Describe diagnostic analytics:

A

Analytics performed to investigate the underlying cause that cannot be answered by just looking at descriptive data.

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7
Q

What are the two categories of diagnostic analytics?

A
  • Identify anomalies/outliers
  • Finding previously unknown linkages, patterns, and relationships
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8
Q

What is the first step of diagnostic analytics?

  • What type of analytics is often done at this stage?
A

Look for and idenitfy unusual, unexpected results/transactions

  • Type of D.A.:

Benford’s law

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9
Q

What is Benford’s law?

Explain it.

A

Type of anlytics to find anomalies/outliers.

In any group of large, randomly produced numbers, there are 9 sets of expected distributions.

  • Group 1 has highest density of numbers
  • Each successive group has less numbers, down to 9.
  • Any numbers outside these 9 groups is an anomaly.
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10
Q
  • controls testing
  • Excel match() commands
  • conditional formatting (to highlight duplicate or unique values)
  • graphing and charting (outlier analytics)
  • minimums and maximums (to find extreme values)
  • fuzzy matching
  • data sorts

What do these examples all have in common?

A

They are all statistical tests for diangostic analytics.

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11
Q

When using diagnostic analytics to find linkages, how might drill down analytics be used?

A

To uncover additional details by summarizing data at different levels to identify potential correlations between variables.

Example:

Sum accounts receivable by customer and sort highest to lowest to determine uncollectibles.

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12
Q

What are some tests in diagnostic analytics for finding correlations/patterns?

A
  • Drill-down
  • Cross-tabulation
  • Correlation
  • Regression
  • Hypothesis testing
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13
Q

How might correlations and regression tests be used in diagnostic analytics?

A

Correlation: assess relationships between two variables. (adv. exp & sales rev.)

Regression: Assess relationship amongst multiple variables (R&D exp, Capital Exp, and NI)

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14
Q

Desrcibe predicitive analytics:

A

Analytics performed to provide foresight by i_dentifying patterns in historical data and assessing future probability._

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15
Q

What are the three categories of prescriptive analytics?

A
  • Classification: Separate and classify a sample into two or more groups
  • Regression: Predict dependent variable values based on independent variable
  • Forecasting using time series analytics: Predict future values based on past
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16
Q

Describe Prescriptive Analytics:

A

Inquiry performed to identify the best possible options given constraints and changing conditions.

17
Q

What are the five categories of prescriptive analytics?

A
  1. Sensitivity Analytics
  2. Capital Budgeting
  3. Marginal analytics
  4. Goal-seek analytics
  5. What-if scenario analytics
18
Q

Prescriptive analytics:

What are sensitivity analytics?

A

Evaluating outcomes based on multiple possible inputs.

(how sensitive outcomes are to input changes)

Example:

What happens to Bank of America profits if
the interest rates change in the future from
3% to 4% or 5% or more?

19
Q

Prescriptive Analytics:

What is capital budgeting?

A

Evaluating future cash flows of capital investments to determine best purchase.

Example

Which assets should we invest in at
Boeing, a new piece of equipment or a
new computer system?

20
Q

Prescriptive Analytics:

What is marginal analytics?

A

Determines the change in profit associated with cost/benefit of additional units.

Question example

Should Tesla make or buy its batteries?

21
Q

Prescriptive Analytics:

What is goal seek analytics?

A

Form of what if analytics; determines what must be done in order to reach desired result.

Question Example

For McGraw-Hill, what are the needed
sales to break even on sales of this text?

22
Q

Prescriptive Analytics:

What is what-if scenario analytics?

A

Analytics of potential future events by considering potential outcomes

Question example

How do we respond to various scenarios
of increased tariffs with China?

23
Q

Describe the difference in:

  • Population
  • Sample
  • Parameter
  • Statistic
A

Population:

  • group of phenomenon having something in common.
  • Characteristic data collected on population is called parameter

Sample

  • Subset of members of a population selected to represent population
  • Data characteristics collected on samples are called statistics
24
Q

What are probability distributions?

What is a normal distribution?

A

Function that describes the possible values of random variables and the likelihood that variable will be within a given range.

Normal Distribution

Bell-shaped probability distribution that is symmetric about its mean

25
Q

What does the z-score tell about a data point?

A

How many standard deviations (σ) a data point is from its population mean (μ)

26
Q

What are the differences in:

  • Uniform Distribution
  • Poisson Distribution
A

Uniform Distribution

Distribution where all outcomes are equally likely (like a coin toos)

Poisson Distribution

  • Mean number of events per interval of time
  • Low mean and highly skewed to right

Data Analytics for ACCT (10 decks)

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