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CMA Exam Part 1 > P1.F.4.3 Data Analytics - Analytic Tools > Flashcards

P1.F.4.3 Data Analytics - Analytic Tools Flashcards

1
Q

Type of Data Analytics

P1.F.4.3 Data Analytics - Analytic Tools

A
  1. Descriptive: Who, what, when and where?
  2. Diagnostic: Why something happened?
  3. Predictive: What will happen? (forecast)
  4. Prescriptive: What should happen? Greatest value because its exercises can lead to decisions that can create value.
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2
Q

Predictive Analytic Techniques

P1.F.4.3 Data Analytics - Analytic Tools

A
  1. Find exploratory variables that correlate to dependent variable.
    Example: calculate regression equations
  2. Decide which data to include or exclude
    Example: outlier: outside the norm.
  3. Derive regression line supported by backtesting
  4. Validating the fit: split into two groups; one to derive and one to test
  5. Compare with other models
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3
Q

Exploratory Data Analysis

P1.F.4.3 Data Analytics - Analytic Tools

A
  1. An exercise undertaken without an existing hypothesis regarding the data.
  2. Goal is to find a new and useful relationship among variables
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4
Q

Limitations of Data Analytics

P1.F.4.3 Data Analytics - Analytic Tools

A
  1. Doesn’t explain causation or address motives
  2. Lacks qualitative measures
  3. May encourage transactional focus instead of relationships
  4. Doesn’t lead to perfect decisions
  5. Confirmation bias must be overcome
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5
Q

Data Analytic Model Challenges

P1.F.4.3 Data Analytics - Analytic Tools

A
  1. Will never reconcile exactly
  2. Employing the right level of detail
  3. Increasing variables increase costs and complexity
  4. Randomness always seems present
  5. Choosing and sampling population
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6
Q

Data Analytic Model Types

P1.F.4.3 Data Analytics - Analytic Tools

A
  1. Clustering: define variables and visually displays them
  2. Classification: puts observations into categories
  3. Regression: study of relationships among variables
  4. Multiple regression: more than one explanatory variable
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7
Q

Sensitivity Analysis

P1.F.4.3 Data Analytics - Analytic Tools

A
  1. Refers to the degree to which changes in input variables affect output.
  2. Shows which variable are critical and how to measure them.
  3. Demonstrates overall quality and data sufficiency
  4. Models should be built to accommodate
  5. End results demonstrates model trustworthiness
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8
Q

Sensitivity Analysis Benefits & Limitations

P1.F.4.3 Data Analytics - Analytic Tools

A

Benefits

  1. Demonstrates model veracity
  2. Spotlights important variables to control

Limitations

  1. Only shows what to discard
  2. Overhead cost that doesn’t add to the value chain
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9
Q

Simulation Models

P1.F.4.3 Data Analytics - Analytic Tools

A
  1. Systematic way of dealing with uncertainty
  2. Repeatedly test model with randomized inputs
  3. Demonstrates range and probability of outputs
  4. Vast applications
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10
Q

Simulation Model Benefits & Limitations

P1.F.4.3 Data Analytics - Analytic Tools

A

Benefits

  1. Makes decisions in the face of uncertainty.
  2. Helpful in replacing intuition, prejudice and flat out guessing
  3. Creates confidence around best-case, worst-case and most likely scenarios

Limitations

  1. Can’t predict human responses or behaviors to changes
  2. Can’t model casual links that affect a particular result in the real world
  3. Accuracy depends on input quality
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11
Q

What-if & Goal Seeker

P1.F.4.3 Data Analytics - Analytic Tools

A
  1. Both tools to run scenarios to understand possibilities
  2. Prepare for best/worst case

What-if

  1. Starts with changes in input
  2. What will happen if we change this?

Goal-seeking

  1. Starts with output goal
  2. If we want to change the result, what needs to happen?
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12
Q

Regression - Simple & Multiple

P1.F.4.3 Data Analytics - Analytic Tools

A
  1. Find dependent variable
  2. From one or more independent (explanatory) variables
  3. Contains constants

Simple: one explanatory
Multiple: more than one explanatory

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13
Q

Least Squares Line

P1.F.4.3 Data Analytics - Analytic Tools

A

The line that minimizes the vertical distances between itself and the data points.

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14
Q

Least Squares Line Equation

P1.F.4.3 Data Analytics - Analytic Tools

A

Observed value = Fitted value + Residual

  1. Fitted value: vertical line distance between x-axis and the line
  2. Observed value: actual point
  3. Residual: difference between fitted value and observed value
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15
Q

Regression Equation Calculations

P1.F.4.3 Data Analytics - Analytic Tools

A

y = a + bx

y = the mean y value
a = the optimal y-intercept
b = the optimal slope (variable coefficient)
x = the mean x value
  1. b (numerator) = (mean x value - x value) - (mean y value - y value)
  2. multiply x difference by y difference
  3. b (denominator) = (mean x value - x value) squared
  4. add the values
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16
Q

Correlation Coefficient: r

P1.F.4.3 Data Analytics - Analytic Tools

A
  1. Correlation between two variables
  2. Range from -1 to 1
  3. -1 implies perfect inverse correlation. As one variable increases, the other decreases by the same amount.
  4. 0 implies no correlation
  5. 1 implies perfect correlation
17
Q

Coefficient of Determination: R2

P1.F.4.3 Data Analytics - Analytic Tools

A
  1. Fit between least squares line and observed data
  2. % of variance in independent variable explained by least squares line
  3. Range from 0% (no explanation) to 100% (perfect explanation)
  4. Can’t compare outside context
  5. Can be applied to simple and multiple regression
18
Q

Time Series Analysis

P1.F.4.3 Data Analytics - Analytic Tools

A
  1. Values of same variables over time
  2. Used in forecasting
  3. Trend: generally, are things increasing or decreasing over time, and if so, by how much?
  4. Cyclical: how things change over long-term cycles (more than one year)
  5. Seasonal: how things change over a one year cycle
  6. Irregular: seemingly random and unpredictable, does not repeat in any particular patterns
19
Q

Time Series Analysis Benefits & Limitations

P1.F.4.3 Data Analytics - Analytic Tools

A

Benefits

  1. Assist with understanding decisions
  2. Applications almost limitless

Limitations

  1. Only shows correlation: don’t help in identifying root cause
  2. Echo chamber effect: more useful within range than outside
  3. Reliance on lagging indicators (historical observations)
  4. Random noise can distort picture

CMA Exam Part 1 (35 decks)

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