Domain 4 - Methodology Selection

Question 1

Three categories of analytical models

Answer 1

Descriptive
Predictive
Prescriptive

Question 2

Prescriptive Techniques

Answer 2

Optimization
Simulation-Optimization
Stochastic Optimization

Question 3

Types of optimization models

Answer 3

Linear programming, Integer programming, Nonlinear programming, Mixed integer programming, Network optimization, Dynamic Programming, Metaheuristics

Question 4

Prescriptive models are used to…

Answer 4

provide new ways to improve certain types of performance as agreed upon with the customer and documented in the business and analytics problem statements.
Ask: “what is the best action/outcome?”

Question 5

Predictive models are used to…

Answer 5

predict future trends and possibilities and explain past relationships. Ask: “what could happen?”

Question 6

Predictive techniques include…

Answer 6

Simulation
Regression
Statistical Inferences
Classification
Clustering
Artificial Intelligence
Game Theory

Question 7

Simulation methods include…

Answer 7

(predictive analysis)
Discrete event simulation
Monte Carol
Agent-based modelling

Question 8

Regression methods include…

Answer 8

(predictive analysis)
Logistic
Linear
Step-wise

Question 9

Statistical Inferences include…

Answer 9

(predictive analysis)
Confidence intervals
Hypothesis testing
Analysis of variance
Design of experiments

Question 10

Descriptive analysis is used to…

Answer 10

answer the question“What happened?”
They describe the problem situation for further analysis. It can be based on descriptive statistics that are conveyed through:
(1) charts and graphs such as histograms, scatter plots, etc., and/or
(2) numerical presentations such mean, median, mode, variance, standard deviations of distributions of data, and cross tabulations.

Question 11

Primary factors that an analyst generally considers when selecting an appropriate methodology (7 things)

Answer 11

1. Time to complete project
2. Accuracy of the model needed
3. Relevance of the methodology and scope of the project
4. Accuracy of the data
5. Data availability and readiness
6. Staff and resource availability
7. Methodology popularity (go with the best approach not the most popular)

Question 12

Discrete event simulation (what is it and why use it)

Answer 12

A simulation methodology that is often used to understand bottlenecks in systems.
Handles cases that cannot be handled by queuing theory.
Often used for multistage processes modeling with variations in their arrivals and service time and utilizing shared resources to perform multiple operations.

Question 13

Queuing model (what is it and why use it)

Answer 13

Designed to identify the most efficient pathway to
solution; i.e., at a bank it might identify the number of tellers needed to satisfy customers in a particular time frame such as no more than 10 minutes waiting in a queue.

Question 14

Monte Carlo Simulation (what is it and why use it)

Answer 14

Used when a queuing model isn’t needed.
Used primarily to estimate dependent variable randomness out of a set of independent variable randomness. This is especially necessary when distributions of the input variables are not necessarily normally distributed and the relationship to estimate the dependent variable is not simple (e.g. additive)

Question 15

Agent-based modeling (what is it and why use it)

Answer 15

A system modeled (simulated) as a collection of autonomous decision-making entities called agents that are used to discover emergent behavior that is hard to predict without simulating it.

Question 16

System dynamics (what is it and why use it)

Answer 16

A simulation approach used to understanding the interactions of a complex system over time.

Question 17

Game theory (what is it and why use it)

Answer 17

Study of strategic decision-making processes through competition and collaboration

Question 18

Probability theory (what is it and why use it)

Answer 18

The likelihood of a particular event occurring expressed as a percentage to make decisions under chosen risk or tolerance. Bayesian and conditional probabilities are widely used in analytics.

Question 19

Economic analysis (what is it and why use it)

Answer 19

Evaluation often used to guide the optimal allocation

of scarce resources. Can include: IRR, NPV, FV, Payback period

Question 20

Regression analysis (what is it and why use it)

Answer 20

A class of statistical methodologies used to map dependent variables with independent variables and understand the significance between the variables and their correlations with one another.

Question 21

Linear regression (what is it and why use it)

Answer 21

Compares the relationship between a dependent
variable and one or more explanatory variables. The variables here are linear; however nonlinear functions can be explored here by scaling input data.

Question 22

Stepwise regression (what is it and why use it)

Answer 22

method of model building that successively adds or deletes variables based on performance

Question 23

Logistic regression (what is it and why use it)

Answer 23

may also be called logit analysis, is a regression analysis often used to predict the outcome of categorical variables.

Question 24

What are some key artificial intelligence models

Answer 24

Artificial Neural Networks, Fuzzy Logic, Expert Systems

Question 25

Value-stream mapping

Answer 25

Is a lean-management method for analyzing the current state and designing a future state for the series of events that take a product or service from its beginning through to the customer.
Requires more aggregate data compared to a discrete-event simulation model.

Question 26

Pros and Cons of aggregating data at a lower level

Answer 26

The lower the level of aggregation, the more accurate and descriptive the model will be of the real-life scenario;
however, it will be harder to validate and will certainly be more prone to mistakes.

Question 27

Pros and Cons of aggregating data at a higher level

Answer 27

usually provides faster results that are easier to understand but with less accuracy.

Question 28

How much should you aggregate data?

Answer 28

The general rule of thumb is to model at the highest level of aggregation possible that will ensure a satisfactory level of accuracy within the time permitted.

Question 29

What are some types of software tools?

Answer 29

Spreadsheets
optimization systems
statistical software
simulation systems
business intelligence systems
data management systems, 
data integration systems, 
Big Data operating systems (like HADOOP)

Question 30

What are the three portions that data should be divided into for model testing

Answer 30

Building data (training data) - used to estimate the need parameters
Testing data - used to test (verify) the model's ability to provide accurate results
Validating data - used to test that the model behaves closely to the physical behaviour being modeled.