# Big Data - Exam II

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1. mathematical (quantitative) models
a system of symbols and expressions representing a real situation
2. environmental scanning and analysis
the monitoring, scanning, and interpretation of collected information
3. influence diagrams
graphical models of mathematical models - can facilitate the variable identification process
4. forecasting (predictive analytics)
predicting the future
5. Decision Support Systems
DSS
6. MBMS (model base management systems)
used to aid in the management of models
7. DSS - use quantitative models
expert systems - use knowledge-based models
DSS uses mostly _____ models, whereas expert systems use _____ models in their applications.
8. 1. optimization of problems with few alternatitives
2. optimization via algorithm
3. optimization via an analytic formula
4. simulation
5. heuristics
6. predictive models
7. other models
seven groups of DSS models
9. heuristics
model that includes finding a good enough solution using rules
10. optimization of problems with few alternatitives
model that infolves finding the best solution from a small number of alternatives
11. optimization via algorithm
model that involves finding the best solution from a large number of alternatives, using a step-by-step improvement process (linear and other mathematical programming models)
12. optimization via an analytic formula
model that involves finding the best solution in one step using a formula
13. simulation
model that involves finding a good enough solution or the best among the alternatives checked, using experimentation
14. predictive models
model that predicts the future for a given scenario
15. other models
model used to solve a what-if case, using a formula
16. management support system
MSS
17. multidimensional analysis (modeling)
involves data analysis in several dimensions - data are generally shown in a spreadsheet format
18. influence diagram
a graphical representation of a model - ie: a model of a model
19. result (or outcome) variables
decision variables
uncontrollable variables (and/or parameters)
intermediate result variables (not a "major" component)

- all linked by mathematical relationships
four basic components of a model
20. Decision Support System (DSS)
any system that is designed to help a decision maker make a better decision
21. a decision variable
A rectangle in an influence diagram represents ____.
22. an uncontrollable or intermediate variable
A circle in an influence diagram represents ____.
23. the result (outcome) variable; intermediate or final
An oval in an influence diagram represents ____.
24. the direction of influence (relationship)
A arrow in an influence diagram indicates ____.
25. mathematical relationships
What links the components of models?
26. result (outcome) variables
reflect the level of effectiveness of a system - ie: indicate how well the system performs or attains its goals
27. dependent variables
Result variables are also known as ______.
28. decision variables
describe alternative courses of action - ex: the amount to invest in bonds, people, times, & schedules
29. uncontrollable variables (fixed), parameters, or variables (can vary)
factors that affect the result variables but are not under the control of the decision maker
30. constraints
Variables that limit the decision maker form _____.
31. intermediate result variables
reflect intermediate outocomes in mathematical models - ie: not what you're looking for, but leads to what you're looking for
32. certainty
risk
uncertainty
categories of knowledge that decision situations can be grouped into
33. certainty model
• decision making model in which:
• complete knowledge is assumed
• all potential outcomes are known
• may yield the optimal solution
34. uncertainty model
• decision making model in which:
• there are several outcomes for each decision
• the probability of each outcome is known
• knowledge would lead to less uncertainty
35. risk analysis model (probablistic decision making)
• the decision making model in which the following are taken into consideration:
• probability of each of several outcomes occuring
• level of uncertainty >= expected value
36. decision making under certainty
it is assumed that complete knowldege is available so that the decision maker knows exactly what the outcome of each course of action will be - occurs most often with structured problems with short time horizons
37. decision making under uncertainty
decision maker considers situations in which several outcomes are possible for each course of action - decision makes does not know, or cannot estimate, the probability of occurrence
38. decision making under risk (ie: probabilistic or stochastic) decision making situation
the decision maker must consider several possible outcomes for each alternative, each with a given probability of occurrence - long-run probabilities that the given outcomes will occur are assumed to be known or can be estimated
39. assumed risk
The category of knowledge that most major business decisions are made under:
40. risk analysis (calculated risk)
a decision-making method that analyzes the risk (based on assumed known probabilities) associated with different alternatives
41. risk analysis (calculated risk)
can be performed by calculating the expected value of each alternative and selecting the one with the best expected value
most popular end-user modeling tool:
43. mathematical programming
a family of tools designed to help solve managerial problems in which the decision maker must allocate scarce resources among competing activities to optimize a measurable goal
44. constraints
restriction of allocation by several limitations and requirements
45. optimal solution (best solution)
the solution for an alocation problem in which the degree of goal attainment associated with it is the highest
46. non-quantitative models (qualitative)
quantitative models
two types of mathematical models
47. non-quantitative models (qualitative)
captures symbolic relationships between decision variables, uncontrollable variables and result variables
48. quantitative models
mathematically links decision variables, uncontrollable variables , and result variables
49. objective function
a linear mathematical function that relates the decision variables to the goal, measures goal attainment, and is to be optimized
50. decision variables
variables in a linear programming (LP) problem whose values are unknown and are searched for
51. objective function coefficient
unit profit or cost coefficients indicating the contribution to the objective of one unit of a decision variable
52. constraints
expressed in the form of linear inequalities or equalities that limit resources and/or requirements
53. capacities
describe the upper and sometimes lower limits on the constraints and variables
54. input/output (technology) coefficients
indicate resource utilization for a decision variable
 Author: mjweston ID: 237430 Card Set: Big Data - Exam II Updated: 2013-09-28 19:58:22 Tags: Modeling Analysis Linear Programming Folders: Description: Modeling & Analysis - Linear Programming Show Answers: