# Big Data - Exam II

 mathematical (quantitative) models a system of symbols and expressions representing a real situation environmental scanning and analysis the monitoring, scanning, and interpretation of collected information influence diagrams graphical models of mathematical models - can facilitate the variable identification process forecasting (predictive analytics) predicting the future Decision Support Systems DSS MBMS (model base management systems) used to aid in the management of models DSS - use quantitative models expert systems - use knowledge-based models DSS uses mostly _____ models, whereas expert systems use _____ models in their applications. 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 heuristics model that includes finding a good enough solution using rules optimization of problems with few alternatitives model that infolves finding the best solution from a small number of alternatives 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) optimization via an analytic formula model that involves finding the best solution in one step using a formula simulation model that involves finding a good enough solution or the best among the alternatives checked, using experimentation predictive models model that predicts the future for a given scenario other models model used to solve a what-if case, using a formula management support system MSS multidimensional analysis (modeling) involves data analysis in several dimensions - data are generally shown in a spreadsheet format influence diagram a graphical representation of a model - ie: a model of a model 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 Decision Support System (DSS) any system that is designed to help a decision maker make a better decision a decision variable A rectangle in an influence diagram represents ____. an uncontrollable or intermediate variable A circle in an influence diagram represents ____. the result (outcome) variable; intermediate or final An oval in an influence diagram represents ____. the direction of influence (relationship) A arrow in an influence diagram indicates ____. mathematical relationships What links the components of models? result (outcome) variables reflect the level of effectiveness of a system - ie: indicate how well the system performs or attains its goals dependent variables Result variables are also known as ______. decision variables describe alternative courses of action - ex: the amount to invest in bonds, people, times, & schedules uncontrollable variables (fixed), parameters, or variables (can vary) factors that affect the result variables but are not under the control of the decision maker constraints Variables that limit the decision maker form _____. intermediate result variables reflect intermediate outocomes in mathematical models - ie: not what you're looking for, but leads to what you're looking for certainty risk uncertainty categories of knowledge that decision situations can be grouped into certainty model decision making model in which:complete knowledge is assumedall potential outcomes are knownmay yield the optimal solution uncertainty model decision making model in which:there are several outcomes for each decisionthe probability of each outcome is knownknowledge would lead to less uncertainty risk analysis model (probablistic decision making) the decision making model in which the following are taken into consideration:probability of each of several outcomes occuringlevel of uncertainty >= expected value 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 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 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 assumed risk The category of knowledge that most major business decisions are made under: risk analysis (calculated risk) a decision-making method that analyzes the risk (based on assumed known probabilities) associated with different alternatives risk analysis (calculated risk) can be performed by calculating the expected value of each alternative and selecting the one with the best expected value spreadsheet most popular end-user modeling tool: 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 constraints restriction of allocation by several limitations and requirements optimal solution (best solution) the solution for an alocation problem in which the degree of goal attainment associated with it is the highest non-quantitative models (qualitative) quantitative models two types of mathematical models non-quantitative models (qualitative) captures symbolic relationships between decision variables, uncontrollable variables and result variables quantitative models mathematically links decision variables, uncontrollable variables , and result variables objective function a linear mathematical function that relates the decision variables to the goal, measures goal attainment, and is to be optimized decision variables variables in a linear programming (LP) problem whose values are unknown and are searched for objective function coefficient unit profit or cost coefficients indicating the contribution to the objective of one unit of a decision variable constraints expressed in the form of linear inequalities or equalities that limit resources and/or requirements capacities describe the upper and sometimes lower limits on the constraints and variables input/output (technology) coefficients indicate resource utilization for a decision variable Authormjweston ID237430 Card SetBig Data - Exam II DescriptionModeling & Analysis - Linear Programming Updated2013-09-28T19:58:22Z Show Answers