Flashcards
30 exam 1
Cards in this deck (25)
- 1Quantitative AnalysisFocus on being able to numerically measure the data associated with the problem
Develop mathematical expressions that describe objectives, constraints and other relationships in the problem
Use some type of quantitative methods approach to make a recommendation - 2Quantitative Analysis1. Determining modeling approach (what solution method are you going to use?)
2. Preparing data
3. Develop modeling solution
4. Generate output
5. Interpret results from output - 33 Types Models1. Iconic
2. Analog
3. Mathematical - 4Iconic modelsphysical replicas of real objects (i.e. model car, a model of a building, etc.)
- 5Analog modelsphysical form, but do not physically represent object being modeled (speedometer, thermometer, graph of social network, etc.) speedometer isn’t really the speed it’s a representation
- 6Mathematical modelsusing mathematical formulas and expressions to represent a real world problem (total profit, total cost, etc.)
Profit (P) = 10x, where x is the number of units sold and $10 is the profit from each unit
[Solution to x is infinite. Unconstrained problem. Here’s profit how much x should I product? Need constraints to make it more realistic] - 7Experimenting with models generallyrequires less time
is less expensive
involves less risk
can enable you to investigate a situation that cannot be represented in reality
“Essentially, all models are wrong, but some are useful” - 8*Objective Function –mathematical expression that describes the problem’s objective, such as maximizing profit or minimizing cost
P=10x (from slide #11)
10x is the OBJECTIVE FUNCTION - 9Constraintsa set of restrictions, limitations, or assumptions such as a limit in production capacity or a fixed number of labor hours
Take our production problem P=10x (from slide #11)
Assume 5 hours of labor are required to produce each unit of x, and the total hours of labor available each week are 40
5x ≤ 40 is the LABOR CONSTRAINT - 10Uncontrollable Inputsenvironmental factors that are not under the control of the decision maker
In our simple model, the profit per unit ($10), the production time per unit (5 hours), and the production capacity (40 hours) are environmental factors not under the control of the manager or decision maker
Called parameters ^ (x are the variables) - 11Decision Variablescontrollable inputs; choices made by the decision maker, such as the number of units of a product to produce
In our model, the decision maker (or manager) has one choice – how much of x to produce - 12Our complete mathematical model for the simple production problems is:
- 13Single Criteria Multi criteriasolution thats the best for one criteria
solution works across all criteria - 14Deterministic*if all uncontrollable inputs (profit, labor hours, etc.) to the model are known with certainty and cannot vary.
For this class, we commonly make this assumption, although in reality this is RARELY the case - 15Stochastic (probabilistic)*if any uncontrollable inputs (profit, labor hours, etc.) are uncertain and subject to variation
For example, if the number of hours of production time per unit of x could vary between 3 and 6 hours - 16Optimal SolutionThe “best” output is the optimal solution
NOT best guess - 17InfeasibleIf the alternative does not satisfy all of the model constraints, it is rejected as being infeasible, regardless of the objective function value
- 18FeasibleIf the alternative satisfies all of the model constraints, it is feasible and a candidate for the “best” solution
- 193 Ways to Assign Probabilities1. Classical
2. Relative frequency method
3. Subjective method n - 20ClassicalAssume equally likely outcomes
Ex) 10 possible outcomes, probability any outcome 1/10 - 21Relative frequency methodAssign probabilities based on experimentation or historical data
- 22Subjective methodAssign probabilities based on judgment
- 23Simspons Paradoxa trend that is observed in multiple groups of data disappears or is reversed when those groups are combined
Ex) large data set tells you one thing..
Break down into groups fresh soph jun senior shows another thing - 24nonparametric proceduresdo NOT rely on the shape of the probability distribution from which they were drawn*
- 25Parametric proceduresdo rely on assumptions about the shape of the probability distribution*
The shape is assumed to be “normal”
All parameter estimates (mean, standard deviation) assume the data come from an underlying normally distributed population
if data is not normally distributed then don't use parametric tests
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- Flashcards on 30 exam 1, created by Hannah Livingston on 19/09/2018.
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- Hannah Livingston
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