Decision Modeling in Statistics
Terms in this set (29)
The three steps are formulation, solution, and interpretation.
Because it translates the problem into a mathematical model, and a poorly formulated problem leads to incorrect results.
Defining the problem, developing a model, and acquiring input data.
A variable is a measurable quantity that may vary or is subject to change.
A parameter is a measurable quantity inherent in the problem, usually with a known fixed value.
Improper or inaccurate input data will result in misleading or incorrect model results.
To solve the mathematical expressions from formulation to find the optimal solution.
A series of repeated steps or procedures used to find the best solution.
A process to determine how much the solution changes when the model or input data change.
Because input data or model assumptions may be inaccurate, and sensitivity analysis tests solution robustness.
Analyzing the results, performing sensitivity analysis, and implementing the solution.
Managers may resist change or not understand the model, preventing proper adoption.
A clear problem statement guides the entire modeling process and avoids solving the wrong problem.
Different stakeholders may see the problem differently, causing resistance to solutions favoring one view.
Because changes in one area can affect others, and ignoring this can cause unintended consequences.
A model assuming all input data and parameters are known with certainty.
A model assuming some input data are uncertain or unknown.
Input data are used in the model to arrive at the final solution; accuracy is critical.
Data may be scattered, incomplete, or not collected for the needed parameters, causing validity issues.
Because they may not understand the model or distrust results that are not intuitive.
Managers often want a range of options, not a single take-it-or-leave-it solution.
Trying all possible values of variables to find the best decision.
To verify the accuracy of input data and the appropriateness of the model before implementation.
Rapid changes in the business environment or problem conditions during model development.
Strong support and user involvement increase the chances of successful implementation.
An individual responsible for developing and analyzing decision models.
The number of units sold where total revenue equals total costs, resulting in zero profit.
The total fixed and variable costs when the number of units sold equals the break-even point.
A feature that allows setting a target value for a cell and automatically adjusting another cell to achieve it.