Final exam study guide: Characteristics $($elements$)$ of optimization problems.

Definition of a shadow price in the sensitivity analysis of a linear programming model.

Understand how to algebraically set up a constraint in a linear programming model.

Effects of rounding a solution in a linear programming model on the feasibility and optimality of the solution.

Understand what a static scheduling model is$.$

List applications of linear programming models.

Understand the meaning of multiple optimal solutions.

Understand what a blending model is$.$

Algebraic representation of constraints in linear programming models including blending models.

Elements of a typical transportation model and the general logistics problem $($network representation$).$

Understand the basic elements of an aggregate planning model.

Define what is integer programming.

Define a payoff matrix.

Understand the different methods of assigning probabilities to the states of nature $($i$.$e$.$ historical data, best judgment and interview results$).$

Understand the different characteristics of decision problems and the goals of decision analysis.

Understand the differences between probabilistic and non$-$probabilistic methods used in decision rules.

Understanding of Maximax, Maximin, Minimax regret decision rules.

Definition and understanding of Expected Opportunity Loss $($expected regret$).$

Definition and understanding of Expected Monetary Value.

Understand the basic structure of decision trees.

Define and calculate the Expected Value of Sample Information $($EVSI$).$

Compute conditional probabilities.

Understand the difference between risk$-$averse, risk$-$neutral and risk$-$lover.

Calculation of the risk premium.

Understand the categories of data mining tasks.

Definition and interpretation of results of discriminant analysis.

Definition and interpretation of results of logistic regression.

Define the k$-$nearest neighbor and the neural network classification technique $($definition only$)$

Define the Affinity Analysis data mining technique $($definition only$).$