Supply Chain Modeling Quiz Exams
When a credit manager of a mortgage company identifies the loans as those resulting in default and those that are current, he/she uses: a. Risk forecasting. b. Risk assessment. c. Confidence determination. d. Classification.
d. Classification.
The first step to create the training and validation data set using XLMiner Platform is: a. Select the "best fit" icon. b. Select the success percentage. c. Determine the size of the validation set. d. Click the Partition icon in the Data Mining section.
d. Click the Partition icon in the Data Mining section.
The term "data mining": a. Refers to digging for precious metals. b. Describes a process used by leading organizations in manufacturing in order to enhance customer value. c. Describes a relatively easy work that requires the little preparation. d. Encompasses a variety of analytic techniques that can be used to help managers analyze, understand, and extract value from large sets of data.
d. Encompasses a variety of analytic techniques that can be used to help managers analyze, understand, and extract value from large sets of data.
In order to indicate the output cell (or cells) that we want Analytic Solver Platform (ASP) to track during the simulation we can use the: a. "Add Output" button on the Analytic Solver Platform (ASP) menu. b. AddGraph(.) function. c. AddPsiChart(.) function. d. GenerateOutput(.) function in ASP menu.
a. "Add Output" button on the Analytic Solver Platform (ASP) menu.
Suppose the highest payoff decision for a given state of nature is 100. You made a decision with a payoff of 80. The regret (or opportunity loss) for your decision is: a. 20 b. 90 c. 80 d. 100
a. 20
In goal programming, hard constraints: a. Cannot be violated. b. Can be relaxed. c. Can be modeled as linear functions. d. Are difficult to meet.
a. Cannot be violated.
Classification techniques differ from most other predictive statistical methods, such as regression analysis, because the dependent variable is: a. Discrete. b. Negative. c. Fractional. d. Continuous.
a. Discrete.
Logistic regression is: a. Limited to analyzing two groups. b. A classification technique that estimates the probability of an observation belonging to a particular group. c. Limited to two independent variables. d. A special case of simple linear regression.
b. A classification technique that estimates the probability of an observation belonging to a particular group.
In what-if analysis: a. Uncertain variables are removed from the decision-making process. b. A manager changes the values of the uncertain input variables to see what happens to the bottom-line performance measure. c. All variables become parameters. d. All variables are assigned random values.
b. A manager changes the values of the uncertain input variables to see what happens to the bottom-line performance measure.
A table that summarizes the final outcome (or payoff) for each decision alternative under each possible state of nature is referred to as: a. A loss table b. A payoff matrix c. Eigentable d. Revenue table
b. A payoff matrix
The decision tree is: a. a graphical presentation of the information available in the payoff b. Intuitive to use c. Easy to use for multi-stage decisions d. All of the above are correct
d. All of the above are correct
Neural networks: a. Simulate human learning. b. Are computer programs modeled after computing architecture of the human brain. c. Are a pattern recognition technique that attempts to learn what relationship exists between a set of input and output variables. d. All of the above characterize neural networks.
d. All of the above characterize neural networks.
Suppose that you have an option to hire a consultant who has the ability to predict the future with 100% accuracy. Using the consultant's reliable recommendations, you found that the expected value with perfect information is equal to $200. Without the consultant's insights, you determined the EMV to be equal to $175. Would you pay the consultant $30 for her service? Why or why not? a. No, because EVPI is $25, which is less than the consultant's fee of $30 b. No, because EVPI is $30. c. Yes, because expected value with perfect information is higher than the consultant's fee. d. Yes, because EVPI is $30.
a. No, because EVPI is $25, which is less than the consultant's fee of $30
The first step in performing a simulation in a spreadsheet is: a. Placing a random number generator (RNG) formula in each cell that represents a random, or uncertain, independent variable. b. Calculating the expected values of the random variables. c. Calculating the values of all dependent variables in the model. d. Fitting a bell-shaped curve to the variable of interest.
a. Placing a random number generator (RNG) formula in each cell that represents a random, or uncertain, independent variable.
The purpose of discriminant analysis (DA) is to: a. Provide theoretically optimal or good classification results. b. Calculate Euclidean distances between points in the data set. c. Locate the centroids. d. Assign points to one of the two groups.
a. Provide theoretically optimal or good classification results.
Recalculating the spreadsheet several hundred or several thousand times and recording the resulting values generated for the output cell(s), or bottom-line performance measure(s) is called: a. Replication. b. Automation. c. Model fitting. d. Estimation.
a. Replication.
Sensitivity analysis is useful when: a. You may be interested in examining how sensitive the simulation output results are to various uncertain input cells in the model. b. There are more than one input cells in the model. c. There is a lot of variability in the model. d. There is one input cell in the model.
a. You may be interested in examining how sensitive the simulation output results are to various uncertain input cells in the model.
The decision rule that pessimistically assumes that nature will always be "against us" regardless of the decision we make is: a. maximin b. luck c. maximax d. laplace
a. maximin
A MINIMAX objective is sometimes helpful in goal programming (GP) when: a) you do not want to explore corner points of the feasible region. b) you want to minimize the maximum deviation from any goal. c) you want to maximize the minimum deviation from a set of goals. d) you do not want to explore points on the edge of the feasible region.
b) you want to minimize the maximum deviation from any goal.
Consider the constraint: X1+- (d[1]^(-) - d[1]^(+) = 5. Suppose that X1 = 3 in the optimal solution. The values of deviational variables and are:
b. d[1]^(-) = 2 d[1]^(+) = 0
Suppose that all goal constraints in a goal programming problem are hard and the objective is: MIN. Then: a. The problem is infeasible. b. All goals must be met exactly. c. The optimal value of the objective function is negative. d. The optimal value of the objective function is zero.
b. All goals must be met exactly.
Overfitting refers to a situation when the tree algorithm: a. Limits the maximum number of observations per node. b. Classifies new observations less accurately than trees that do not overfit the training data. c. Easily determines the required minimum number of observations per node. d. Calculates the maximum reduction of impurity to avoid overfitting.
b. Classifies new observations less accurately than trees that do not overfit the training data.
Analytic Solver Platform (ASP) provides several "Psi" functions that can be used to: a. Complete a PivotTable. b. Create the RNGs required for simulating a model. c. Interpret the simulation results. d. Store the simulation results for replication.
b. Create the RNGs required for simulating a model.
The _____ in a decision problem represent various factors that are important to decision maker. a. Base b. Criteria c. evaluation d. alternatives
b. Criteria
In Discriminant Analysis (DA), precision is a measure of: a. Robustness of prediction. b. How accurate the classifier is when it predicts a "success". c. Solution sensitivity. d. Solution applicability.
b. How accurate the classifier is when it predicts a "success".
The manager hopes that using the expected, or most likely, values for all the uncertain variables will: a. Guarantee that an optimal decision will be made. b. Provide the most likely value for the bottom-line performance measure (Y). c. Be supported by the manager's team. d. Be the right guess
b. Provide the most likely value for the bottom-line performance measure (Y).
We can apply a process known as _____ to a decision tree to determine the decision with the largest EMV. a. Unraveling b. Rolling back c. Folding d. Unrolling
b. Rolling back
If we don't know what value a particular cell in a spreadsheet will assume and enter a number that we think is the most likely value for the uncertain cell, we can calculate the most likely value of the bottom-line performance measure. This is called: a. The random scenario. b. The base-case scenario. c. The optimistic scenario. d. The pessimistic scenario
b. The base-case scenario.
When faced with a classification problem, careful consideration should be given to: a. The sample size. b. The composition of the training sample. c. Adoptive filtering. d. The number of groups.
b. The composition of the training sample.
Under probabilistic decision rules: a. The goal is to select a decision assuming the most optimistic occurrence of states of nature b. The states of nature in a decision problem can be assigned probabilities of occurrence c. Probability information is not available d. the alternatives in a decision problem can be assigned probabilities of occurrence
b. The states of nature in a decision problem can be assigned probabilities of occurrence
The goal of decision analysis is to: a. use qualitative models for making decisions b. help individuals make good decisions c. use quantitative models for making decisions d. use judgmental models for making decisions
b. help individuals make good decisions
The decision rule that selects the alternative associated with the largest payoff is: a. laplace b. maximax c. maximin d. bayes
b. maximax
Suppose that, for a given decision alternative, the payoffs are (10, 20, 30) with respective probabilities (0.2, 0.3, and 0.5). The expected payoff for this alternative is: a. 60 b. 30 c. 23 d. 10
c. 23
Cluster analysis is: a. A method for determining the estimated probability of a consequent. b. A recommender system technique. c. A data mining technique used to identify meaningful groupings of records within a data set. d. A method for calculating confidence of an association rule.
c. A data mining technique used to identify meaningful groupings of records within a data set.
A course of action intended to solve a problem is called: a. A decision b. A model c. An Alternative d. A criterion
c. An Alternative
In Discriminant Analysis, the F1 score: a. Is restricted to positive values. b. Is a measure of recall. c. Combines the precision and recall measures to provide an overall measure of a classifier's accuracy. d. Is restricted to negative values.
c. Combines the precision and recall measures to provide an overall measure of a classifier's accuracy.
Goal programming: a. Requires firm RHS values of the constraints. b. Requires the use of only hard constraints. c. Involves solving problems containing a collection of goals that we would like to achieve. d. Is not an iterative solution procedure.
c. Involves solving problems containing a collection of goals that we would like to achieve.
The decision rules that assume that probabilities of occurrence are not known or cannot be assigned to the states of nature in a decision problem are referred to as: a. Causal rules b. Probabilistic rules c. Non-Probabilistic rules d. Correlation rules
c. Non-Probabilistic rules
Suppose that the objective function for a GP problem is: MIN. The terms (w[i])^(-) and (w[i])^(+) represent: a.The exponentially decreasing weights representing the relative importance of a deviational variable. b. Desirability of a negative deviation from the target value. c. Numeric constants that can be assigned values to weight the various deviational variables in the problem. d. Desirability of a positive deviation from the target value.
c. Numeric constants that can be assigned values to weight the various deviational variables in the problem.
The key concept in goal programming (GP) is: a. Recognizing the key goals. b. Limiting the number of soft constraints. c. Reconciling trade-offs between conflicting goals. d. Relaxing the hard constraints.
c. Reconciling trade-offs between conflicting goals.
Suppose that the objective function for a GP problem is: MIN. The term ti represents: a. The amount of underachievement for goal i. b. The weighted sum of over and underachievement for goal i. c. The target value for goal i. d. The amount of overachievement for goal i.
c. The target value for goal i.
A random variable is a: a. Parameter whose numerical value is unknown. b. Number with an unknown variance. c. Variable whose value cannot be predicted or set with certainty. d. Constant with an unknown mean.
c. Variable whose value cannot be predicted or set with certainty.
Suppose that in goal programming (GP) we assign arbitrarily large weights to deviations from these goals to ensure that undesirable deviations from them never occur. This approach is called: a) Inclusive GP. b) Forcing. c) Reactive GP. d) Preemptive GP.
d) Preemptive GP.
Goal programming (GP) provides a way of analyzing potential solutions to a decision problem that involves soft constraints. Soft constraints can be stated as: a) '=>' type constraints b) '=<' type constraints c) nonlinear constraints d) goals with target values and devotional variables, which measures the amount by which a given solution deviates from a particular goal
d) goals with target values and devotional variables, which measures the amount by which a given solution deviates from a particular goal
Deviational Variables: a) represent the amount by which each goal's target value is underachieved b) must take a value of zero in the optimal solution to the problem c) represent the amount of which each goal's target value is overachieved d) represent the amount by which each goal deviates form its target value
d) represent the amount by which each goal deviates form its target value
The term 'iterative solution procedure' means that: a) the solution algorithm executes in cycles b) optimal solution can be found quickly c) a known number of iterations is required to find the optimal solution to the problem d) the decision maker investigates a variety of solutions to find one that is most satisfactory
d) the decision maker investigates a variety of solutions to find one that is most satisfactory
Affinity analysis is: a. Characterized by high model variability. b. Equivalent to correlation analysis. c. Not applicable for analyzing large amounts of data. d. A data mining technique aimed at discovering what goes with what.
d. A data mining technique aimed at discovering what goes with what.
Simulation is: a. A technique that measures various characteristics of the model bottom-line performance measure. b. A technique that describes various characteristics of the bottom-line performance measure. c. A technique that is useful when one or more values for the independent variables are uncertain. d. All of the above answers are correct.
d. All of the above answers are correct.
The benefit(s) of simulation include(s): a. The results of simulation do give us greater insight into the problem. b. It gives a decision maker some idea of the best- and worst-case total outcomes for the problem. c. It provides an idea of the distribution and variability of the possible outcomes. d. All of the above answers are correct.
d. All of the above answers are correct.
When faced with uncertainty, people do the following: a. React with paralysis. b. Do exhaustive research. c. Avoid making a decision. d. All of the above answers are correct.
d. All of the above answers are correct.
Data mining tasks fall into the following potential categories: a. Classification. b. Prediction. c. Association/Segmentation. d. All of the above are categories of data mining tasks.
d. All of the above are categories of data mining tasks.
A classification tree: a. Is a graphical representation of a set of rules for classifying observations into two or more groups. b. Use a hierarchical sorting process consisting of splitting nodes and terminal nodes to group records from a data set into increasingly homogeneous groups. c. Are popular because the resulting classification rules are very apparent and easy to interpret. d. All of the above are characteristics of classification trees.
d. All of the above are characteristics of classification trees.
The k-nearest neighbor (k-NN) technique: a. Determines the residual values. b. Measures the amount of noise in the actual data around the centroid. c. Makes restrictive assumptions about the functional form of the relationship between the dependent group variable and the independent variables. d. Identifies the k observations in the training data that are most similar (or nearest) to a new observation we want to classify.
d. Identifies the k observations in the training data that are most similar (or nearest) to a new observation we want to classify.
When such a decision is made, some chance exists that the decision will not produce the intended results. This chance, or uncertainty, represents: a. Bad luck. b. Good luck. c. A residual. d. Risk.
d. Risk.
The challenge with data availability today is getting: a. Actual process data. b. The aggregate data. c. The detailed data. d. The right data in the right amount for the problem at hand.
d. The right data in the right amount for the problem at hand.
A strategy table: a. is a sensitivity analysis technique b. allows a decision maker to analyze optimal decision strategy changes c. allows two simultaneous changes in probability estimates. d. all of the above answers are correct
d. all of the above answers are correct
The expected monetary value (EMV) and expected opportunity loss (EOL) decision rules: a. require that the regret table must be generated prior to implementing the decision rule b. produce different decisions c. provide stable predictions d. always result in the selection of the same decision alternatives
d. always result in the selection of the same decision alternatives
The first step in using the minimax regret decision rule is: a. obtaining the probability information b. determining the decision evaluation criteria c. selecting the minimum payoff for each decision d. converting the payoff matrix into a regret matrix
d. converting the payoff matrix into a regret matrix
Your company decided to build a manufacturing plant in Georgia in anticipation of increased demand for the product it produces. The level of demand in this a. criterion b. decision c. alternative d. state of nature
d. state of nature
Future events that are not under the decision maker's control are known as: a. parameters b. alternatives c. random variables d. states of nature
d. states of nature
Tornado charts in Analytic Solver Platform (ASP) help identify: a. sources of model variability b. the state of nature nodes in a decision tree c. the event nodes in the decision tree d. the inputs that have the greatest impact on the EMV
d. the inputs that have the greatest impact on the EMV
