Questions and Answers of Introduction To Management Science 13th

What is the name of the streamlined version of the simplex method that is designed to solve minimum-cost flow problems very efficiently?
When does a dummy destination need to be added to the formulation of a shortest path problem?
What is the continuous-variables assumption?
What are the four interrelated decisions that need to be made by the management of the California Manufacturing Co.?
Reconsider Problem 3.17. Partition the 4,985 historical records of interest into a training partition (60 percent of the records) and a validation partition (the remaining 40 percent of the
Reconsider Problem 3.17. Partition the 4,985 historical records of interest into a training partition (60 percent of the records) and a validation partition (the remaining 40 percent of the
Reconsider Problem 3.17. a. Apply the regression tree algorithm to predict the number of late payments based on the predictor variables of annual income and credit score. Generate a regression
Reconsider Problem 3.17. a. Apply the classification tree algorithm, allowing a maximum of four splits as the stopping rule. Assuming an applicant is classified as likely to default if they
Reconsider Problem 3.17. Now apply the KNN algorithm with k = 10 to predict the number of late payments for each of the three applicants (a, b, and c).Data from Problem 3.17. Part a, b and c.As first
As first described in Problem 2.16, Friendly Bank is very active with making loans to deserving people in the local community. However, the bank does need to carefully evaluate each loan to make sure
Reconsider Problem 3.6. Partition the historical records into a training partition (60 percent of the 800 records) and a validation partition (the remaining 40 percent of the 800 records). a.
Reconsider Problem 3.6.a. Apply logistic regression so as to classify applicants as either likely to graduate (or not). Determine the logistic response function giving the probability of graduation
Reconsider Problem 3.6. a. Apply multiple linear regression so as to predict the college GPA of applicants based on the predictor variables of high school GPA and SAT score. Determine the
The Making Numerical Predictions with Regression Trees subsection of Section 3.3 discusses how, when the outcome is measured by a numerical output variable, the regression tree algorithm splits the
Reconsider Problem 3.6. a. Apply the regression tree algorithm to predict the college GPA of applicants based on the predictor variables of high school GPA and SAT score. Generate a regression
Figures 3.16 and 3.17 show the second and third splitting of the data into regions when applying the classification tree algorithm to the case study. a. Calculate the values of the Gini Index
Reconsider Problem 3.6. Partition the historical records into a training partition (60 percent of the 800 records) and a validation partition (the remaining 40 percent of the 800 records). a.
Reconsider Problem 3.6. a. Assuming an applicant is classified as a likely success (and admitted) if they are at least 50% likely to graduate, apply the classification tree algorithm to generate
Reconsider Problem 3.6. Partition the historical records into a training partition (60 percent of the 800 records) and a validation partition (the remaining 40 percent of the 800 records). a.
As first described in Problem 2.9, Pathfinder College is a small liberal arts college that wants to improve its admissions process. In particular, too many of its incoming freshmen have failed to
Beyond predictive accuracy, what aspect of an algorithm is useful when you need to justify the algorithm and results to a customer or decision maker?
In the 100 percent rule for simultaneous changes in objective function coefficients, what are the percentage changes that are being considered?
Which numbers in this model represent tentative managerial decisions that management might want to change after receiving the Analytics Department’s analysis?
Are chance constraints more useful for hard constraints or soft constraints?
What is meant by a soft constraint?
Why might it be of interest to investigate the effect of making simultaneous changes in the functional constraints?
How many result cells can be selected for display in Analytic Solver’s two-way parameter analysis report?
What type of report can Analytic Solver generate to show how the optimal solution varies for a defined range of values of a parameter cell?
Which estimates of the parameters in the linear programming model for the Wyndor problem are most questionable?
What are the parameters of a linear programming model?
Which categories of functional constraints are included in the new linear programming model?
Pure assignment problems have what type of functional constraints?
What is the form of the Excel equation for each output cell (including the objective cell) when formulating such a model?
What kind of trade-off does the management of the Quick Co. need to consider in making its final decision about how to expedite its new product to market?
Name five important categories of network optimization problems that turn out to be special types of minimum-cost flow problems.
Consider Figure 9.9 (in Section 9.3), which depicts the BMZ Co. distribution network from its factories in Stuttgart and Berlin to the distribution centers in both Los Angeles and Seattle. This
What are a few typical kinds of applications of minimum-cost flow problems?
How are binary variables used to represent managerial decisions on which projects from a group of proposed projects should be selected for approval?
How are binary variables used to represent managerial decisions regarding which site or sites should be selected for new facilities?
What is the crew scheduling problem that is encountered by companies in the travel industry?
How does a mixed BIP problem differ from a pure BIP problem?
What change in a linear programming problem makes it an integer programming problem?
Why are binary decision variables appropriate to represent these decisions?
What types of projects are under consideration in the Tazer Corp. problem?
What are some types of emergency services facilities for which sites may need to be selected?
What are the yes-or-no decisions that need to be made when addressing a crew scheduling problem?
Why is a linear programming formulation no longer valid for a product-mix problem when there are setup costs for initiating production?
Comparing a large linear programming problem and the corresponding integer programming problem, which of these problem types tends to be more difficult to solve?
What caused the optimal solution for the revised Wyndor problem to differ from that for the original Wyndor problem?
What are some examples where the decision variables need to be integers?
What are the contingent decisions in this problem? For each one, what is the form of the resulting constraint in the BIP model?
What are binary decision variables?
What is the tentative managerial decision concerning which what-if analysis needs to be performed?
What is the objective specified by management for this problem?
What is the objective for this problem?
What was the objective for the Caliente City problem?
For the Southwestern Airways problem, there is a constraint for each flight to ensure that this flight is covered by a crew. Describe the mathematical form of this constraint. Then explain in words
How can a binary variable be defined in terms of whether a setup is performed to initiate the production of a certain product?
What is the distinction between pure and mixed integer programming problems?
What are the mutually exclusive alternatives in this problem? What is the form of the resulting constraint in the BIP model?
What is a set covering constraint and what is a set covering problem?
In Section 1.4, the analytics study team was tasked with making a recommendation for the best level of advertising for the VRX2000 during the upcoming first quarter (Q1). They used historical data
What are some quantitative decision sciences that are drawn upon by business analytics?
What are some objectives that management might choose for a study?
Give three examples of types of errors that are corrected during data cleaning?
What does the k-nearest-neighbors algorithm base its predictions upon?
What are the many advantages of a decision model over a verbal description of a problem?
Label each of the following statements as True or False. If false, explain why. a. The goal of descriptive analytics is to describe the analytical tools that are being used in a study. b.
How has the loan interest rate been determined at First Bank?
What is the difference between a prediction model and a classification model?
What are some pitfalls to be avoided when using decision models?
Label each of the following statements as True or False. If false, explain why. a. Business analysts who are highly trained in management science have a special expertise for performing
What are three questions that could be addressed with descriptive analytics to show how unsecured loans have been performing for First Bank?
What is the difference between an historical record and a predictor record?
What is the process of model enrichment?
Label each of the following statements as True or False. If false, explain why. a. When developing a decision model, a good approach is to begin with elaborate models that reflect the complexity
What is an essential prediction for a new loan applicant at First Bank?
How are summary statistics useful in exploring the data?
What is the difference between a predictor variable and an outcome variable?
What are the two types of decision models being planned for First Bank?
Label each of the following statements as True or False. If false, explain why. a. What-if analysis provides an alternative form of analysis when it is not possible to obtain an optimal
Reconsider Problem 2.9. Now using the data on the Clean Data tab, explore the data quantitatively by evaluating the summary statistics as follows. a. For each column of numerical data (High
When performing predictive analytics, what criteria would be used to determine whether a new loan applicant at First Bank is likely to default?
How is sorting and filtering useful in exploring the data?
Give two ways correlation can be used when choosing variables to include in the model.
What is the distinction between the optimal solution for a decision model and the best possible solution for the real problem?
Pathfinder College is a small liberal arts college that wants to improve its admissions process. In particular, too many of its incoming freshmen have failed to graduate for a variety of reasons,
The First Bank study team will use prescriptive analytics to study which two types of policies?
How are charts useful in exploring the data?
What does it mean to overfit to the data?
What is meant by satisficing rather than optimizing the outcome of a study?
What is data visualization?
Give two reasons for partitioning the data.
What are heuristic procedures and metaheuristics?
Why is post-optimality analysis often an important part of a study?
Reconsider Problem 2.9. Now using the data on the Clean Data worksheet tab, apply sorting and filtering on the dataset to explore the data as follows. a. Sort the data by High School GPA.
What is the familiarization objective of descriptive analytics?
What is the difference between specificity and sensitivity?
What are the sensitive parameters of a decision model and why is it important to identify them?
Reconsider Problem 2.9. Now using the data on the Clean Data worksheet tab, apply sorting and filtering on the dataset as follows. a. For each column of numerical data (High School GPA, SAT

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