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ISYE 6501 Final Exam|All Possible Questions and Answers|Latest |Study tips to ace in ISYE 6501 Finals;100% accurate. $18.49   Add to cart
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ISYE 6501 Final Exam|All Possible Questions and Answers|Latest |Study tips to ace in ISYE 6501 Finals;100% accurate.
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ISYE 6501 Final Exam|All Possible Questions and Answers|Latest |Study tips to ace in ISYE 6501 Finals;100% accurate.

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  • November 28, 2024
  • 67
  • 2024/2025
  • Exam (elaborations)
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ISYE 6501 Final Exam|All Possible Questions and Answers|Latest 2024-
2025|Study tips to ace in ISYE 6501 Finals;100% accurate.



Correct 91
100%
Incorrect 00


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Your answers




1 of 91

Definition



Use the same random numbers when possible...so that each replication
uses the same random numbers - which method is better, and how
often.



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, Descriptive statistics for data Regression analysis for predictive
summarization modeling




Non-parametric test for
Parametric test for data normalization
simulations' replications


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2 of 91

Term



Descriptive models



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Develop a marketing strategy Get an understanding of reality




Analyze past events for trends Create a fictional narrative


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3 of 91

Definition

,A subset of combinations to test - selected combinations give same
result as full factorial design i.e a balanced design
Before modeling and before collecting data
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Linear Program Finite Element Analysis




Fractional Factorial Design Within-subjects Design


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4 of 91

Term



Variables, constraints and objective function for optimization for
GARCH



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variables = a0,a1,...am (coefficients)
constraints = none
objective function = minimize prediction error

, variables = coordinates for cluster centers, and binary variables for whether each
data point is part of each cluster
constraints = each data points must be assigned to exactly 1 cluster
objective function = minimize the sum of distances from each data point to its
cluster center




variables = omega, Beta i, gamma i
constraints = none
objective function = minimize the prediction error




(standard regression, w/ 1 constraint added)
variables = a0,a1,...am (coefficients)
constraints = restrict the sum of the variables
objective function = minimize the squared error in the model estimates


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5 of 91

Definition



At each step, it does the one thing that looks best
without taking future options into consideration. Good for initial
analysis
1. Forward selection
2. Backwards elimination
3. Stepwise regression



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