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
Next steps
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.
Give this one a try later!
, Descriptive statistics for data Regression analysis for predictive
summarization modeling
Non-parametric test for
Parametric test for data normalization
simulations' replications
Don't know?
2 of 91
Term
Descriptive models
Give this one a try later!
Develop a marketing strategy Get an understanding of reality
Analyze past events for trends Create a fictional narrative
Don't know?
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
Give this one a try later!
Linear Program Finite Element Analysis
Fractional Factorial Design Within-subjects Design
Don't know?
4 of 91
Term
Variables, constraints and objective function for optimization for
GARCH
Give this one a try later!
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
Don't know?
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
Give this one a try later!
2025|Study tips to ace in ISYE 6501 Finals;100% accurate.
Correct 91
100%
Incorrect 00
Next steps
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.
Give this one a try later!
, Descriptive statistics for data Regression analysis for predictive
summarization modeling
Non-parametric test for
Parametric test for data normalization
simulations' replications
Don't know?
2 of 91
Term
Descriptive models
Give this one a try later!
Develop a marketing strategy Get an understanding of reality
Analyze past events for trends Create a fictional narrative
Don't know?
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
Give this one a try later!
Linear Program Finite Element Analysis
Fractional Factorial Design Within-subjects Design
Don't know?
4 of 91
Term
Variables, constraints and objective function for optimization for
GARCH
Give this one a try later!
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
Don't know?
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
Give this one a try later!
