, ◍
THIS IS A SHORT DESCRIPTION OF THE DOCUMENT
Ace ISYE 6501 Exam 3 with this complete OMS Analytics study guide. Updated 2026/2027.
Covers 35+ exam Q&A with answers:
1. Probability & Distributions:
Weibull, Exponential, Poisson.
Memoryless property. k<l in Weibull
means failure rate decreases over time.
2. Optimization: 3 components -
Variables, Constraints, Objective Function.
Integer programs take longer to solve. Linear regression minimizes squared error.
3. Simulation: Models that imitate
real systems. Mixed/randomized
strategies. 4. Machine Learning: Neural
network layers and process. K-Means
clustering formula.
5. Time Series: Exponential
smoothing S_t = ax_t + (1 Alpha near
1 = low randomness.
,◍ Poisson distribution.
Answer: A discrete probability distribution of the number of iid events happening within a fixed time: Pr(
)=( ^ )( ^− )/( !). If the time between the events follows the exponential distribution, then
the number of events follows the Poisson distribution.
◍ What does the C value do in a CUSUM model?.
Answer: It pulls the Xt down a bit as we expect Xt to be higher than expectation at random. The bigger C
is, the harder it is for St to get large, and the less sensitive the method will be.The smaller C is the more
sensitive the model will be.
◍ what is a general non-convex program.
Answer: Optimization problem is not convex
◍ If it is not memoryless, it is not....
Answer: Exponential
◍ What are the three main components of the optimization models?.
Answer: variables, constraints, objective function
◍ What is a validation set used for?.
Answer: used to choose best model
◍ Does a SVM classifier need to be a straight line?.
, ◍
Answer: No, SVM can be generalized using kernel methods that allow for nonlinear classifiers. Software
has a kernel SVM function that you can use to solve for both linear and nonlinear classifiers.
what is a feasible solution in an optimization context?.
Answer: variable values that satisfy all constraints in an optimization context
◍ Entity.
Answer: A person/thing moving through a simulation.
◍ Variable (statistics sense).
Answer: An attribute whose value can differ for different data points.
◍ Machine learning.
Answer: Use of computer algorithms to learn and discover patterns or structure in data, without being
programmed specifically for them.
◍ What is a deterministic simulations?.
Answer: · Same inputs give the same outputs (no randomness)
◍ If we have a larger C ....
Answer: the harder for S_t to get large and the less sensitive the method will be
◍ Name some distributions that are memoryless.
Answer: poisson and exponential
THIS IS A SHORT DESCRIPTION OF THE DOCUMENT
Ace ISYE 6501 Exam 3 with this complete OMS Analytics study guide. Updated 2026/2027.
Covers 35+ exam Q&A with answers:
1. Probability & Distributions:
Weibull, Exponential, Poisson.
Memoryless property. k<l in Weibull
means failure rate decreases over time.
2. Optimization: 3 components -
Variables, Constraints, Objective Function.
Integer programs take longer to solve. Linear regression minimizes squared error.
3. Simulation: Models that imitate
real systems. Mixed/randomized
strategies. 4. Machine Learning: Neural
network layers and process. K-Means
clustering formula.
5. Time Series: Exponential
smoothing S_t = ax_t + (1 Alpha near
1 = low randomness.
,◍ Poisson distribution.
Answer: A discrete probability distribution of the number of iid events happening within a fixed time: Pr(
)=( ^ )( ^− )/( !). If the time between the events follows the exponential distribution, then
the number of events follows the Poisson distribution.
◍ What does the C value do in a CUSUM model?.
Answer: It pulls the Xt down a bit as we expect Xt to be higher than expectation at random. The bigger C
is, the harder it is for St to get large, and the less sensitive the method will be.The smaller C is the more
sensitive the model will be.
◍ what is a general non-convex program.
Answer: Optimization problem is not convex
◍ If it is not memoryless, it is not....
Answer: Exponential
◍ What are the three main components of the optimization models?.
Answer: variables, constraints, objective function
◍ What is a validation set used for?.
Answer: used to choose best model
◍ Does a SVM classifier need to be a straight line?.
, ◍
Answer: No, SVM can be generalized using kernel methods that allow for nonlinear classifiers. Software
has a kernel SVM function that you can use to solve for both linear and nonlinear classifiers.
what is a feasible solution in an optimization context?.
Answer: variable values that satisfy all constraints in an optimization context
◍ Entity.
Answer: A person/thing moving through a simulation.
◍ Variable (statistics sense).
Answer: An attribute whose value can differ for different data points.
◍ Machine learning.
Answer: Use of computer algorithms to learn and discover patterns or structure in data, without being
programmed specifically for them.
◍ What is a deterministic simulations?.
Answer: · Same inputs give the same outputs (no randomness)
◍ If we have a larger C ....
Answer: the harder for S_t to get large and the less sensitive the method will be
◍ Name some distributions that are memoryless.
Answer: poisson and exponential
