,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.
,◍
COMPREHENSIVE QUESTIONS AND ANSWERS
◍ what question is the wilcoxon signed rank test trying to answer?. Answer:
is the median of the distribution different from m
◍ what is stepwise regression.
Answer: it is a combination of forward selection and backward elimination. We can either start with all
factors or no factors and at each step we remove or add a factor. As we go through the procedure after
adding each new factor and at the end we eliminate right away factors that no longer appear.
◍ What are the assumptions does a geometric distribution make?.
Answer: Each Bernoulli trial is independent and identically distributed
◍ What is modularity?.
Answer: measures of how well the graph is separated into communities or modules that are connected a lot
internally but not connected much in between each other
◍ What happens when n is big for binomial distribution.
Answer: it converges to normal distribution
◍ Pros and cons of imputation with mean and mode.
Answer: Pros: hedge against being too wrong, easy to computeCons: biased imputation, if certain group
has missing data, the mean may not be truly representative
◍ Do optimization implicitly assume that we know all of the values of the input data exactly?.
, Answer: Optimization models treat all of the data as known exactly
◍ what are replications in simulations?.
Answer: number of runs of a simulaiton
◍ What do kernel methods allow for in SVMs.
Answer: nonlinear classifiers
◍ what is an objective function?.
Answer: The objective function is a measure of the quality of a set of values for the variables, which we're
trying to maximize or minimize
◍ What are the advantages and disadvantages of using regression for imputation.
Answer: It reduces or eliminates the problem of bias. Also gives better values for missing data
Disadvantages: we have to build, validate and test a whole other model just to fill in the missing data and
then we have to do it all over again to get the answer we want. Also we are using the same data twice: once
for imputation and a second time to fit the model
◍ What is the objective function in optimization model?.
Answer: measure the quality of the solution, the set of variables; we typically want to min or max the
function
◍ what are deterministic simulations.
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.
,◍
COMPREHENSIVE QUESTIONS AND ANSWERS
◍ what question is the wilcoxon signed rank test trying to answer?. Answer:
is the median of the distribution different from m
◍ what is stepwise regression.
Answer: it is a combination of forward selection and backward elimination. We can either start with all
factors or no factors and at each step we remove or add a factor. As we go through the procedure after
adding each new factor and at the end we eliminate right away factors that no longer appear.
◍ What are the assumptions does a geometric distribution make?.
Answer: Each Bernoulli trial is independent and identically distributed
◍ What is modularity?.
Answer: measures of how well the graph is separated into communities or modules that are connected a lot
internally but not connected much in between each other
◍ What happens when n is big for binomial distribution.
Answer: it converges to normal distribution
◍ Pros and cons of imputation with mean and mode.
Answer: Pros: hedge against being too wrong, easy to computeCons: biased imputation, if certain group
has missing data, the mean may not be truly representative
◍ Do optimization implicitly assume that we know all of the values of the input data exactly?.
, Answer: Optimization models treat all of the data as known exactly
◍ what are replications in simulations?.
Answer: number of runs of a simulaiton
◍ What do kernel methods allow for in SVMs.
Answer: nonlinear classifiers
◍ what is an objective function?.
Answer: The objective function is a measure of the quality of a set of values for the variables, which we're
trying to maximize or minimize
◍ What are the advantages and disadvantages of using regression for imputation.
Answer: It reduces or eliminates the problem of bias. Also gives better values for missing data
Disadvantages: we have to build, validate and test a whole other model just to fill in the missing data and
then we have to do it all over again to get the answer we want. Also we are using the same data twice: once
for imputation and a second time to fit the model
◍ What is the objective function in optimization model?.
Answer: measure the quality of the solution, the set of variables; we typically want to min or max the
function
◍ what are deterministic simulations.
