ISYE 6501 Final Exam Test Bank|415 All Possible Questions and Answers|2 Versions|100% Accurate.()
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ISYE 6501 Final Exam Test Bank|415 All Possible
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Intro to Analytics Modeling (ISYE 6501)
1 of 92
Definition
a social dilemma scenario in which mutual cooperation guarantees the
best mutual outcome
both sides have the incentive to cheat
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Trust Game Ultimatum Game
Game Theory Prisoner's Dilemma Game
,12/7/24, 1:10 AM ISYE 6501 Final
Term
When to use Empirical Bayes Modeling
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If arrivals are Poisson, with arrival rate lambda, then the time between arrivals (inter-
arrival time) follows the exponential distribution (1/lambda = inter-arrival)
The same is true if inter-arrival time is exponential
- Overall distribution of something is known or estimated
- Only a little data available
Take a single observation combined with a broader set of observations that
are relevant in general, but not specifically...and make a deduction or a
prediction.
-Assume the the response function is continuous and -symmetric - and only asks if
the median of the distro is different from a specified value (m)
- 1 data set
- sum of ranks
- calculate p-value
- like a normal distribution test
- | yi | - m, rank ascending, add up ranks only where yi > m
---- 2 pairs ----
- 2 data sets
- |yi|-zi -- a lot like McNemar's test, but used when yi/zi are numeric
- compare pairs of results (responses. i.e whether the treatment worked. Throws out
the cases where the results are the same)
- data points where 2 approaches were used on the same thing
- virus example w/ 2 different treatments
- uses the binomial distribution
- 2 data sets
- yi or zi are yes/no responses
,12/7/24, 1:10 AM ISYE 6501 Final
Term
Variables, constraints and objective function for optimization for
Support vector machine models (hard)
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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 = a0,a1,...am (coefficients)
constraints = each data point has to be correctly classified
objective function = maximize the distance (or margin) between the support
vectors
variables = omega, Beta i, gamma i
constraints = none
objective function = minimize the prediction error
variables = mu, theta, phis
constraints = none
objective function = minimize the prediction error
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4 of 92
Term
What does the memoryless property mean?
, 12/7/24, 1:10 AM ISYE 6501 Final
Tire wearing out IS NOT constant (Weibull) or an inter-arrival
memoryless rate (Exponential)
1) Throw away entirely
Linear Programs, Convex Quadratic
2) Use categorical variables to
Programs,
indicate missing data
Convex Programs, Integer Programs,
3) Estimate missing values
and General Non-Convex Programs.
(imputation)
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5 of 92
Definition
variable selection; classical
Start with model with no factors, at each step find best new factor to
add. Continue until none bad enough to remove, # of factor threshold
is satisfied. Remove factors at the end that were not good enough
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Stepwise regression Forward selection
Stepwise selection Backward elimination
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6 of 92
Term
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