ISYE 6501 FINAL COMPREHENSIVE TEST PAPER
WITH COMPLETE SOLUTIONS
◉ ARIMA.
Answer: Autoregressive integrated moving average.
◉ Arrival Rate.
Answer: Expected number of arrivals of people, things, etc. per unit
time -- for example, the expected number of truck deliveries per
hour to a warehouse.
◉ Assignment Problem.
Answer: Network optimization model with two sets of nodes, that
finds the best way to assign each node in one set to each node in the
other set.
◉ Attribute.
Answer: A characteristic or measurement - for example, a person's
height or the color of a car. Generally interchangeable with "feature",
and often with "covariate" or "predictor". In the standard tabular
format, a column of data.
◉ Autoregression.
,Answer: Regression technique using past values of time series data
as predictors of future values.
◉ Autoregressive integrated moving average (ARIMA).
Answer: Time series model that uses differences between
observations when data is nonstationary. Also called Box-Jenkins.
◉ Backward elimination.
Answer: Variable selection process that starts with all variables and
then iteratively removes the least-immediately-relevant variables
from the model.
◉ Balanced Design.
Answer: Set of combinations of factor values across multiple factors,
that has the same number of runs for all combinations of levels of
one or more factors.
◉ Balking.
Answer: An entity arrives to the queue, sees the size of the line (or
some other attribute), and decides to leave the system.
◉ Bayes' theorem/Bayes' rule.
Answer: Fundamental rule of conditional probability:
𝑃(𝐴|𝐵)=𝑃(𝐵|𝐴)*𝑃(𝐴) / 𝑃(𝐵)
,◉ Bayesian Information criterion (BIC).
Answer: Model selection technique that trades off model fit and
model complexity. When comparing models, the model with lower
BIC is preferred. Generally penalizes complexity more than AIC.
◉ Bayesian Regression.
Answer: Regression model that incorporates estimates of how
coefficients and error are distributed.
◉ Bellman's Equation.
Answer: Equation used in dynamic programming that ensures
optimality of a solution.
◉ Bernoulli Distribution.
Answer: Discrete probability distribution where the outcome is
binary, either 0 or 1. Often, 1 represents success and 0 represents
failure. The probability of the outcome being 1 is 𝑝 and the
probability of outcome being 0 is 𝑞 = 1−𝑝, where 𝑝 is between 0 and
1.
◉ Bias.
Answer: Systematic difference between a true parameter of a
population and its estimate.
, ◉ Binary Data.
Answer: Data that can take only two different values (true/false,
0/1, black/white, on/off, etc.)
◉ Binary integer program.
Answer: Integer program where all variables are binary variables.
◉ Binary Variable.
Answer: Variable that can take just two values: 0 and 1.
◉ Binomial Distribution.
Answer: Discrete probability distribution for the exact number of
successes, k, out of a total of n iid Bernoulli trials, each with
probability p: Pr(𝑘)= (n over k) p^k(1-p)^n-k
◉ Blocking.
Answer: Factor introduced to an experimental design that interacts
with the effect of the factors to be studied. The effect of the factors is
studied within the same level (block) of the blocking factor.
◉ box and whisker plot.
WITH COMPLETE SOLUTIONS
◉ ARIMA.
Answer: Autoregressive integrated moving average.
◉ Arrival Rate.
Answer: Expected number of arrivals of people, things, etc. per unit
time -- for example, the expected number of truck deliveries per
hour to a warehouse.
◉ Assignment Problem.
Answer: Network optimization model with two sets of nodes, that
finds the best way to assign each node in one set to each node in the
other set.
◉ Attribute.
Answer: A characteristic or measurement - for example, a person's
height or the color of a car. Generally interchangeable with "feature",
and often with "covariate" or "predictor". In the standard tabular
format, a column of data.
◉ Autoregression.
,Answer: Regression technique using past values of time series data
as predictors of future values.
◉ Autoregressive integrated moving average (ARIMA).
Answer: Time series model that uses differences between
observations when data is nonstationary. Also called Box-Jenkins.
◉ Backward elimination.
Answer: Variable selection process that starts with all variables and
then iteratively removes the least-immediately-relevant variables
from the model.
◉ Balanced Design.
Answer: Set of combinations of factor values across multiple factors,
that has the same number of runs for all combinations of levels of
one or more factors.
◉ Balking.
Answer: An entity arrives to the queue, sees the size of the line (or
some other attribute), and decides to leave the system.
◉ Bayes' theorem/Bayes' rule.
Answer: Fundamental rule of conditional probability:
𝑃(𝐴|𝐵)=𝑃(𝐵|𝐴)*𝑃(𝐴) / 𝑃(𝐵)
,◉ Bayesian Information criterion (BIC).
Answer: Model selection technique that trades off model fit and
model complexity. When comparing models, the model with lower
BIC is preferred. Generally penalizes complexity more than AIC.
◉ Bayesian Regression.
Answer: Regression model that incorporates estimates of how
coefficients and error are distributed.
◉ Bellman's Equation.
Answer: Equation used in dynamic programming that ensures
optimality of a solution.
◉ Bernoulli Distribution.
Answer: Discrete probability distribution where the outcome is
binary, either 0 or 1. Often, 1 represents success and 0 represents
failure. The probability of the outcome being 1 is 𝑝 and the
probability of outcome being 0 is 𝑞 = 1−𝑝, where 𝑝 is between 0 and
1.
◉ Bias.
Answer: Systematic difference between a true parameter of a
population and its estimate.
, ◉ Binary Data.
Answer: Data that can take only two different values (true/false,
0/1, black/white, on/off, etc.)
◉ Binary integer program.
Answer: Integer program where all variables are binary variables.
◉ Binary Variable.
Answer: Variable that can take just two values: 0 and 1.
◉ Binomial Distribution.
Answer: Discrete probability distribution for the exact number of
successes, k, out of a total of n iid Bernoulli trials, each with
probability p: Pr(𝑘)= (n over k) p^k(1-p)^n-k
◉ Blocking.
Answer: Factor introduced to an experimental design that interacts
with the effect of the factors to be studied. The effect of the factors is
studied within the same level (block) of the blocking factor.
◉ box and whisker plot.
