ISYE 6501 FINAL EXAM QUIZ VERSION 2
COMPREHENSIVE STUDY GUIDE WITH
SOLUTIONS
◉ 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.
Answer: Graphical representation data showing the middle range of
data (the "box"), reasonable ranges of variability ("whiskers"), and
points (possible outliers) outside those ranges.
◉ Box-Cox Transformation.
Answer: Transformation of a non-normally-distributed response to a
normal distribution.
◉ Branching.
Answer: Splitting a set of data into two or more subsets, to each be
analyzed separately.
◉ CART.
Answer: Classification and regression trees.
◉ Categorical Data.
Answer: Data that classifies observations without quantitative
meaning (for example, colors of cars) or where quantitative amounts
are categorized (for example, "0-10, 11-20, ...").
COMPREHENSIVE STUDY GUIDE WITH
SOLUTIONS
◉ 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.
Answer: Graphical representation data showing the middle range of
data (the "box"), reasonable ranges of variability ("whiskers"), and
points (possible outliers) outside those ranges.
◉ Box-Cox Transformation.
Answer: Transformation of a non-normally-distributed response to a
normal distribution.
◉ Branching.
Answer: Splitting a set of data into two or more subsets, to each be
analyzed separately.
◉ CART.
Answer: Classification and regression trees.
◉ Categorical Data.
Answer: Data that classifies observations without quantitative
meaning (for example, colors of cars) or where quantitative amounts
are categorized (for example, "0-10, 11-20, ...").
