Isye 6501 Final exam question with answers
1-norm - -Similar to rectilinear distance; measures the straight-line length of a vector from the origin. If
z=(z1,z2,...,zm) is a vector in an m-dimensional space, then it's 1-norm is square root(|𝑧1|+|𝑧2|+⋯+|
𝑧𝑚| = |𝑧1|+|𝑧2|+⋯+|𝑧| = Σm over i=1 |𝑧𝑖|
-A/B Testing - -testing two alternatives to see which one performs better
-2-norm - -Similar to Euclidian distance; measures the straight-line length of a vector from the origin. If
z=(z1,z2,...,zm) is a vector in an 𝑚-dimensional space, then its 2-norm is the same as 1-norm but
everything is squared= square root(Σm over i=1 (|𝑧𝑖|)^2)
-Accuracy - -Fraction of data points correctly classified by a model; equal to TP+TN / TP+FP+TN+FN
-Action - -In ARENA, something that is done to an entity.
-Additive Seasonality - -Seasonal effect that is added to a baseline value (for example, "the temperature
in June is 10 degrees above the annual baseline").
-Adjusted R-squared - -Variant of R2 that encourages simpler models by penalizing the use of too many
variables.
-AIC - -Akaike information criterion- Model selection technique that trades off between model fit and
model complexity. When comparing models, the model with lower AIC is preferred. Generally penalizes
complexity less than BIC.
-Algorithm - -Step-by-step procedure designed to carry out a task.
,-Analysis of Variance/ANOVA - -Statistical method for dividing the variation in observations among
different sources.
-Approximate dynamic program - -Dynamic programming model where the value functions are
approximated.
-Arc - -Connection between two nodes/vertices in a network. In a network model, there is a variable for
each arc, equal to the amount of flow on the arc, and (optionally) a capacity constraint on the arc's flow.
Also called an edge.
-Area under the curve (AUC) - -Area under the ROC curve; an estimate of the classification model's
accuracy. Also called concordance index.
-ARIMA - -Autoregressive integrated moving average.
-Arrival Rate - -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 - -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 - -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 - -Regression technique using past values of time series data as predictors of future
values.
-Autoregressive integrated moving average (ARIMA) - -Time series model that uses differences between
observations when data is nonstationary. Also called Box-Jenkins.
, -Backward elimination - -Variable selection process that starts with all variables and then iteratively
removes the least-immediately-relevant variables from the model.
-Balanced Design - -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 - -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 - -Fundamental rule of conditional probability: 𝑃(𝐴|𝐵)=𝑃(𝐵|𝐴)*𝑃(𝐴) / 𝑃(𝐵)
-Bayesian Information criterion (BIC) - -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 - -Regression model that incorporates estimates of how coefficients and error are
distributed.
-Bellman's Equation - -Equation used in dynamic programming that ensures optimality of a solution.
-Bernoulli Distribution - -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 - -Systematic difference between a true parameter of a population and its estimate.
-Binary Data - -Data that can take only two different values (true/false, 0/1, black/white, on/off, etc.)
, -Binary integer program - -Integer program where all variables are binary variables.
-Binary Variable - -Variable that can take just two values: 0 and 1.
-Binomial Distribution - -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 - -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 - -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 - -Transformation of a non-normally-distributed response to a normal
distribution.
-Branching - -Splitting a set of data into two or more subsets, to each be analyzed separately.
-CART - -Classification and regression trees.
-Categorical Data - -Data that classifies observations without quantitative meaning (for example, colors
of cars) or where quantitative amounts are categorized (for example, "0-10, 11-20, ...").
-Causation - -Relationship in which one thing makes another happen (i.e., one thing causes another).
-Chance Constraint - -A probability-based constraint. For example, a standard linear constraint might be
𝐴x≤𝑏. A similar chance constraint might be Pr (𝐴x≤𝑏)≥0.95
1-norm - -Similar to rectilinear distance; measures the straight-line length of a vector from the origin. If
z=(z1,z2,...,zm) is a vector in an m-dimensional space, then it's 1-norm is square root(|𝑧1|+|𝑧2|+⋯+|
𝑧𝑚| = |𝑧1|+|𝑧2|+⋯+|𝑧| = Σm over i=1 |𝑧𝑖|
-A/B Testing - -testing two alternatives to see which one performs better
-2-norm - -Similar to Euclidian distance; measures the straight-line length of a vector from the origin. If
z=(z1,z2,...,zm) is a vector in an 𝑚-dimensional space, then its 2-norm is the same as 1-norm but
everything is squared= square root(Σm over i=1 (|𝑧𝑖|)^2)
-Accuracy - -Fraction of data points correctly classified by a model; equal to TP+TN / TP+FP+TN+FN
-Action - -In ARENA, something that is done to an entity.
-Additive Seasonality - -Seasonal effect that is added to a baseline value (for example, "the temperature
in June is 10 degrees above the annual baseline").
-Adjusted R-squared - -Variant of R2 that encourages simpler models by penalizing the use of too many
variables.
-AIC - -Akaike information criterion- Model selection technique that trades off between model fit and
model complexity. When comparing models, the model with lower AIC is preferred. Generally penalizes
complexity less than BIC.
-Algorithm - -Step-by-step procedure designed to carry out a task.
,-Analysis of Variance/ANOVA - -Statistical method for dividing the variation in observations among
different sources.
-Approximate dynamic program - -Dynamic programming model where the value functions are
approximated.
-Arc - -Connection between two nodes/vertices in a network. In a network model, there is a variable for
each arc, equal to the amount of flow on the arc, and (optionally) a capacity constraint on the arc's flow.
Also called an edge.
-Area under the curve (AUC) - -Area under the ROC curve; an estimate of the classification model's
accuracy. Also called concordance index.
-ARIMA - -Autoregressive integrated moving average.
-Arrival Rate - -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 - -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 - -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 - -Regression technique using past values of time series data as predictors of future
values.
-Autoregressive integrated moving average (ARIMA) - -Time series model that uses differences between
observations when data is nonstationary. Also called Box-Jenkins.
, -Backward elimination - -Variable selection process that starts with all variables and then iteratively
removes the least-immediately-relevant variables from the model.
-Balanced Design - -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 - -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 - -Fundamental rule of conditional probability: 𝑃(𝐴|𝐵)=𝑃(𝐵|𝐴)*𝑃(𝐴) / 𝑃(𝐵)
-Bayesian Information criterion (BIC) - -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 - -Regression model that incorporates estimates of how coefficients and error are
distributed.
-Bellman's Equation - -Equation used in dynamic programming that ensures optimality of a solution.
-Bernoulli Distribution - -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 - -Systematic difference between a true parameter of a population and its estimate.
-Binary Data - -Data that can take only two different values (true/false, 0/1, black/white, on/off, etc.)
, -Binary integer program - -Integer program where all variables are binary variables.
-Binary Variable - -Variable that can take just two values: 0 and 1.
-Binomial Distribution - -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 - -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 - -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 - -Transformation of a non-normally-distributed response to a normal
distribution.
-Branching - -Splitting a set of data into two or more subsets, to each be analyzed separately.
-CART - -Classification and regression trees.
-Categorical Data - -Data that classifies observations without quantitative meaning (for example, colors
of cars) or where quantitative amounts are categorized (for example, "0-10, 11-20, ...").
-Causation - -Relationship in which one thing makes another happen (i.e., one thing causes another).
-Chance Constraint - -A probability-based constraint. For example, a standard linear constraint might be
𝐴x≤𝑏. A similar chance constraint might be Pr (𝐴x≤𝑏)≥0.95
