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Isye 6501 Final exam Questions and answers | Updated 2024/25
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Isye 6501 Final exam Questions and answers | Updated 2024/25
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Isye 6501 Final exam Questions andanswers | Updated 2024/25
1-norm - Similar to rectilinear distance; measures the straight-line length of a vector from
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the origin. If z=(z1,z2,...,zm) is a vector in an m-dimensional space, then it's 1-norm is
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square root(|𝑧1|+|𝑧2|+⋯+|𝑧𝑚| = |𝑧1|+|𝑧2|+⋯+|𝑧| = Σm over i=1 |𝑧𝑖|
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ii A/B Testing - testing two alternatives to see which one performs better
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2-norm - Similar to Euclidian distance; measures the straight-line length of a vector from
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the origin. If z=(z1,z2,...,zm) is a vector in an 𝑚-dimensional space, then its 2-norm is the
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same as 1-norm but everything is squared= square root(Σm over i=1 (|𝑧𝑖|)^2)
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Accuracy - Fraction of data points correctly classified by a model; equal to TP+TN /
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TP+FP+TN+FN
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ii Action - In ARENA, something that is done to an entity.
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Additive Seasonality - Seasonal effect that is added to a baseline value (for example, "the
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temperature in June is 10 degrees above the annual baseline").
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Adjusted R-squared - Variant of R2 that encourages simpler models by penalizing the use
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of too many variables.
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AIC - Akaike information criterion- Model selection technique that trades off between
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model fit and model complexity. When comparing models, the model with lower AIC is
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preferred. Generally penalizes complexity less than BIC.
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ii Algorithm - Step-by-step procedure designed to carry out a task. ii ii ii ii ii ii ii ii ii ii ii
Analysis of Variance/ANOVA - Statistical method for dividing the variation in observations
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among different sources.
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Approximate dynamic program - Dynamic programming model where the value functions
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are approximated.
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Arc - Connection between two nodes/vertices in a network. In a network model, there is a
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variable for each arc, equal to the amount of flow on the arc, and (optionally) a capacity
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constraint on the arc's flow. Also called an edge.
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Area under the curve (AUC) - Area under the ROC curve; an estimate of the classification
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model's accuracy. Also called concordance index.
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ii ARIMA - Autoregressive integrated moving average.
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, Arrival Rate - Expected number of arrivals of people, things, etc. per unit time -- for
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example, the expected number of truck deliveries per hour to a warehouse.
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Assignment Problem - Network optimization model with two sets of nodes, that finds the
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best way to assign each node in one set to each node in the other set.
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Attribute - A characteristic or measurement - for example, a person's height or the color of
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a car. Generally interchangeable with "feature", and often with "covariate" or "predictor". In
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the standard tabular format, a column of data.
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Autoregression - Regression technique using past values of time series data as
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predictors of future values.
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Autoregressive integrated moving average (ARIMA) - Time series model that uses
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differences between observations when data is nonstationary. Also called Box-Jenkins.
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Backward elimination - Variable selection process that starts with all variables and then
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iteratively removes the least-immediately-relevant variables from the model.
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Balanced Design - Set of combinations of factor values across multiple factors, that has
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the same number of runs for all combinations of levels of one or more factors.
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Balking - An entity arrives to the queue, sees the size of the line (or some other attribute),
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and decides to leave the system.
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Bayes' theorem/Bayes' rule - Fundamental rule of conditional probability:
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𝑃(𝐴|𝐵)=𝑃(𝐵|𝐴)*𝑃(𝐴) / 𝑃(𝐵)
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Bayesian Information criterion (BIC) - Model selection technique that trades off model fit
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and model complexity. When comparing models, the model with lower BIC is preferred.
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Generally penalizes complexity more than AIC.
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Bayesian Regression - Regression model that incorporates estimates of how coefficients
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and error are distributed.
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Bellman's Equation - Equation used in dynamic programming that ensures optimality of a
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solution.
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Bernoulli Distribution - Discrete probability distribution where the outcome is binary, either
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0 or 1. Often, 1 represents success and 0 represents failure. The probability of the
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outcome being 1 is 𝑝 and the probability of outcome being 0 is 𝑞 = 1−𝑝, where 𝑝 is between
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0 and 1.
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ii Bias - Systematic difference between a true parameter of a population and its estimate.
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, Binary Data - Data that can take only two different values (true/false, 0/1, black/white,
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on/off, etc.)
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ii Binary integer program - Integer program where all variables are binary variables.
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ii Binary Variable - Variable that can take just two values: 0 and 1.
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Binomial Distribution - Discrete probability distribution for the exact number of successes,
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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
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Blocking - Factor introduced to an experimental design that interacts with the effect of the
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factors to be studied. The effect of the factors is studied within the same level (block) of the
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blocking factor.
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box and whisker plot - Graphical representation data showing the middle range of data
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(the "box"), reasonable ranges of variability ("whiskers"), and points (possible outliers)
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outside those ranges.
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Box-Cox Transformation - Transformation of a non-normally-distributed response to a
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normal distribution.
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Branching - Splitting a set of data into two or more subsets, to each be analyzed
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separately.
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ii CART - Classification and regression trees.
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Categorical Data - Data that classifies observations without quantitative meaning (for
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example, colors of cars) or where quantitative amounts are categorized (for example, "0-
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10, 11-20, ...").
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Causation - Relationship in which one thing makes another happen (i.e., one thing
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causes another).
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Chance Constraint - A probability-based constraint. For example, a standard linear
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constraint might be 𝐴x≤𝑏. A similar chance constraint might be Pr (𝐴x≤𝑏)≥0.95
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ii Change Detection - Identifying when a significant change has taken place in a process.
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Classification - The separation of data into two or more categories, or (a point's
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classification) the category a data point is put into.
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Classification tree - Tree-based method for classification. After branching to split the data,
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each subset is analyzed with its own classification model.
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Classifier - A boundary that separates the data into two or more categories. Also (more
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generally) an algorithm that performs classification.
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