Isye 6501 Final exam
Study online at https://quizlet.com/_7nre5z
1. 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(|5g 1|+|5g
2|+ï+|5g5Z
| = |5g
1|+|5g2|+ï+|5g
| = Σm over i=1 |5g5V
|
2. A/B Testing testing two alternatives to see which one performs better
3. 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 5Z
-dimensional space, then its 2-norm is
the same as 1-norm but everything is squared= square root(Σm over i=1 (|5g5V |)^2)
4. Accuracy Fraction of data points correctly classified by a model; equal to TP+TN /
TP+FP+TN+FN
5. Action In ARENA, something that is done to an entity.
6. Additive Season- Seasonal effect that is added to a baseline value (for example, "the temperature
ality in June is 10 degrees above the annual baseline").
7. Adjusted Variant of R2 that encourages simpler models by penalizing the use of too many
R-squared variables.
8. 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.
9. Algorithm Step-by-step procedure designed to carry out a task.
10. Analysis of Vari- Statistical method for dividing the variation in observations among different
ance/ANOVA sources.
11. Approximate dy- Dynamic programming model where the value functions are approximated.
namic program
12. Arc
, Isye 6501 Final exam
Study online at https://quizlet.com/_7nre5z
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.
13. Area under the Area under the ROC curve; an estimate of the classification model's accuracy. Also
curve (AUC) called concordance index.
14. ARIMA Autoregressive integrated moving average.
15. 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.
16. Assignment Network optimization model with two sets of nodes, that finds the best way to
Problem assign each node in one set to each node in the other set.
17. 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.
18. Autoregression Regression technique using past values of time series data as predictors of future
values.
19. Autoregressive Time series model that uses differences between observations when data is
integrated nonstationary. Also called Box-Jenkins.
moving average
(ARIMA)
20. Backward elimi- Variable selection process that starts with all variables and then iteratively removes
nation the least-immediately-relevant variables from the model.
21. 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.
22. Balking
, Isye 6501 Final exam
Study online at https://quizlet.com/_7nre5z
An entity arrives to the queue, sees the size of the line (or some other attribute),
and decides to leave the system.
23. Bayes' theo- Fundamental rule of conditional probability: 5C
(54
|55
)=5C
(55
|54
)*5C
(54
) / 5C
(55
)
rem/Bayes' rule
24. Bayesian Infor- Model selection technique that trades off model fit and model complexity. When
mation criterion comparing models, the model with lower BIC is preferred. Generally penalizes
(BIC) complexity more than AIC.
25. Bayesian Regres- Regression model that incorporates estimates of how coefficients and error are
sion distributed.
26. Bellman's Equa- Equation used in dynamic programming that ensures optimality of a solution.
tion
27. Bernoulli Distrib- Discrete probability distribution where the outcome is binary, either 0 or 1. Often,
ution 1 represents success and 0 represents failure. The probability of the outcome
being 1 is 5]
and the probability of outcome being 0 is 5^= 1−5]
, where 5]is between 0
and 1.
28. Bias Systematic difference between a true parameter of a population and its estimate.
29. Binary Data Data that can take only two different values (true/false, 0/1, black/white, on/off,
etc.)
30. Binary integer Integer program where all variables are binary variables.
program
31. Binary Variable Variable that can take just two values: 0 and 1.
32. Binomial Distrib- Discrete probability distribution for the exact number of successes, k, out of a total
ution of n iid Bernoulli trials, each with probability p: Pr(5X
)= (n over k) p^k(1-p)^n-k
, Isye 6501 Final exam
Study online at https://quizlet.com/_7nre5z
33. 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.
34. box and whisker Graphical representation data showing the middle range of data (the "box"), rea-
plot sonable ranges of variability ("whiskers"), and points (possible outliers) outside
those ranges.
35. Box-Cox Trans- Transformation of a non-normally-distributed response to a normal distribution.
formation
36. Branching Splitting a set of data into two or more subsets, to each be analyzed separately.
37. CART Classification and regression trees.
38. 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,
...").
39. Causation Relationship in which one thing makes another happen (i.e., one thing causes
another).
40. Chance Con- A probability-based constraint. For example, a standard linear constraint might be
straint xd
.5AOsimilar chance constraint might be Pr (54
54 xd
50O
)e.95
41. Change Detec- Identifying when a significant change has taken place in a process.
tion
42. Classification The separation of data into two or more categories, or (a point's classification) the
category a data point is put into.
43. Classification Tree-based method for classification. After branching to split the data, each subset
tree is analyzed with its own classification model.
Study online at https://quizlet.com/_7nre5z
1. 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(|5g 1|+|5g
2|+ï+|5g5Z
| = |5g
1|+|5g2|+ï+|5g
| = Σm over i=1 |5g5V
|
2. A/B Testing testing two alternatives to see which one performs better
3. 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 5Z
-dimensional space, then its 2-norm is
the same as 1-norm but everything is squared= square root(Σm over i=1 (|5g5V |)^2)
4. Accuracy Fraction of data points correctly classified by a model; equal to TP+TN /
TP+FP+TN+FN
5. Action In ARENA, something that is done to an entity.
6. Additive Season- Seasonal effect that is added to a baseline value (for example, "the temperature
ality in June is 10 degrees above the annual baseline").
7. Adjusted Variant of R2 that encourages simpler models by penalizing the use of too many
R-squared variables.
8. 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.
9. Algorithm Step-by-step procedure designed to carry out a task.
10. Analysis of Vari- Statistical method for dividing the variation in observations among different
ance/ANOVA sources.
11. Approximate dy- Dynamic programming model where the value functions are approximated.
namic program
12. Arc
, Isye 6501 Final exam
Study online at https://quizlet.com/_7nre5z
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.
13. Area under the Area under the ROC curve; an estimate of the classification model's accuracy. Also
curve (AUC) called concordance index.
14. ARIMA Autoregressive integrated moving average.
15. 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.
16. Assignment Network optimization model with two sets of nodes, that finds the best way to
Problem assign each node in one set to each node in the other set.
17. 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.
18. Autoregression Regression technique using past values of time series data as predictors of future
values.
19. Autoregressive Time series model that uses differences between observations when data is
integrated nonstationary. Also called Box-Jenkins.
moving average
(ARIMA)
20. Backward elimi- Variable selection process that starts with all variables and then iteratively removes
nation the least-immediately-relevant variables from the model.
21. 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.
22. Balking
, Isye 6501 Final exam
Study online at https://quizlet.com/_7nre5z
An entity arrives to the queue, sees the size of the line (or some other attribute),
and decides to leave the system.
23. Bayes' theo- Fundamental rule of conditional probability: 5C
(54
|55
)=5C
(55
|54
)*5C
(54
) / 5C
(55
)
rem/Bayes' rule
24. Bayesian Infor- Model selection technique that trades off model fit and model complexity. When
mation criterion comparing models, the model with lower BIC is preferred. Generally penalizes
(BIC) complexity more than AIC.
25. Bayesian Regres- Regression model that incorporates estimates of how coefficients and error are
sion distributed.
26. Bellman's Equa- Equation used in dynamic programming that ensures optimality of a solution.
tion
27. Bernoulli Distrib- Discrete probability distribution where the outcome is binary, either 0 or 1. Often,
ution 1 represents success and 0 represents failure. The probability of the outcome
being 1 is 5]
and the probability of outcome being 0 is 5^= 1−5]
, where 5]is between 0
and 1.
28. Bias Systematic difference between a true parameter of a population and its estimate.
29. Binary Data Data that can take only two different values (true/false, 0/1, black/white, on/off,
etc.)
30. Binary integer Integer program where all variables are binary variables.
program
31. Binary Variable Variable that can take just two values: 0 and 1.
32. Binomial Distrib- Discrete probability distribution for the exact number of successes, k, out of a total
ution of n iid Bernoulli trials, each with probability p: Pr(5X
)= (n over k) p^k(1-p)^n-k
, Isye 6501 Final exam
Study online at https://quizlet.com/_7nre5z
33. 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.
34. box and whisker Graphical representation data showing the middle range of data (the "box"), rea-
plot sonable ranges of variability ("whiskers"), and points (possible outliers) outside
those ranges.
35. Box-Cox Trans- Transformation of a non-normally-distributed response to a normal distribution.
formation
36. Branching Splitting a set of data into two or more subsets, to each be analyzed separately.
37. CART Classification and regression trees.
38. 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,
...").
39. Causation Relationship in which one thing makes another happen (i.e., one thing causes
another).
40. Chance Con- A probability-based constraint. For example, a standard linear constraint might be
straint xd
.5AOsimilar chance constraint might be Pr (54
54 xd
50O
)e.95
41. Change Detec- Identifying when a significant change has taken place in a process.
tion
42. Classification The separation of data into two or more categories, or (a point's classification) the
category a data point is put into.
43. Classification Tree-based method for classification. After branching to split the data, each subset
tree is analyzed with its own classification model.
