ISYE 6501 Final QUESTIONS
AND VERIFIED CORRECT
ANSWERS GRADED A+ 100%
GUARANTEED PASS [ LATEST
2026-2027]
GARCH (Generalized Autoregressive Conditional
Heteroskedasticity) - CORRECT ANSWER-Time series analytic method that estimates/forecasts
variance. Helps determine how much a forecast may be higher or lower than the true value.
Useful for estimating risks on investment portfolios.
Linear Regression - CORRECT ANSWER-A Regression technique that describes relationships
between independent and dependent variables as linear functions
AIC (Akaike information criterion) - CORRECT ANSWER-Model selection technique that balances
model fit and complexity. Penalizes models with too much complexity in an attempt to avoid
overfitting.
BIC (Bayesian information criterion) - CORRECT ANSWER-Model election technique that
balances model fit and complexity. Generally penalizes complexity more than AIC.
Box-Cox Transformation - CORRECT ANSWER-Logarithmic transformation technique used to
eliminate heteroskedasticity (unequal variance) across a data set to make it fit a normal
, distribution. This will help a model avoid being biased towards parts of the data with greater
variance.
PCA (principal component analysis) - CORRECT ANSWER-Feature extraction technique that can
reduce the dimensionality of the feature space in a model, creating new components ordered
by importance, while also transforming the data around new orthogonal axis in order to remove
correlation.
PCA Pros and Cons - CORRECT ANSWER-Pros: Removes correlated features
Can improve algorithm performance by omitting correlated variables
Reduces over fitting
Improves visualization
Cons: Harder to interpret effects of individual independent variables
Requires data standardization
CART (classification and regression trees) - CORRECT ANSWER-Regression technique that
creates different models for different subset of data. This is useful when different factors may
have different ways they interact that makes them more effective to model separately. For
example when modeling likeliness of response to advertisement, may be useful to divide
population into age group and marital status.
Branching - CORRECT ANSWER-The process of deciding how to split the data, and which factors
to include in a CART model. Can go either backwards, removing leaves that don't decrease
variance more than a certain threshold, or forward by adding leaves until they either don't
improve the model enough or don't contain enough data.
Random Forests - CORRECT ANSWER-Method where a multitude of CARTs are produced, using
randomized subset of data and randomized factors. For regression you then use the average
predicted response across the trees, and for classification you use the most common predicted
response/
AND VERIFIED CORRECT
ANSWERS GRADED A+ 100%
GUARANTEED PASS [ LATEST
2026-2027]
GARCH (Generalized Autoregressive Conditional
Heteroskedasticity) - CORRECT ANSWER-Time series analytic method that estimates/forecasts
variance. Helps determine how much a forecast may be higher or lower than the true value.
Useful for estimating risks on investment portfolios.
Linear Regression - CORRECT ANSWER-A Regression technique that describes relationships
between independent and dependent variables as linear functions
AIC (Akaike information criterion) - CORRECT ANSWER-Model selection technique that balances
model fit and complexity. Penalizes models with too much complexity in an attempt to avoid
overfitting.
BIC (Bayesian information criterion) - CORRECT ANSWER-Model election technique that
balances model fit and complexity. Generally penalizes complexity more than AIC.
Box-Cox Transformation - CORRECT ANSWER-Logarithmic transformation technique used to
eliminate heteroskedasticity (unequal variance) across a data set to make it fit a normal
, distribution. This will help a model avoid being biased towards parts of the data with greater
variance.
PCA (principal component analysis) - CORRECT ANSWER-Feature extraction technique that can
reduce the dimensionality of the feature space in a model, creating new components ordered
by importance, while also transforming the data around new orthogonal axis in order to remove
correlation.
PCA Pros and Cons - CORRECT ANSWER-Pros: Removes correlated features
Can improve algorithm performance by omitting correlated variables
Reduces over fitting
Improves visualization
Cons: Harder to interpret effects of individual independent variables
Requires data standardization
CART (classification and regression trees) - CORRECT ANSWER-Regression technique that
creates different models for different subset of data. This is useful when different factors may
have different ways they interact that makes them more effective to model separately. For
example when modeling likeliness of response to advertisement, may be useful to divide
population into age group and marital status.
Branching - CORRECT ANSWER-The process of deciding how to split the data, and which factors
to include in a CART model. Can go either backwards, removing leaves that don't decrease
variance more than a certain threshold, or forward by adding leaves until they either don't
improve the model enough or don't contain enough data.
Random Forests - CORRECT ANSWER-Method where a multitude of CARTs are produced, using
randomized subset of data and randomized factors. For regression you then use the average
predicted response across the trees, and for classification you use the most common predicted
response/
