ISYE 6501 Final EXAM REVIEW
QUESTIONS AND VERIFIED
CORRECT ANSWERS
GRADED A+ 100%
GUARANTEED PASS [ LATEST
2026-2027]
CUSUM - CORRECT ANSWER-Change detection model that keeps a running total of the amount
that observations vary above the expected value. The running total exceeds a preset threshold
value, it indicates there has been a change.
CUSUM Pros/Cons - CORRECT ANSWER-Pros: Best way to detect the small shifts of process
mean especially 0.5 to 2 SD from the target mean
Easy to identify visually the shifts in process mean
Cons: Cumbersome to establish and maintain
Tough to interpret the patterns.
Choosing C and T values is a pro and con as it can cause bias but creates more flexibility
Exponential Smoothing - CORRECT ANSWER-Technique regarding time series data in which
older observations are assigned exponentially decreasing weights, so more emphasis is given to
recent observations. Can include trends, seasonality, and cyclic patterns to account for expected
differences in observations over time
, Exponential Smoothing Pros and Cons - CORRECT ANSWER-Pros: Easy to learn and apply
Can produce accurate forecasts
can account for trends/seasonality/cyclic effects
Works well when mean/variance/etc are expected to remain relatively constant
Cons: Forecasts can sometimes have lag
ARIMA (Auto Regressive Integrated Moving Average) - CORRECT ANSWER-A time series analysis
method used for forecasting that combines three components: Differences in differences to find
stationary change when data metrics aren't stationary.
Autoregression, where predicting current value is based on previous time period values
Moving averages where we go back and incorporate q time periods' previous errors
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
QUESTIONS AND VERIFIED
CORRECT ANSWERS
GRADED A+ 100%
GUARANTEED PASS [ LATEST
2026-2027]
CUSUM - CORRECT ANSWER-Change detection model that keeps a running total of the amount
that observations vary above the expected value. The running total exceeds a preset threshold
value, it indicates there has been a change.
CUSUM Pros/Cons - CORRECT ANSWER-Pros: Best way to detect the small shifts of process
mean especially 0.5 to 2 SD from the target mean
Easy to identify visually the shifts in process mean
Cons: Cumbersome to establish and maintain
Tough to interpret the patterns.
Choosing C and T values is a pro and con as it can cause bias but creates more flexibility
Exponential Smoothing - CORRECT ANSWER-Technique regarding time series data in which
older observations are assigned exponentially decreasing weights, so more emphasis is given to
recent observations. Can include trends, seasonality, and cyclic patterns to account for expected
differences in observations over time
, Exponential Smoothing Pros and Cons - CORRECT ANSWER-Pros: Easy to learn and apply
Can produce accurate forecasts
can account for trends/seasonality/cyclic effects
Works well when mean/variance/etc are expected to remain relatively constant
Cons: Forecasts can sometimes have lag
ARIMA (Auto Regressive Integrated Moving Average) - CORRECT ANSWER-A time series analysis
method used for forecasting that combines three components: Differences in differences to find
stationary change when data metrics aren't stationary.
Autoregression, where predicting current value is based on previous time period values
Moving averages where we go back and incorporate q time periods' previous errors
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
