ISYE6501 INTRODUCTION ANALYTICS
MODELING FINAL PAPER 2026 TESTED
QUESTIONS WITH VERIFIED ANSWERS
GRADE A
◉ Is written text structured or unstructured? Answer: Unstructured
◉ When we increase the sum of the square of the coefficients we...
Answer: Decrease the distance between the lines
◉ In SVM soft classifier we tradeoff between maximizing ___ and
minimizing ___ Answer: margin and errors
◉ If lambda gets small what gets emphasized, large margin or
minimizing training error?, Answer: Minimizing errors.
◉ What is a support vector? Answer: A point that holds up a shape.
◉ Does ...[⅔(a-1)+1/3(a+1)] move an SVM classifier up or down?
Answer: Up
◉ How do you make errors more costly in a soft SVM classifier?
Answer: include a multiplier for the point-error term.
,◉ If an SVM coefficient is very close to zero... Answer: that term is not
very important to the classification.
◉ What is the difference between standardization and scaling? Answer:
Scaling is bounded in range. Standardization is scaling to a normal
distribution. Standardization is the (value - factor mean) / (factor
standard deviation)
◉ What is the 2-norm? Answer: Euclidean distance
◉ What is the 1-norm? Answer: The rectilinear (Manhattan) distance
◉ What is the infinity norm? Answer: The value of the largest
dimension
◉ Measuring the quality of a model is called? Answer: Validation
◉ What does a confusion matrix show? Answer: The performance of a
classification model.
◉ A time series outlier that seems "off the curve" is called a... Answer:
contextual outlier.
◉ A data element that is different from all other data in a set is called a...
Answer: point outlier.
,◉ When something is missing in a range of points Answer: it is called
a..., collective outlier.
◉ The whiskers on a box plot extend to... Answer: the 10th and 90th
percentiles (or 5th and 95th)
◉ Why are hypothesis tests generally not sufficient for change
detection? Answer: They are slow to detect changes.
◉ In CUSUM, T is _____ and C is _____., Answer: Threshold and a
"bring down factor"
◉ In a CUSUM model, you adjust T and C to manage the tradeoff
between..., Answer: early detection and false-alarms
◉ In exponential smoothing, if the data is less random, then you want to
pick an alpha that is..., Answer: Close to 1.
◉ What is the initial condition for T in exponential smoothing with
trending? Answer: T_i=0
◉ In cyclic exponential smoothing, L represents..., Answer: The length
of the cycle or season
, ◉ In cyclic exponential smoothing, C_1 ... C_L = ___?, Answer: 1. In
other words, initialize it to no initial cycle.
◉ Exponential, trending and cyclic smoothing are also referred to as
Answer: single double and triple.
◉ Triple smoothing is also known as? Answer: Winter's or Holt-
Winter's
◉ What is the optimization formula for Exponential Smoothing?
Answer: min(F_t-Xt)^2 where alpha and beta are between 0 and 1.
◉ ARIMA stands for? Answer: Autoregressive Integrated Moving
Average
◉ Exponential smoothing is an order ___ autoregressive model.
Answer: Infinity. It uses data going all the way back.
◉ For ARIMA, the D parameter is used to specify ___. Answer: , The
order, or the differences of the differences of the differences (d-times.)
◉ For ARIMA, the P parameters is used to specify ____., Answer: The
order of periods (autoregression).
◉ For ARIMA, the Q parameter is used to specify ______., Answer:
The order of the moving average.
MODELING FINAL PAPER 2026 TESTED
QUESTIONS WITH VERIFIED ANSWERS
GRADE A
◉ Is written text structured or unstructured? Answer: Unstructured
◉ When we increase the sum of the square of the coefficients we...
Answer: Decrease the distance between the lines
◉ In SVM soft classifier we tradeoff between maximizing ___ and
minimizing ___ Answer: margin and errors
◉ If lambda gets small what gets emphasized, large margin or
minimizing training error?, Answer: Minimizing errors.
◉ What is a support vector? Answer: A point that holds up a shape.
◉ Does ...[⅔(a-1)+1/3(a+1)] move an SVM classifier up or down?
Answer: Up
◉ How do you make errors more costly in a soft SVM classifier?
Answer: include a multiplier for the point-error term.
,◉ If an SVM coefficient is very close to zero... Answer: that term is not
very important to the classification.
◉ What is the difference between standardization and scaling? Answer:
Scaling is bounded in range. Standardization is scaling to a normal
distribution. Standardization is the (value - factor mean) / (factor
standard deviation)
◉ What is the 2-norm? Answer: Euclidean distance
◉ What is the 1-norm? Answer: The rectilinear (Manhattan) distance
◉ What is the infinity norm? Answer: The value of the largest
dimension
◉ Measuring the quality of a model is called? Answer: Validation
◉ What does a confusion matrix show? Answer: The performance of a
classification model.
◉ A time series outlier that seems "off the curve" is called a... Answer:
contextual outlier.
◉ A data element that is different from all other data in a set is called a...
Answer: point outlier.
,◉ When something is missing in a range of points Answer: it is called
a..., collective outlier.
◉ The whiskers on a box plot extend to... Answer: the 10th and 90th
percentiles (or 5th and 95th)
◉ Why are hypothesis tests generally not sufficient for change
detection? Answer: They are slow to detect changes.
◉ In CUSUM, T is _____ and C is _____., Answer: Threshold and a
"bring down factor"
◉ In a CUSUM model, you adjust T and C to manage the tradeoff
between..., Answer: early detection and false-alarms
◉ In exponential smoothing, if the data is less random, then you want to
pick an alpha that is..., Answer: Close to 1.
◉ What is the initial condition for T in exponential smoothing with
trending? Answer: T_i=0
◉ In cyclic exponential smoothing, L represents..., Answer: The length
of the cycle or season
, ◉ In cyclic exponential smoothing, C_1 ... C_L = ___?, Answer: 1. In
other words, initialize it to no initial cycle.
◉ Exponential, trending and cyclic smoothing are also referred to as
Answer: single double and triple.
◉ Triple smoothing is also known as? Answer: Winter's or Holt-
Winter's
◉ What is the optimization formula for Exponential Smoothing?
Answer: min(F_t-Xt)^2 where alpha and beta are between 0 and 1.
◉ ARIMA stands for? Answer: Autoregressive Integrated Moving
Average
◉ Exponential smoothing is an order ___ autoregressive model.
Answer: Infinity. It uses data going all the way back.
◉ For ARIMA, the D parameter is used to specify ___. Answer: , The
order, or the differences of the differences of the differences (d-times.)
◉ For ARIMA, the P parameters is used to specify ____., Answer: The
order of periods (autoregression).
◉ For ARIMA, the Q parameter is used to specify ______., Answer:
The order of the moving average.
