ISYE 6501 ISYE6501 Midterm

ISYE 6501 / ISYE6501 Midterm Exam 1 (Latest Update 2025 / 2026) Intro to Analytics Modeling | Questions & Answers | 100% Correct | Grade A - Georgia Tech

Question:

Single exponential smoothing

Answer:

Exponential smoothing technique with just one parameter, that does not incorporate trend or seasonality.

Question:

Smoothing

Answer:

time series analysis technique to help filter out underlying randomness/noise.Examples include moving average, exponential smoothing, and ARIMA.

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Question:

Smoothing constant

Answer:

Parameter in exponential smoothing to determine the relative importance of recent observations and previous estimates. Smoothing constants are between 0 and 1; a higher value indicates more reliance on observation, and lower value indicates more reliance on previous estimates.

Question:

Stationary Process

Answer:

Process whose joint probability distribution and statistical properties (mean, variance, autocorrelation, etc.) do not vary with time. Examples include data with trends or cycles.

Question:

Trend

Answer:

Increase or decrease in data values over time.

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Question:

Triple Exponential smoothing

Answer:

Three parameter exponential smoothing technique that incorporates trend and seasonality; also called Holt-Winters.

Question:

1-norm

Answer:

similar to rectilinear distance; measures the sum of the lengths of each dimension of a vector from the origin.

Question:

2-norm

Answer:

similar to Euclidian distance; measures the straight-line length of a vector from the origin.

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Question:

Convex hull (of a set of points)

Answer:

smallest convex shape that the set of points is contained in.

Question:

Descriptive Analytics

Answer:

Loosely speaking, the use of analytics to explain or describe what has happened.

Question:

Distance

Answer:

How far it is between two points - but there are different ways to measure it

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