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.
- / 4
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.
- / 4
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.
- / 4
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
- / 4