ISYE6501 / ISYE 6501 Final Exam 1 (Latest Update 2025 / 2026) Intro to Analytics Modeling | Questions & Answers | Grade A | 100% Correct - 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 a lower value indicates more reliance on previous estimates.
Question:
Solution (in the optimization sense)
Answer:
A vector of values, one for each variable in an optimization model.
Question:
Specificity
Answer:
Fraction of data points not in a certain category that are correctly classified by a model; equal to TN/TN+FP; also called the true negative rate.
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Question:
Spline Regression
Answer:
Regression model where different functions are used for different ranges of the data.
Question:
Stable equilibrium
Answer:
A situation in game theory where, given each participant's current choice of action, no participant can do better by changing actions.
Question:
Standardization
Answer:
Transforming data by subtracting the mean and then dividing by standard deviation, so that it has mean 0 and variance 1.
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Question:
State
Answer:
Description of a system's condition.
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:
Steady state
Answer:
In a Markov chain, having the same probability distribution of being in each state, before and after a transition.
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