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ISYE 6501 | midterm 1 | industrial engineering | systems engineering | probability | statistics | regression analysis | simulation | optimization | data analysis | engineering mathematics | stochastic processes | decision making | exam review

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**Title:** ISYE 6501 – Midterm 1 – industrial and systems engineering exam review & assessment material **Description:** This document is the ISYE 6501 Midterm 1, covering core topics in industrial and systems engineering with a focus on analytical methods and data-driven decision-making. It includes foundational concepts such as probability, statistics, regression analysis, simulation, and optimization as applied in engineering systems. The material is designed to support exam preparation by strengthening quantitative reasoning, modeling skills, and problem-solving in systems engineering contexts. **Keywords:** ISYE 6501 | midterm 1 | industrial engineering | systems engineering | probability | statistics | regression analysis | simulation | optimization | data analysis | engineering mathematics | stochastic processes | decision making | exam review

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Institution
ISYE 6501
Course
ISYE 6501

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What do descriptive questions ask?


What happened? (e.g., which customers are most alike)


What do predictive questions ask?


What will happen? (e.g., what will Google's stock price be?)


What do prescriptive questions ask?


What action(s) would be best? (e.g., where to put traffic lights)


What is a model?


Real-life situation expressed as math.


What do classifiers help you do?


differentiate

,What is a soft classifier and when is it used?


In some cases, there won't be a line that separates all of the labeled examples. So we use a classifier
that minimizes the number of mistakes.


What does it mean when the classifier/decision boundary is almost parallel to the vertical x-axis?


The horizontal attribute is all that is needed.


What does it mean when the classifier/decision boundary is almost parallel to the horizontal y-axis?


The vertical attribute is all that is needed.


What is time-series data?


The same data recorded over time often recorded at equal intervals


What is quantitative data?


Number with a meaning: higher means more, lower means less (e.g., age, sales, temperature, income)

,What is categorical data?


Numbers w/o meaning (e.g., zip codes), non-numeric (e.g., hair color), binary data (e.g., male/female,
yes/no, on/off)


Which of these is time series data?
A. The average cost of a house in the United States every year since 1820
B. The height of each professional basketball player in the NBA at the start of the season


A


Which of these is structured data?
A. The contents of a person's Twitter feed
B. The amount of money in a person's bank account


B


What is structured data?


Data that can be stores in a structured way

, What is unstructured data?


Data that is not easily described and stored (e.g., written text)


A survey of 25 people recorded each person's family size and type of car. Which of these is a data
point?
A. The 14th person's family size and car type
B. The 14th person's family size
C.The car type of each person


A.
A data point is all the information about one observation


The farther the wrongly classified point is from the line ___


The bigger the mistake we've made

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ISYE 6501
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ISYE 6501

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Uploaded on
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