ACCESS Test Bank for Introduction to Management Science and Business
Analytics 7th Edition Hillier
CHAPTER 1
INTRODUCTION
Review Questions
1.1–1 The rapid development of the discipline began in the 1940’s and 1950’s.
1.1–2 The traditional name given to the discipline is operations research.
1.1–3 A management science study provides an analysis and recommendations, based on the
quantitative factors involved in the problem, as input to the managers.
1.1–4 Management science is based strongly on some scientific fields, including mathematics and
computer science. It also draws upon the social sciences, especially economics.
1.1–5 Many managerial problems revolve around such quantitative factors as production quantities,
revenues, costs, the amounts available of needed resources, etc.
1.2–1 Business analytics has drawn on various other quantitative decision sciences, including
management science, mathematics, statistics, computer science, information technology,
industrial engineering, etc.
1.2–2 The era of big data is where massive amounts of data (accompanied by massive amounts of
computational power) are now commonly available to many businesses to help guide
managerial decision making. A primary focus of business analytics is on how to make the most
effective use of all these data.
1.2–3 Descriptive analytics uses innovative techniques (including algorithms) to explore the data,
locate and extract the data that are relevant, and then identify the interesting patterns and
summary data.
1.2–4 Predictive analytics often involves applying statistical models to predict future events or trends.
1.2–5 Prescriptive analytics uses powerful techniques drawn mainly from management science to
prescribe what should be done in the future.
1.2–6 Data science tends to be more interdisciplinary, more based on scientific methods, more
applicable to various areas in addition to business, and more concerned with how to deal with
even massive amounts of data in various forms.
1.2–7 The goal of machine learning is to allow computers to learn automatically from historical
relationships and trends in the data in order to do such things as making data-driven predictions.
Machine learning is a popular method for applying predictive analytics by performing pattern
recognition.
1.2–8 Machine learning is just one important part of artificial intelligence. Because its focus is on
automatically learning from historical relationships and trends in the data, machine learning
provides an ideal platform for performing artificial intelligence. The difficult part then is taking
the next step to use this platform to truly simulate human thinking capability and behavior.
1–1
© McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.
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Analytics 7th Edition Hillier
CHAPTER 1
INTRODUCTION
Review Questions
1.1–1 The rapid development of the discipline began in the 1940’s and 1950’s.
1.1–2 The traditional name given to the discipline is operations research.
1.1–3 A management science study provides an analysis and recommendations, based on the
quantitative factors involved in the problem, as input to the managers.
1.1–4 Management science is based strongly on some scientific fields, including mathematics and
computer science. It also draws upon the social sciences, especially economics.
1.1–5 Many managerial problems revolve around such quantitative factors as production quantities,
revenues, costs, the amounts available of needed resources, etc.
1.2–1 Business analytics has drawn on various other quantitative decision sciences, including
management science, mathematics, statistics, computer science, information technology,
industrial engineering, etc.
1.2–2 The era of big data is where massive amounts of data (accompanied by massive amounts of
computational power) are now commonly available to many businesses to help guide
managerial decision making. A primary focus of business analytics is on how to make the most
effective use of all these data.
1.2–3 Descriptive analytics uses innovative techniques (including algorithms) to explore the data,
locate and extract the data that are relevant, and then identify the interesting patterns and
summary data.
1.2–4 Predictive analytics often involves applying statistical models to predict future events or trends.
1.2–5 Prescriptive analytics uses powerful techniques drawn mainly from management science to
prescribe what should be done in the future.
1.2–6 Data science tends to be more interdisciplinary, more based on scientific methods, more
applicable to various areas in addition to business, and more concerned with how to deal with
even massive amounts of data in various forms.
1.2–7 The goal of machine learning is to allow computers to learn automatically from historical
relationships and trends in the data in order to do such things as making data-driven predictions.
Machine learning is a popular method for applying predictive analytics by performing pattern
recognition.
1.2–8 Machine learning is just one important part of artificial intelligence. Because its focus is on
automatically learning from historical relationships and trends in the data, machine learning
provides an ideal platform for performing artificial intelligence. The difficult part then is taking
the next step to use this platform to truly simulate human thinking capability and behavior.
1–1
© McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.
mynursytest.store
