Table Of ContentBig Data, Business analytics, anD smart technology collection
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Mark Ferguson, Editor H
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BUSINESS ANALYTICS,
VOLUME II
BUSINESS ANALYTICS, VOLUME II
A Data-Driven Decision-Making Approach for Business
A Data-Driven Decision-Making
Amar Sahay, PhD
Approach for Business
This business analytics (BA) text discusses the models based on fact-based data to measure past business
performance to guide an organization in visualizing and predicting future business performance and outcomes.
It provides a comprehensive overview of analytics in general with an emphasis on predictive analytics. Given
the booming interest in analytics and data science, this book is timely and informative. It brings many terms,
tools, and methods of analytics together.
The first three chapters provide an introduction to BA, importance of analytics, types of BA–descriptive,
predictive, and prescriptive–along with the tools and models. Business intelligence (BI) and a case on B
U
descriptive analytics are discussed. Additionally, the book discusses the most widely used predictive models, SIN
including regression analysis, forecasting, data mining, and an introduction to recent applications of predictive ES
S
analytics–machine learning, neural networks, and artificial intelligence. The concluding chapter discusses the A
N
current state, job outlook, and certifications in analytics. A
L
Y
T
IC
Dr. Amar Sahay is a professor of decision sciences engaged in teaching, research, S
, V
consulting, and training. He holds a BS in production engineering (BIT, India), MS in industrial O
L
U
engineering, and a PhD in mechanical engineering--both from the University of Utah, USA. M
E
He has taught and is teaching at several institutions in Utah, including the University of Utah II
(School of Engineering and Management), SLCC, Westminster College, and others. Amar is
a certified Six Sigma Master Black Belt and is also lean manufacturing/lean management
certified. He has contributed a number of research papers in national and international journals/proceedings to
his credit. Amar has authored around 10 books in the areas of data visualization, business analytics, Six Sigma,
statistics and data analysis, modeling, and applied regression. He is also associated with QMS Global LLC, a
company engaged in data visualization, analytics, quality, lean six sigma, manufacturing, and systems analysis
services. Amar is a senior member of the Industrial & Systems Engineers, the American Society for Quality (ASQ),
and Data Science.
AMAR SAHAY, PhD
Business Analytics
Praise for Business Analytics
“In this second volume on business analytics, Dr. Sahay provides a
useful overview of analytics in general with an emphasis on predictive
analytics. Given the booming interest in analytics and data science, his
book is timely and informative. It brings many terms, tools, and methods
together in a meaningful way. It is common for practitioners and even
scholars to conflate terms such as business intelligence, data analytics,
and data mining. Dr. Sahay clarifies such terms and helps differentiate
their meanings. I found the glossaries at the end of the early chapters to
be especially useful in making sense of all of the terms that have emerged
recently and are often used interchangeably.
Being an expert on quality management and Six Sigma, Dr. Sahay also
incorporated quality tools into the analytics process, something that is
rare, but in my opinion extremely important and helpful. Moreover, his
treatment of the tools for predictive analytics not only explains the tools,
but goes a step further in clarifying when each should be used and how
the tools fit together. Such clarification is often presented in tabular form,
which makes it easy to refer back to whenever the information is needed.
Finally, I found the incorporation of practical examples to make the
concepts much more concrete. The implementation of the regression and
other tools in both Excel and Minitab makes them accessible to almost
all practitioners, which makes it much more likely that the reader will be
able to grasp the concepts and apply the tools in her or his own work.
I think that Business Analytics: A Data-Driven Decision-Making Approach
for Business Volume II (Predictive Analytics Model) will serve as a nice
introduction to anyone who wants an introduction to predictive business
analytics”.
—Don G. Wardell, Professor of Operations Management, David Eccles
School of Business, University of Utah, Salt Lake City, Utah
Business Analytics
A Data-Driven Decision-Making
Approach for Business
Volume II (Predictive Analytics)
Amar Sahay, PhD
Business Analytics: A Data-Driven Decision-Making Approach for Business,
Volume II (Predictive Analytics)
Copyright © Business Expert Press, LLC, 2020.
All rights reserved. No part of this publication may be reproduced, stored
in a retrieval system, or transmitted in any form or by any means—
electronic, mechanical, photocopy, recording, or any other except for
brief quotations, not to exceed 250 words, without the prior permission
of the publisher.
First published in 2020 by
Business Expert Press, LLC
222 East 46th Street, New York, NY 10017
www.businessexpertpress.com
ISBN-13: 978-1-63157-479-5 (paperback)
ISBN-13: 978-1-63157-480-1 (e-book)
Business Expert Press Big Data, Business Analytics, and Smart Technology
Collection
Collection ISSN: 2333-6749 (print)
Collection ISSN: 2333-6757 (electronic)
Cover and interior design by S4Carlisle Publishing Services Private Ltd.,
Chennai, India
Cover image licensed by Ingram Image, StockPhotoSecrets.com
First edition: 2020
10 9 8 7 6 5 4 3 2 1
Printed in the United States of America.
Dedication
This book is dedicated to
Priyanka Nicole
Our Love and Joy
Abstract
This business analytics (BA) text discusses the models based on fact-based
data to measure past business performance to guide an organization in
visualizing and predicting future business performance and outcomes.
It provides a comprehensive overview of analytics in general with an
emphasis on predictive analytics. Given the booming interest in analytics
and data science, this book is timely and informative. It brings many
terms, tools, and methods of analytics together. The first three chap-
ters provide an introduction to BA, importance of analytics, types of
BA— descriptive, predictive, and prescriptive—along with the tools and
models. Business intelligence (BI) and a case on descriptive analytics are
discussed. Additionally, the book discusses the most widely used predic-
tive models, including regression analysis, forecasting, data mining, and
an introduction to recent applications of predictive analytics— machine
learning, neural networks, and artificial intelligence. The concluding
chapter discusses the current state, job outlook, and certifications in
analytics.
Keywords
analytics; business analytics; business intelligence; data analysis; decision
making; descriptive analytics; predictive analytics; prescriptive analytics;
statistical analysis; quantitative techniques; data mining; predictive mod-
eling; regression analysis; modeling; time series forecasting; optimization;
simulation; machine learning; neural networks; artificial intelligence
Contents
Preface ..................................................................................................xi
Acknowledgments ................................................................................xvii
Chapter 1 Business Analytics at a Glance ...........................................1
Chapter 2 Business Analytics and Business Intelligence ....................23
Chapter 3 Analytics, Business Analytics, Data Analytics, and
How They Fit into the Broad Umbrella of Business
Intelligence .....................................................................33
Chapter 4 Descriptive Analytics—Overview, Applications,
and a Case .......................................................................57
Chapter 5 Descriptive versus Predictive Analytics ............................71
Chapter 6 Key Predictive Analytics Models (Predicting Future
Business Outcomes Using Analytic Models) ....................83
Chapter 7 Regression Analysis and Modeling .................................103
Chapter 8 Time Series Analysis and Forecasting .............................195
Chapter 9 Data Mining: Tools and Applications in Predictive
Analytics .......................................................................239
Chapter 10 Wrap-Up, Overview, Notes on Implementation, and
Current State of Business Analytics ...............................263
Appendices .........................................................................................281
Additional Readings ........................................................................... 373
About the Author ................................................................................377
Index .................................................................................................379
