Fluent Essays

This weeks article provided a case study approach which highlights how businesses have integrated Big Data Analytics with their Business Intelligence to gain dominance within their respective industry.  Search the UC Library and/or Google Scholar for a Fortune 1000 company that has been successful in this integration. Discuss the company, its approach to big data analytics with business intelligence, what they are doing right, what they are doing wrong, and how they can improve to be more successful in the implementation and maintenance of big data analytics with business intelligence   Your paper should meet these requirements:   Be approximately FIVE full pages in length, not including the required cover page and reference page. Follow APA 7 guidelines. Your paper should include an introduction, a body with fully developed content, and a conclusion. Support your answers with the readings from the course and at least two scholarly journal articles to support your positions, claims, and observations, in addition to your textbook. The UC Library is a great place to find resources. Be clearly and well-written, concise, and logical, using excellent grammar and style techniques. You are being graded in part on the quality of your writing.     Reading Assignments Reading AssignmentsElhoseny, M., Kabir Hassan, M., & Kumar Singh, A. (2020). Special issue on cognitive big data analytics for business intelligence applications: Towards performance improvement. International Journal of Information Management, 50, 413–415. https://doi.org/10.1016/j.ijinfomgt.2019.08.004Integrated Understanding of Big Data, Big Data Analysis, and Business Intelligence: A Case Study of Logistics. Sustainability 2018, 10(10), 3778; https://doi.org/10.3390/su10103778 There is a PDF link above the Abstract. Krivo, A., & Mirvoda, S. (2020). The Experience of Cyberthreats Analysis Using Business Intelligence System. 2020 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT), Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT, 2020 Ural Symposium On, 0619–0622. https://doi.org/10.1109/USBEREIT48449.2020.9117694 Data Science & Big Data Analytics Discovering, Analyzing, Visualizing and Presenting Data EMC Education Services WILEY Data Science & Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data Published by John Wiley & Sons, Inc. 10475 Crosspoint Boulevard Indianapolis, IN 46256 www. wiley. com Copyright© 2015 by John Wiley & Sons, Inc., Indianapolis, Indiana Published simultaneously in Canada ISBN: 978-1-118-87613-8 ISBN: 978-1-118-87622-0 (ebk) ISBN: 978-1-118-87605-3 (ebk) Manufactured in the United States of America 10987654321 No part ofthis publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except as permitted under Sections 107 or 108 of the 1976 United States Copyright Act, without either the prior written permis- sion of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 646-8600. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008, or online at http: I /www. wiley. com/ go/permissions. limit ofliability/DisclaimerofWarranty: The publisher and the author make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation warranties of fitness for a particular purpose. No warranty may be created or extended by sales or promotional materials. The advice and strategies contained herein may not be suitable for every situation. This work is sold with the understanding that the publisher is not engaged in rendering legal, accounting, or other professional services. If professional assistance is required, the services of a competent professional person should be sought. 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Some material included with standard print versions of this book may not be included in e-books or in print-on-demand.lf this book refers to media such as a CD or DVD that is not included in the version you purchased, you may download this material at http: I /book support. wiley. com. For more information about Wiley products, visit www. wiley. com. library of Congress Control Number: 2014946681 Trademarks: Wiley and the Wiley logo are trademarks or registered trademarks of John Wiley & Sons, Inc. and/or its affiliates, in the United States and other coun- tries, and may not be used without written permission. All other trademarks are the property of their respective owners. John Wiley & Sons, Inc. is not associated with any product or vendor mentioned in this book. Credits Executive Editor Carol Long Project Editor Kelly Talbot Production Manager Kathleen Wisor Copy Editor Karen Gill Manager of Content Development and Assembly Mary Beth Wakefield Marketing Director David Mayhew Marketing Manager Carrie Sherrill Professional Technology and Strategy Director Ba rry Pruett Business Manager Amy Knies Associate Publisher Jim Minatel Project Coordinator, Cover Patrick Redmond Proofreader Nancy Carrasco Indexer Johnna Van Hoose Dinse Cover Designer Mallesh Gurram About the Key Contributors David Dietrich heads the data science education team within EMC Education Services, where he leads the curriculum, strategy and course development related to Big Data Analytics and Data Science. He co-au- thored the first course in EMCs Data Science curriculum, two additional EMC courses focused on teaching leaders and executives about Big Data and data science, and is a contributing author and editor of this book. He has filed 14 patents in the areas of data science, data privacy, and cloud computing. David has been an advisor to severa l universities looking to develop academic programs related to data analytics, and has been a frequent speaker at conferences and industry events. He also has been a a guest lecturer at universi- ties in the Boston area. His work has been featured in major publications including Forbes, Harvard Business Review, and the 2014 Massachusetts Big Data Report, commissioned by Governor Deval Patrick. Involved with analytics and technology for nearly 20 years, David has worked with many Fortune 500 companies over his career, holding mu lti ple roles involving analytics, including managing ana lytics and operations teams, delivering analytic con- sulting engagements, managing a line of analytical software products for regulating the US banking industry, and developing Sohware-as-a-Service and BI-as-a-Service offerings. Additionally, David collaborated with the U.S. Federal Reserve in develop- ing predictive models for monitoring mortgage portfolios. Barry Heller is an advisory technical education consultant at EMC Education Services. Barry is a course developer and cu r- riculum advisor in the emerging technology areas of Big Data and data science. Prior to his current role, Barry was a consul- tant research scientist leadi ng numerous analytical initiatives within EMCs Total Customer Experience organization. Early in his EMC career, he managed the statistical engineering group as well as led the data warehousing efforts in an Enterprise Resource Planning (ERP) implementation. Prior to joining EMC, Barry held managerial and analytical roles in reliability engineering functions at medical diagnostic and technology companies. During his career, he has applied his quantitative skill set to a myriad of business applications in the Customer Service, Engineering, Ma nufacturing, Sales/Marketing, Finance, and Legal arenas. Underscoring the importance of strong executive stakeholder engagement, many of his successes have resulted from not only focusing on the technical details of an analysis, but on the decisions that will be resulting from the analysis. Barry earned a B.S. in Computational Mathematics from the Rochester Institute ofTechnology and an M.A. in Mathematics from the State University of New York (SUNY) New Paltz. Beibei Yang is a Technical Education Consultant of EMC Education Services, responsible for developing severa l open courses at EMC related to Data Science and Big Data Analytics. Beibei has seven years of experi ence in the IT industry. Prior to EMC she worked as a sohware engineer, systems manager, and network manager for a Fortune 500 company where she introduced new technologies to improve efficiency and encourage collaboration. Beibei has published papers to prestigious conferences and has filed multiple patents. She received her Ph.D. in computer science from the University of Massachusetts Lowell. She has a passion toward natural language processing and data mining, especially using various tools and techniques to find hidden patterns and tell storie s with data. Data Science and Big Data Analytics is an exciting domain where the potential of digital information is maximized for making intelligent business decisions. We believe that this is an area that will attract a lot of talented students and professiona ls in the short, mid, and long term. Acknowledgments EMC Education Services embarked on learning this subject with the intent to develop an open curriculum and certification. It was a challenging journey at the time as not many understood what it would take to be a true data scientist. After initial research (and struggle), we were able to define what was needed and attract very talented professionals to work on the project. The course, Data Science and Big Data Analytics, has become well accepted across academia and the industry. Led by EMC Education Services, this book is the result of efforts and contributions from a number of key EMC organizations and supported by the office of the CTO, IT, Global Services, and Engi neering. Many sincere thanks to many key contributors and subject matter experts David Dietrich, Barry Heller, and Beibei Yang for their work developing content and graphics for the chapters. A special thanks to subject matter experts John Cardente and Ganesh Rajaratnam for their active involvement reviewing multiple book chapters and providing valuable feedback throughout the project. We are also grateful to the fol lowing experts from EMC and Pivotal for their support in reviewing and improving the content in this book: Aidan OBrien Joe Kambourakis Alexander Nunes Joe Milardo Bryan Miletich John Sopka Dan Baskette Kathryn Stiles Daniel Mepham Ken Taylor Dave Reiner Lanette Wells Deborah Stokes Michael Hancock Ellis Kriesberg Michael Vander Donk Frank Coleman Narayana n Krishnakumar Hisham Arafat Richard Moore Ira Sch ild Ron Glick Jack Harwood Stephen Maloney Jim McGroddy Steve Todd Jody Goncalves Suresh Thankappan Joe Dery Tom McGowa n We also thank Ira Schild and Shane Goodrich for coordinating this project, Mallesh Gurram for the cover design, Chris Conroy and Rob Bradley for graphics, and the publisher, John Wiley and Sons, for timely support in bringing this book to the industry. Nancy Gessler Director, Education Services, EMC Corporation Alok Shrivastava Sr. Direc tor, Education Services, EMC Corporation Contents Introduction ................ . .. . .....• . •.. ... .... •..... .. .. . .. . .......... .. ... . ..................... •.•...... xvii Chapter 1 • Introduction to Big Data Analytics ................... . . . ....................... 1 1.1 Big Data Overview ..................... ....... .....•... • ...... . . . ........ • .. ... . . ... ....... ....... 2 1.1.1 Data Structures .. . .. . . . .. ................ ... ... . .. . ...... . .. .. .... . .................... ..... . .. . . . .. 5 1.1.2 Analyst Perspective on Data Repositories . ............................. . .......... .......•. ... ... .. .. 9 1.2 State of the Practice in Analytics ................................................................. . 11 1.2.1 Bl Versus Data Science .............. .... ....... . .. . ........... . . . .... . ....................... .. .... 12 1.2.2 Current Analytical Architecture ... . .... .• . . ................ .... .............. .... .... ...... •.. . ..... 13 1.2.3 Drivers of Big Data .................................................... . . . .. ................. .. ... . . 15 1.2.4 Emerging Big Data Ecosystem and a New Approach to Analytics .. ....... ...... . ............ .. ....... 16 1.3 Key Roles for the New Big Data Ecosystem ....... ..... ......... . ....... . ..... .. .................... 19 1.4 Examples of Big Data Analytics ... .... .......... .... . ... ....... ... .... . ...... . .................... 22 Summary .............. ............ ... ... ......... .... • ... •....... ........ .. • ..•... . ................ 23 Exercises ..................... .... ..... .. ...... . ......•......... .. .. . ... .... . ..•.................... 23 Bibliography ........................... .... .. ... ... ... •................... .. • ...... ..... ..... ....... 24 Chapter 2 • Data Ana lytics Lifecycle ..................................................... . 25 2.1 Data Analytics Lifecycle Overview ... ..... . ............. • ...... •.. ..... ...... • ... •............. . . . 26 2.1.1 Key Roles for a Successful Anolytics Project .... . .. . .... .... . ........ . .. .. . ..•......... •. •....... . .. . . 26 2.1.2 Background and Overview of Data Analytics Lifecyc/e .......................... . .......•... . ..... ... 28 2.2 Phase 1: Discovery ..... .. .. .. . ............................. . ..•..................... •........... . 30 2.2.1 Learning the Business Domain .. . ....... ... ..•.•. •.... . .. ..... . . .. . ...................•........... .30 2.2.2 Resources . . ... . ................... . ...... . ......................... ..... ............. •.......•.... 31 2.2.3 Framing the Problem ............•.... . ...................................•......... •.•.... . . ...... 32 2.2.41dentifying Key Stakeholders ... .. ....................... ... . ... ......... .... . ....... •. . .......... . . 33 2.2.51nterviewing the Analytics Sponsor ...... ........ ...... .. .......... .... ... .. ... ..... .. ........... ... 33 2.2.6 Developing Initial Hypotheses ................. .. . . . .. . . . .. . . . . ... .... .. ........... . . •............ . . 35 2.2.71dentifying Po tential Data Sources . ... ...•. •.. .... . . .. . ......•. •.......... . ....... . ..... . ... . .. .. . . 35 2.3 Phase 2: Data Preparation ...........................................................•...•..•..... 36 2.3.1 Preparing the Analytic Sandbox . .............. . ...................... ... •. •.......•.......... .. .... 37 2.3.2 Performing ETLT ..................................................................•.•.......•... .. . 38 2.3.3 Learning About the Data .. ..... . .............. .. ........................•.•.......•.•........ ..... . 39 2.3.4 Data Conditioning ....... .. ....•.......... . ....................... .. . .. . . . ......•. •............. .. .40 2.3.5 Survey and Visualize . . . ... .. .... .. .. ...... . . ..... .. . .................. . . •. ...... . .•.. .. .. .. . . . ..... 41 2.3.6 Common Tools for the Data Preparation Phase . . . .... .. ..... ....... . •......... •.• .•.. .. ..... .. .. . . .42 2.4 Phase 3: Model Planning ............................•................. . ... . .. •..... .....•........ 42 2.4.1 Data Exploration and Variable Selection . . ... . . .. . ......... •... . ... . . ........ . .............. .. .. . . . .44 2.4.2 Model Selection . ... ................ . .. . . . ................ •.......•...•.......................... . .45 2.4.3 Common Tools for the Model Planning Phase . . ..........•....... . . •. ........................... . . . .45 CONTENTS 2.5 Phase 4: Model Building ...... .................. ...... •. ... ..... .... • ... •. . •. .. •.........•...•.... 46 2.5.1 Common Tools for th e Mode/Building Phase ...... .. .. . ..... .. ..... . ....... . .. . . .. . . .. . .... . . .. . .... 48 2.6 Phase 5: Communicate Re sults ......... .... ...... . ... •........ ........ ... . •..... .....•. ..... •.... 49 2.7 Phase 6: Operationalize ... ... ....... ... . .. ........ ....... ... ........... •. . •. . ... ....... .......... SO 2.8 Case Study: Global Innovation Network and Analysis (GINA) ................. •...................... 53 2.8.1 Phase 1: Discovery ................................................................................. 54 2.8.2 Phase 2: Data Preparation .... •........ . ...................................................... . .... 55 2.8.3 Phase 3: Model Planning . . . ...•.•. . . .. . . ..... .. . . .. . ..... .. .. ... ...... . . . ................... . . . .. . . 56 2.8.4 Phase 4: Mode/Building ..... . ....•.. .. .. .......... . .............. . . .. . ... . . ....... .. . .... ... . .. . . . 56 2.8.5 Phase 5: Commun icate Results .. . . ..... . ...... .. ...... ... .. . .. . . ..................... ...... ........ 58 2.8.6 Phase 6: Operationalize . . ... ......•..... ..• .. . . . .. . . ..............•................................ 59 Summary ................................. • ................. •..•.. •.......•.....••........ . ....•.... 60 Exercises .................................•.... .. ..............•. . •....................... . . . . . •.... 61 Bibliography ....• . .••...................................•.... . . • ..... .. ............................. 61 Chapter 3 • Review of Basic Data Analytic Methods Using R . . . . . . .. . ... . .. .. . ... . . . . . .. ... . 63 3.1 Introd uction toR ............................ ... .................................... ..... ......... 64 3.1.1 R Graphical User Interfaces . ............ . ............................... ...... . .. ... . . . ... ....... ... 67 3.1.2 Data Import and Export. . ......... . .. ............. ........... ........... .................... ....... 69 3.1.3 Attribute and Data Types . .......... .. ...... . ....................................................... 71 3.1.4 Descriptive Statistics ....................... . . . ..................................................... 79 3.2 Exploratory Data Analysis .............. • ... . .• •.............•........... . .................... .... 80 3.2.1 Visualization Before Analysis ........ . ..................................................•........... 82 3.2.2 Dirty Data ............ .. ................................................ . ........... ...•...... .... . 85 3.2.3 Visualizing a Single Variable ........ •.. . ................ .. .. . . ........... . .... ....... •.. . . . .... .. . . 88 3.2.4 Examining Multiple Varia bles . .... .... ....• . .. . ... .......... .............. ...... . .. .. .............. 91 3.2.5 Data Exploration Versus Presentation ...... . ........ •. . . . .. . . ..... ...... ................... ...... .. 99 3.3 Statistical Methods for Evaluation .................... . .. .• ......... ... . .. .................... . .. 101 3.3.1 Hypoth esis Testing ........ ........ .......... .... ............................ . .. . ...... .. ...... . ... 102 3.3.2 Difference of Means ...... . .... .. . .... ..... . ..................................................... 704 3.3.3 Wilcoxon Rank-Sum Test ................•........................ ... .. . ... . .................. •... 108 3.3.4 Type I and Type II Errors ... . ...... . .. . .................. . ........ . .. .... .. ......................... 109 3.3.5 Power and Sample Size .....•.. . . .. . ... ...... . ........ ....... .............. ....... .. .... .......... 110 3.3.6 ANOVA . ................ . .. ......... . . .... .. . . ... .... ........ . . .. ..... . ... .. .. .... . •. •.......•... . 110 Summary ...... ............. • ....... ...... ....• .. •... • ............................... •......•...... 114 Exercises ...... ......... ......................... . ............... ...... . ... ... ....... •............. 114 Bibliography ................................... . . . ................. .................. •.... . . .. . .... 11 5 Chapter 4 • Advanced Analytical Theory and Method s: Clu stering .. . . .. . .. . ... . .. . . . ... . .. 117 4.1 Overview of Clustering ........ ...... ......... .. ................................................. 11 8 4.2 K-means ............... ....... ... ....................... .. ........ . ... . .......... . .... . .... .... 11 8 4.2.1 Use Cases ..... .. ............. . •.....• ... ... .. ..... ........ .......... . . .. ........ ...... ... .. . ...... 119 4.2.2 Overview of the Method . ............ ....... ... . .. ........ ................... ... ... .. . .•. ..... . .. . 120 4.2.3 Determining the Number of Clusters . . . .. .. •. •...................... . .......... ..... .. ... ...... . ... 123 4.2.4 Diagnostics .. ......................... ...•.... ........... ..... ....................... .. .. ....... . 128 CONTENTS 4.2.5 Reasons to Choose and Cautions .. . .. . . . . . . .. . . . . . .. ... . ..... ... .. .. . . • . •. • . . ...•. • .• . ... . ..... ... 730 4.3 Add itional Algorithms .............. ... . . . . .. . ...... . ... . ........ .• .. .. . .. ................ .. .... 134 Summary ......... .... ........................ .. . ....................... . . . ..•.. . .................. 135 Exercises ........... ..................... . . ..... . ............................... . .......... .. ..... . 135 Bibliography ............................. ....... ................................ . .................. 136 Chapter 5 • Advanced Analytica l Theory and Methods: Association Ru les .................. 137 5.1 Overview .... . . ... ........................................ .. . .. . ..... . .. .................. .. .... 138 5.2 A priori Algorit hm ........... . ............... . . . ...... ... . . .... . . ..... .......... .. ......... ... ... 140 5.3 Evaluation of Candidate Rules ....................... . ... .. . .. ..... • ....... . ................ ..... 141 5.4 Applications of Association Rules ............ ... ..... . ..... . . . ... ..... . . .. . . . ...... .............. 143 5.5 An Example: Transactions in a Grocery Store ... . .................... .... . . ... .......... ........... 143 5.5.1 The Groceries Dataset ................... . . .. .............. •........... •... . .......•............... 144 5.5.2 Frequent ltemset Generation . . ........................... .. ......... . . • . •......... •............... 146 5.5.3 Rule Generation and Visualization ...... . ... . ......................... . .•. •.... . •. •........... . .. . 752 5.6 Validation and Testing ........... . ... .... . . ............................................. . ....... 157 5.7 Diagnostics .. .... ..................... . .. . . ..... . ............ . ... . . ... . ...... . ......... .. .... . . . 158 Summa ry ....... .. ................ . ..... ... . . .. . . ...... .... .... . ........ . . .... ..... .............. . . 158 Exercises ................................ ... . . . ........ . ................. . .... ....... ......... . .... 159 Bibliog raphy ................................ . .. .... ..... ............ ..... . ... ........... ... . ...... . 160 Chapter 6 • Advanced Analytical Theory and Methods: Regression .................. . ..... 161 6.1 Li near Regression .......... . .......... . .. . .. .. ...... . ............ .... . . . ....... ........... ...... 162 6.1.1 UseCases . . . ... . . . .. . ...... ..... ......................... .. . ....... .... .... .. ...... . .......... . .. . /62 6.1.2 Model Description .. ... .. . .. . ..... . ........... . .. . .. .... . . •. ..... . •. •.• . ...... . .•............. . .. . 163 6.1.3 Diagnostics ....................... . .... .. . . . . . . ....... •.•.• .....•. •.•...... .• . • .•.. . .. . .... . . . . . . . 773 6.2 Logistic Regression ............ ........ . ..... ................................ . ......... .. .. . .. .. 178 6.2.1 Use Cases ...... . ....................................... .... ................ .... ................... 179 6.2.2 Model Description ........ .. .... ... •..... . .... ........ .. .. • . ..... ... . .•. •...• .•................... 179 6.2.3 Diagnostics ................. ..... ...... . . .. ............•. •. ........•. ..... .• .•................... 181 6.3 Reasons to Choose and Cautions ....... . . .... .. .... ............ ........... ......... ....... ..... . 188 6.4 Additional Regression Models ............ ... .. ...... . ... . ............. . ... ........ ........... ... 189 Summary ........... .... . . ........... . ....... . .........•... . ...... . ...... ... . .. . . ... .. ........... . . 190 Exercises ............ .. .......... .. . .. ................ .. .. .. ............ . . .. .......... . . . .. .. .... . . 190 Chapter 7 • Advanced Ana lytical Theory and Methods: Classification ...... . .......... . .... 191 7.1 Decision Trees ... .. ............... ...... ............ ............. .......... .............. ... .... 192 7.1.1 Overview of a Decision Tree ...... . .................... .. . ........................ .. .... ..... . ...... 193 7.1.2 The General Algorithm . .............. .............. ... ..•. ... .............. .• .. .. ........ .... . .. . . 197 7.1.3 Decision Tree Algorithms ............. .. . .... .. ......•. . .•.. ... • . •... .... . .... ... . .............. .. 203 7.1.4 Evaluating a Decision Tree ............. . . •... . ... . ...•... .... . ....... . .................... . ... . . . . 204 7.1.5 Decision Trees in R . . . .. ................ ...... .. .. ..... ..... .... .................. . ..... ........ .. 206 7.2 Nalve Bayes . .... ... ................ . ..... . ...... . .......... . .. . ... . ..... .. ..... ......... . ...... 211 7.2.1 Bayes Theorem . . .. . ........................ . ..................................................... 212 7.2.2 Naive Bayes Classifier ................... •... . ... ..... .......•.................................. .. . 214 CONTENTS 7.2.3 Smoothing . ............... .................... . .. . ........ . .. . ...... .. • . .. .......... .. .......... . 277 7.2.4 Diagnostics .. . ........... . ..................... .. .... . .•......... •.•.....•...•........ . . . ......... 217 7.2.5 Naive Bayes in R ............... . . .. . .....•... .. . ...•.•.........•.•.. .. . .. •. •.•.... ........ . .. .... . 278 7.3 Diagnostics of Classifiers ............ •...... ........... •.......... ...•...• .. •... •. .... ........... 224 7.4 Additiona l Classification Methods .... • ... • ...... • ............. • .................•... .... ......... 228 Summary ................. ..... ............ • ......•.............. .. ..........................•..... 229 Exercises .................. ... ......... .... .........................•.... . . . .................•..... 230 Bibliography ...... . ..........•......... .... ........... . ... . .............. ... ...•................... 231 Chapter 8 • Advanced Analytical Theory and Methods: Time Series Analysis . . .. ... . ... . .. . 233 8.1 Overview of Time Series Analysis ....... ....... ................ ......................... .... ..... 234 8.1.1 Box-Jenkins Methodology ................... . .. .... ...... . .................... . .. ..... ............ 235 8.2 ARIMA Model. ................ . .. . ....... •..•..... .. ...... . ... •................. • ... . ..•........ 236 8.2.1 Autocorrelation Function (A CF) .. ......... ...................... ... ........ . ......... . .. ..... ..... 236 8.2.2 Autoregressive Models . ...... ... ............ . . . .. •. ... ..... ... . .. ... ... . ......... . ....... .. . . .... 238 8.2.3 Moving Average Models . .. .. . .................................... .................... •..... . .... . 239 8.2.4 ARMA and ARIMA Models ............. . .................................•...........•.....•....... 241 8.2.5 Building and Evaluating an ARIMA Model ............................. . .•.........•. •. . ... •...... 244 8.2.6 Reasons to Choose and Cautions .. ................ . .. . ........ .. . . .. . ....... . .... .•.•. •.. . •. . .... . 252 8.3 Additional Methods ........ ... . ... ....... ... .. ...... ...... .. ....... ....... .. ... . .... . ... . ...... . 253 Summary ........................ ... ... ...... .. ............ • ......... ......... ..• .. .......• ... ..... 254 Exercises .............. . .......... ... ......... . •. .. .............................• .. . . .. • . .• ... ..... 254 Chapter 9 • Advanced Analytical Theory and Methods: Text Analysis ...... . ... . .. .. .. . . ... 255 9.1 Text Analysis Steps .......... . .... ......... ...... ... .................... . ...... . ...... . . .•....... 257 9.2 A Text Analysis Example ..... •.... .... ............................ .. ............ …