Higher Education

eBook for Essentials of Business Analytics

Author(s): Jeffrey D. Camm | James J Cochran | Michael J. Fry | Jeffrey W. Ohlmann | David R. Anderson

ISBN: 9781337019019

Edition: 2nd

© Year : 2017

₹799

Binding: eBook

Imprint : South Western

Pages:

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ESSENTIALS OF BUSINESS ANALYTICS, 2e provides coverage over the full range of analytics--descriptive, predictive, and prescriptive--not covered by any other single book. It includes step-by-step instructions to help students learn how to use Excel and powerful but easy to use Excel add-ons such as XL Miner for data mining. Extensive solutions to problems help instructors master material and grade student assignments.

*Special prices for countries of South-Asia

  • DATA files and MODEL files: All data sets used as examples and in student exercises are also provided online as files available for download by the student. DATAfiles are Excel files that contain data needed for the examples and problems given in the textbook. MODELfiles contain additional modeling features such as extensive use of Excel formulas or the use of Excel Solver or Analytic Solver Platform.
  • Excel is completely integrated throughout the book, so students learn the latest methods for solving practical problems. It includes step-by-step instructions to help students learn how to use Excel 2016 to apply material in the book. It also includes by-hand calculation approaches to convey insights when this is appropriate.
  • First Mindtap for Business Analytics. MindTap is a customizable digital course solution that includes an interactive eBook, autograded exercises from the textbook, and author-created video walkthroughs of key chapter concepts and select examples that use Analytic Solver platform. Students can complete assignments whenever and wherever they are ready to learn with course material specially customized for students by you streamlined in one proven, easy-to-use interface. MindTap gives students a roadmap to master decision-making in business analytics. With an array of resources, tools, and apps -- including videos, practice opportunities, note taking, and flashcards.

 

Chapter 1 Introduction

1.1 Decision Making

1.2 Business Analytics Defined

1.3 A Categorization of Analytical Methods and Models

1.4 Big Data

1.5 Business Analytics in Practice

Summary

Glossary

 

Chapter 2 Descriptive Statistics

2.1 Overview of Using Data: Definitions and Goals

2.2 Types of Data

2.3 Modifying Data in Excel

2.4 Creating Distributions from Data

2.5 Measures of Location

2.6 Measures of Variability

2.7 Analyzing Distributions

2.8 Measures of Association Between Two Variables

Summary

Glossary

Problems

Case Problem: Heavenly Chocolates Web Site Transactions

Appendix 2.1 Creating Box Plots with XLMiner

 

Chapter 3 Data Visualization

3.1 Overview of Data Visualization

3.2 Tables

3.3 C harts

3.4 Advanced Data Visualization

3.5 Data Dashboards

Summary

Glossary

Problems

Case Problem: All-Time Movie Box-Office Data

Appendix 3.1 Creating a Scatter-Chart Matrix and a Parallel-Coordinates Plot with XLMiner

 

Chapter 4 Descriptive Data Mining

4.1 Data Preparation

4.2 Cluster Analysis

4.3 Association Rules

Summary

Glossary

Problems

Case Problem: Know Thy Customer

Appendix 4.1 Hierarchical Clustering with XLMiner

Appendix 4.2 k-Means Clustering with XLMiner

Appendix 4.3 Association Rules with XLMiner

 

Chapter 5 Probability: An Introduction to Modeling Uncertainty

5.1 Events and Probabilities

5.2 Some Basic Relationships of Probability

5.3 Conditional Probability

5.4 Random Variables

5.5 Discrete Probability Distributions

5.6 Continuous Probability Distributions

Summary

Glossary

Problems

Case Problem: Hamilton County Judges

 

Chapter 6 Statistical Inference

6.1 Selecting a Sample

6.2 Point Estimation

6.3 Sampling Distributions

6.4 Interval Estimation

6.5 Hypothesis Tests

Summary

Glossary

Problems

Case Problem 1: Young Professional Magazine

Case Problem 2: Quality Associates, Inc.

 

Chapter 7 Linear Regression

7.1 Simple Linear Regression Model

7.2 Least Squares Method

7.3 Assessing the Fit of the Simple Linear Regression Model

7.4 The Multiple Regression Model

7.5 Inference and Regression

7.6 Categorical Independent Variables

7.7 Modeling Nonlinear Relationships

7.8 Model Fitting

Summary

Glossary

Problems

Case Problem: Alumni Giving

Appendix 7.1 Regression with XLMiner

 

Chapter 8 Time Series Analysis and Forecasting

8.1 Time Series Patterns

8.2 Forecast Accuracy

8.3 Moving Averages and Exponential Smoothing

8.4 Using Regression Analysis for Forecasting

8.5 Determining the Best Forecasting Model to Use

Summary

Glossary

Problems

Case Problem: Forecasting Food and Beverage Sales

Appendix 8.1 Using Excel Forecast Sheet

Appendix 8.2 Forecasting with XLMiner

 

Chapter 9 Predictive Data Mining

9.1 Data Sampling

9.2 Data Partitioning

9.3 Accuracy Measures

9.4 Logistic Regression

9.5 k-Nearest Neighbors

9.6 Classification and Regression Trees

Summary

Glossary

Problems

Case Problem: Grey Code Corporation

Appendix 9.1 Data Partitioning with XLMiner

Appendix 9.2 Logistic Regression Classification with XLMiner

Appendix 9.3 k-Nearest Neighbor Classification and Estimation with XLMiner

Appendix 9.4 Single Classification and Regression Trees with XLMiner

Appendix 9.5 Random Forests of Classification or Regression Trees with XLMiner

 

Chapter 10 Spreadsheet Models

10.1 Building Good Spreadsheet Models

10.2 What-If Analysis

10.3 Some Useful Excel Functions for Modeling

10.4 Auditing Spreadsheet Models

Summary

Glossary

Problems

Case Problem: Retirement Plan

 

Chapter 11 Linear Optimization Models

11.1 A Simple Maximization Problem

11.2 Solving the Par, Inc. Problem

11.3 A Simple Minimization Problem

11.4 Special Cases of Linear Program Outcomes

11.5 Sensitivity Analysis

11.6 General Linear Programming Notation and More Examples

11.7 Generating an Alternative Optimal Solution for a Linear Program

Summary

Glossary

Problems

Case Problem: Investment Strategy

Appendix 11.1 Solving Linear Optimization Models Using Analytic Solver Platform

 

Chapter 12 Integer Linear Optimization Models

12.1 Types of Integer Linear Optimization Models

12.2 Eastborne Realty, An Example of Integer Optimization

12.3 Solving Integer Optimization Problems with Excel Solver

12.4 Applications Involving Binary Variables

12.5 Modeling Flexibility Provided by Binary Variables

12.6 Generating Alternatives in Binary Optimization

Summary

Glossary

Problems

Case Problem: Applecore Children’s Clothing

Appendix 12.1 Solving Integer Linear Optimization Problems Using Analytic Solver Platform

 

Chapter 13 Nonlinear Optimization Models

13.1 A Production Application: Par, Inc. Revisited

13.2 Local and Global Optima

13.3 A Location Problem

13.4 Markowitz Portfolio Model

13.5 Forecasting Adoption of a New Product

Summary

Glossary

Problems

Case Problem: Portfolio Optimization with Transaction Costs

Appendix 13.1 Solving Nonlinear Optimization Problems with Analytic Solver Platform

 

Chapter 14 Monte Carlo Simulation

14.1 Risk Analysis for Sanotronics LLC 

14.2 Simulation Modeling for Land Shark Inc.

14.3 Simulation Considerations

Summary

Glossary

Problems

Case Problem: Four Corners

Appendix 14.1 Land Shark Inc. Simulation with Analytic Solver Platform

Appendix 14.2 Distribution Fitting with Analytic Solver Platform

Appendix 14.3 Simulation Optimization with Analytic Solver Platform

Appendix 14.4 Correlating Random Variables with Analytic Solver Platform

Appendix 14.5 Probability Distributions for Random Variables

 

Chapter 15 Decision Analysis

15.1 Problem Formulation

15.2 Decision Analysis Without Probabilities

15.3 Decision Analysis with Probabilities

15.4 Decision Analysis with Sample Information

15.5 Computing Branch Probabilities with Bayes’ Theorem

15.6 Utility Theory

Summary

Glossary

Problems

Case Problem: Property Purchase Strategy

Appendix 15.1 Using Analytic Solver Platform to Create Decision Trees

Appendix A Basics of Excel

Appendix B Database Basics with Microsoft Access

Appendix C Solutions to Even-Numbered Questions (Online)

References

Index

 

Jeffrey D. Camm, Wake Forest University

Dr. Jeffrey D. Camm is the Inmar Presidential Chair and Associate Dean of Business Analytics in the School of Business at Wake Forest University. Born in Cincinnati, Ohio, he holds a B.S. from Xavier University (Ohio) and a Ph.D. from Clemson University. Prior to joining the faculty at Wake Forest, he served on the faculty of the University of Cincinnati. He has also served as a visiting scholar at Stanford University and as a visiting Professor of Business Administration at the Tuck School of Business at Dartmouth College. Dr. Camm has published more than 40 papers in the general area of optimization applied to problems in operations management and marketing. He has published his research in numerous professional journals, including Science, Management Science, Operations Research and Interfaces. Dr. Camm was named the Dornoff Fellow of Teaching Excellence at the University of Cincinnati and he was the 2006 recipient of the INFORMS Prize for the Teaching of Operations Research Practice. A firm believer in practicing what he preaches, he has served as an operations research consultant to numerous companies and government agencies. From 2005 to 2010 he served as editor-in-chief of Interfaces. In 2016, Dr. Camm received the George E. Kimball Medal for service to the operations research profession and in 2017 he was named an INFORMS Fellow.

 

James J. Cochran, University of Alabama

James J. Cochran is Associate Dean for Research, Professor of Applied Statistics and the Rogers-Spivey Faculty Fellow at The University of Alabama. Born in Dayton, Ohio, he earned his B.S., M.S., and M.B.A. from Wright State University and his Ph.D. from the University of Cincinnati. He has been at The University of Alabama since 2014 and has been a visiting scholar at Stanford University,

Universidad de Talca, the University of South Africa and Pole Universitaire Leonard de Vinci. Dr. Cochran has published more than 40 papers in the development and application of operations research and statistical methods. He has published in several journals, including Management Science, The American Statistician, Communications in Statistics—Theory and Methods, Annals of Operations Research, European Journal of Operational Research, Journal of Combinatorial Optimization, Interfaces and Statistics and Probability Letters. He received the 2008 INFORMS Prize for the Teaching of Operations Research Practice, 2010 Mu Sigma Rho Statistical Education Award and 2016 Waller Distinguished Teaching Career Award from the American Statistical Association. Dr. Cochran was elected to the International Statistics Institute in 2005, was named a Fellow of the American Statistical Association in 2011 and was named a Fellow of INFORMS in 2017. He received the Founders Award in 2014, the Karl E. Peace Award in 2015 from the American Statistical Association and the INFORMS President’s Award in 2019. A strong advocate for effective operations research and statistics education as a means of improving the quality of applications to real problems, Dr. Cochran has chaired teaching effectiveness workshops around the globe. He has served as operations research consultant to numerous companies and not-for-profit organizations.

 

Michael J. Fry, University of Cincinnati

Michael J. Fry is Professor of Operations, Business Analytics, and Information Systems (OBAIS) and Academic Director of the Center for Business Analytics in the Carl H. Lindner College of Business at the University of Cincinnati. Born in Killeen, Texas, he earned a B.S. from Texas A&M University, and M.S.E. and Ph.D. degrees from the University of Michigan. He has been at the University of Cincinnati since 2002, where he was previously department chair and has been named a Lindner Research Fellow. He has also been a visiting professor at the Samuel Curtis Johnson Graduate School of Management at

Cornell University and the Sauder School of Business at the University of British Columbia. Dr. Fry has published more than 25 research papers in journals such as Operations Research, M&SOM, Transportation Science, Naval Research Logistics, IIE Transactions, Critical Care Medicine and Interfaces. His research interests focus on applying analytics to the areas of supply chain management, sports and public-policy operations. He has worked with many different organizations for his research, including Dell, Inc., Starbucks Coffee Company, Great American Insurance Group, the Cincinnati Fire Department, the State of Ohio Election Commission, the Cincinnati Bengals and the Cincinnati Zoo & Botanical Garden. He was named a finalist for the Daniel H. Wagner Prize for Excellence in Operations Research Practice, and he has been recognized for both his research and teaching excellence at the University of Cincinnati. In 2019 he led the team that was awarded the INFORMS UPS George D. Smith Prize on behalf of the OBAIS Department at the University of Cincinnati.

 

Jeffrey W. Ohlmann, University of Iowa

Jeffrey W. Ohlmann is Associate Professor of Business Analytics and Huneke Research Fellow in the Tippie College of Business at the University of Iowa. Born in Valentine, Nebraska, he earned a B.S. from the University of Nebraska, and M.S. and Ph.D. degrees from the University of Michigan. He has taught at the University of Iowa since 2003. Dr. Ohlmann’s research on the modeling and solution of decision-making problems has produced more than two dozen research papers in journals, such as Operations Research, Mathematics of Operations Research, INFORMS Journal on Computing, Transportation Science and European Journal of Operational Research. He has collaborated with companies such as Transfreight, LeanCor, Cargill and the Hamilton County Board of Elections as well as three National Football League franchises. Because of the relevance of his work to the industry, he was bestowed the George B. Dantzig Dissertation Award and was recognized as a finalist for the Daniel H. Wagner Prize for Excellence in Operations Research Practice.

 

David R. Anderson, University of Cincinnati

Dr. David R. Anderson is a leading author and Professor Emeritus of Quantitative Analysis in the College of Business Administration at the University of Cincinnati. He has served as head of the Department of Quantitative Analysis and Operations Management and as Associate Dean of the College of Business Administration. He was also coordinator of the college’s first Executive Program. In addition to introductory statistics for business students, Dr. Anderson has taught graduate-level courses in regression analysis, multivariate analysis, and management science. He also has taught statistical courses at the Department of Labor in Washington, D.C. Dr. Anderson has received numerous honors for excellence in teaching and service to student organizations. He is the co-author of ten well-respected textbooks related to decision sciences and actively consults with businesses in the areas of sampling and statistical methods. Born in Grand Forks, North Dakota, he earned his B.S., M.S., and Ph.D. degrees from Purdue University.

 

Dennis J. Sweeney, University of Cincinnati

Dennis J. Sweeney is Professor Emeritus of Quantitative Analysis and founder of the Center for Productivity Improvement at the University of Cincinnati. Born in Des Moines, Iowa, he earned a BSBA degree from Drake University and his MBA and DBA degrees from Indiana University, where he was an NDEA Fellow. Professor Sweeney has worked in the management science group at Procter & Gamble and has been a visiting professor at Duke University. Professor Sweeney served as Head of the Department of Quantitative Analysis and four years as Associate Dean of the College of Business Administration at the University of Cincinnati. Professor Sweeney has published more than 30 articles and monographs in the area of management science and statistics. The National Science Foundation, IBM, Procter & Gamble, Federated Department Stores, Kroger and Cincinnati Gas & Electric have funded his research, which has been published in Management Science, Operations Research, Mathematical Programming, Decision Sciences and other journals. Professor Sweeney has co-authored ten textbooks in the areas of statistics, management science, linear programming and production and operations management.

 

Thomas A. Williams, Rochester Institute of Technology