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Essentials of Business Analytics International Edition [Multiple-component retail product, part(s) enclosed]

(Uuem väljaanne: 9781305627734)
(Rochester Institute of Technology), (University of Iowa), (University of Cincinnati), (University of Cincinnati), (University of Alabama), (Wake Forest University), (University of Cincinnati)
  • Formaat: Multiple-component retail product, part(s) enclosed, 696 pages, kõrgus x laius x paksus: 261x210x31 mm, kaal: 1410 g, Contains 1 Hardback and 1 Digital online
  • Ilmumisaeg: 01-Jan-2014
  • Kirjastus: South-Western College Publishing
  • ISBN-10: 128518727X
  • ISBN-13: 9781285187273 (Uuem väljaanne: 9781305627734)
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  • Formaat: Multiple-component retail product, part(s) enclosed, 696 pages, kõrgus x laius x paksus: 261x210x31 mm, kaal: 1410 g, Contains 1 Hardback and 1 Digital online
  • Ilmumisaeg: 01-Jan-2014
  • Kirjastus: South-Western College Publishing
  • ISBN-10: 128518727X
  • ISBN-13: 9781285187273 (Uuem väljaanne: 9781305627734)
Teised raamatud teemal:
This book provides coverage over the full range of analytics--descriptive, predictive, 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 and Analytic Solver Platform for optimization and simulation.
About the Authors xiv
Preface xvii
Chapter 1 Introduction
1(14)
1.1 Decision Making
4(1)
1.2 Business Analytics Defined
5(1)
1.3 A Categorization of Analytical Methods and Models
5(3)
Descriptive Analytics
5(1)
Predictive Analytics
6(1)
Prescriptive Analytics
6(1)
Analytics in Action: Procter & Gamble Uses Business Analytics to Redesign its Supply Chain
7(1)
1.4 Big Data
8(1)
1.5 Business Analytics in Practice
9(6)
Financial Analytics
9(1)
Human Resource (HR) Analytics
10(1)
Marketing Analytics
10(1)
Health Care Analytics
10(1)
Supply Chain Analytics
11(1)
Analytics for Government and Nonprofits
11(1)
Sports Analytics
12(1)
Web Analytics
12(1)
Summary
13(1)
Glossary
13(2)
Chapter 2 Descriptive Statistics
15(55)
Analytics in Action: U.S. Census Bureau
16(1)
2.1 Overview of Using Data: Definitions and Goals
16(1)
2.2 Types of Data
17(4)
Population and Sample Data
17(1)
Quantitative and Categorical Data
18(1)
Cross-Sectional and Time Series Data
18(1)
Sources of Data
18(3)
2.3 Modifying Data in Excel
21(4)
Sorting and Filtering Data in Excel
21(2)
Conditional Formatting of Data in Excel
23(2)
2.4 Creating Distributions from Data
25(10)
Frequency Distributions for Categorical Data
25(2)
Relative Frequency and Percent Frequency Distributions
27(1)
Frequency Distributions for Quantitative Data
28(3)
Histograms
31(3)
Cumulative Distributions
34(1)
2.5 Measures of Location
35(5)
Mean (Arithmetic Mean)
35(1)
Median
36(1)
Mode
37(1)
Geometric Mean
38(2)
2.6 Measures of Variability
40(4)
Range
41(1)
Variance
41(2)
Standard Deviation
43(1)
Coefficient of Variation
44(1)
2.7 Analyzing Distributions
44(7)
Percentiles
44(1)
Quartiles
45(1)
z-scores
46(2)
Empirical Rule
48(1)
Identifying Outliers
48(1)
Box Plots
49(2)
2.8 Measures of Association Between Two Variables
51(19)
Scatter Charts
51(1)
Covariance
52(3)
Correlation Coefficient
55(2)
Summary
57(1)
Glossary
57(1)
Problems
58(8)
Case: Heavenly Chocolates Web Site Transactions
66(1)
Appendix: Creating Box Plots in XLMiner
67(3)
Chapter 3 Data Visualization
70(53)
Analytics in Action: Cincinnati Zoo & Botanical Garden
71(2)
3.1 Overview of Data Visualization
73(2)
Effective Design Techniques
73(2)
3.2 Tables
75(10)
Table Design Principles
77(2)
Crosstabulation
79(1)
PivotTables in Excel
80(5)
3.3 Charts
85(17)
Scatter Charts
85(2)
Line Charts
87(3)
Bar Charts and Column Charts
90(3)
A Note on Pie Charts and 3-D Charts
93(1)
Bubble Charts
93(2)
Heat Maps
95(2)
Additional Charts for Multiple Variables
97(4)
PivotCharts in Excel
101(1)
3.4 Advanced Data Visualization
102(3)
Advanced Charts
103(1)
Geographic Information Systems Charts
104(1)
3.5 Data Dashboards
105(18)
Principles of Effective Data Dashboards
106(1)
Applications of Data Dashboards
106(2)
Summary
108(1)
Glossary
109(1)
Problems
110(8)
Case Problem: All-Time Movie Box Office Data
118(1)
Appendix: Creating a Scatter Chart Matrix and a Parallel Coordinates Plot with XLMiner
119(4)
Chapter 4 Linear Regression
123(79)
Analytics in Action: Alliance Data Systems
124(1)
4.1 The Simple Linear Regression Model
125(2)
Regression Model and Regression Equation
125(1)
Estimated Regression Equation
126(1)
4.2 Least Squares Method
127(6)
Least Squares Estimates of the Regression Parameters
129(3)
Using Excel's Chart Tools to Compute the Estimated Regression Equation
132(1)
4.3 Assessing the Fit of the Simple Linear Regression Model
133(5)
The Sums of Squares
134(2)
The Coefficient of Determination
136(1)
Using Excel's Chart Tools to Compute the Coefficient of Determination
137(1)
4.4 The Multiple Regression Model
138(5)
Regression Model and Regression Equation
138(1)
Estimated Multiple Regression Equation
138(1)
Least Squares Method and Multiple Regression
139(1)
Butler Trucking Company and Multiple Regression
140(1)
Using Excel's Regression Tool to Develop the Estimated Multiple Regression Equation
140(3)
4.5 Inference and Regression
143(18)
Conditions Necessary for Valid Inference in the Least Squares Regression Model
144(4)
Testing for an Overall Regression Relationship
148(2)
Testing Individual Regression Parameters
150(3)
Addressing Nonsignificant Independent Variables
153(1)
Multicollinearity
154(2)
Inference and Very Large Samples
156(5)
4.6 Categorical Independent Variables
161(4)
Butler Trucking Company and Rush Hour
161(1)
Interpreting the Parameters
162(2)
More Complex Categorical Variables
164(1)
4.7 Modeling Nonlinear Relationships
165(12)
Quadratic Regression Models
167(3)
Piecewise Linear Regression Models
170(3)
Interaction Between Independent Variables
173(4)
4.8 Model Fitting
177(25)
Variable Selection Procedures
177(2)
Overfitting
179(1)
Summary
180(1)
Glossary
180(2)
Problems
182(15)
Case Problem: Alumni Giving
197(1)
Appendix: Using XLMiner for Regression
198(4)
Chapter 5 Time Series Analysis and Forecasting
202(49)
Analytics in Action: Forecasting Demand for a Broad Line of Office Products
203(2)
5.1 Time Series Patterns
205(7)
Horizontal Pattern
205(2)
Trend Pattern
207(2)
Seasonal Pattern
209(1)
Trend and Seasonal Pattern
209(2)
Cyclical Pattern
211(1)
Identifying Time Series Patterns
212(1)
5.2 Forecast Accuracy
212(5)
5.3 Moving Averages and Exponential Smoothing
217(9)
Moving Averages
217(4)
Forecast Accuracy
221(1)
Exponential Smoothing
221(3)
Forecast Accuracy
224(2)
5.4 Using Regression Analysis for Forecasting
226(10)
Linear Trend Projection
226(2)
Seasonality
228(1)
Seasonality Without Trend
228(2)
Seasonality with Trend
230(1)
Using Regression Analysis as a Causal Forecasting Method
231(4)
Combining Causal Variables with Trend and Seasonality Effects
235(1)
Considerations in Using Regression in Forecasting
235(1)
5.5 Determining the Best Forecasting Model to Use
236(15)
Summary
237(1)
Glossary
237(1)
Problems
238(8)
Case Problem: Forecasting Food and Beverage Sales
246(1)
Appendix: Using XLMiner for Forecasting
247(4)
Chapter 6 Data Mining
251(69)
Analytics in Action: Online Retailers Using Predictive Analytics to Cater to Customers
252(1)
6.1 Data Sampling
253(1)
6.2 Data Preparation
254(1)
Treatment of Missing Data
254(1)
Identification of Outliers and Erroneous Data
254(1)
Variable Representation
254(1)
6.3 Unsupervised Learning
255(14)
Cluster Analysis
256(9)
Association Rules
265(4)
6.4 Supervised Learning
269(51)
Partitioning Data
269(4)
Classification Accuracy
273(4)
Prediction Accuracy
277(1)
k-Nearest Neighbors
277(6)
Classification and Regression Trees
283(16)
Logistic Regression
299(9)
Summary
308(1)
Glossary
309(2)
Problems
311(8)
Case Problem: Grey Code Corporation
319(1)
Chapter 7 Spreadsheet Models
320(32)
Analytics in Action: Procter and Gamble Sets Inventory Targets Using Spreadsheet Models
321(1)
7.1 Building Good Spreadsheet Models
322(5)
Influence Diagrams
322(1)
Building a Mathematical Model
322(2)
Spreadsheet Design and Implementing the Model in a Spreadsheet
324(3)
7.2 What-If Analysis
327(5)
Data Tables
327(4)
Goal Seek
331(1)
7.3 Some Useful Excel Functions for Modeling
332(7)
Sum and Sumproduct
332(1)
If and Countif
333(4)
Vlookup
337(2)
7.4 Auditing Spreadsheet Models
339(13)
Trace Precedents and Dependents
339(1)
Show Formulas
340(1)
Evaluate Formulas
340(1)
Error Checking
341(1)
Watch Window
342(1)
Summary
343(1)
Glossary
343(1)
Problems
344(6)
Case Problem: Retirement Plan
350(2)
Chapter 8 Linear Optimization Models
352(53)
Analytics in Action: Timber Harvesting Model at MeadWestvaco Corporation
353(1)
8.1 A Simple Maximization Problem
354(4)
Problem Formulation
355(2)
Mathematical Model for the Par, Inc. Problem
357(1)
8.2 Solving the Par, Inc. Problem
358(6)
The Geometry of the Par, Inc. Problem
358(2)
Solving Linear Programs with Excel Solver
360(4)
8.3 A Simple Minimization Problem
364(3)
Problem Formulation
364(1)
Solution for the M&D Chemicals Problem
365(2)
8.4 Special Cases of Linear Program Outcomes
367(5)
Alternative Optimal Solutions
367(1)
Infeasibility
368(2)
Unbounded
370(2)
8.5 Sensitivity Analysis
372(2)
Interpreting Excel Solver Sensitivity Report
372(2)
8.6 General Linear Programming Notation and More Examples
374(12)
Investment Portfolio Selection
375(3)
Transportation Planning
378(3)
Advertising Campaign Planning
381(5)
8.7 Generating an Alternative Optimal Solution for a Linear Program
386(19)
Summary
388(1)
Glossary
389(1)
Problems
390(8)
Case Problem: Investment Strategy
398(1)
Appendix: Solving Linear Optimization Models Using Analytic Solver Platform
399(6)
Chapter 9 Integer Linear Optimization Models
405(43)
Analytics in Action: Optimizing the Transport of Oil Rig Crews
406(1)
9.1 Types of Integer Linear Optimization Models
406(1)
9.2 Eastborne Realty, An Example of Integer Optimization
407(3)
The Geometry of Linear All-Integer Optimization
408(2)
9.3 Solving Integer Optimization Problems with Excel Solver
410(5)
A Cautionary Note About Sensitivity Analysis
414(1)
9.4 Applications Involving Binary Variables
415(11)
Capital Budgeting
415(1)
Fixed Cost
416(4)
Bank Location
420(4)
Product Design and Market Share Optimization
424(2)
9.5 Modeling Flexibility Provided by Binary Variables
426(2)
Multiple-Choice and Mutually Exclusive Constraints
427(1)
k out of n Alternatives Constraint
427(1)
Conditional and Corequisite Constraints
427(1)
9.6 Generating Alternatives in Binary Optimization
428(20)
Summary
430(1)
Glossary
430(1)
Problems
431(10)
Case Problem: Applecore Children's Clothing
441(1)
Appendix: Solving Integer Linear Optimization Problems Using Analytic Solver Platform
442(6)
Chapter 10 Nonlinear Optimization Models
448(37)
Analytics in Action: Intercontinental Hotels Optimizes Retail Pricing
449(1)
10.1 A Production Application: Par, Inc. Revisited
449(6)
An Unconstrained Problem
450(1)
A Constrained Problem
450(3)
Solving Nonlinear Optimization Models Using Excel Solver
453(1)
Sensitivity Analysis and Shadow Prices in Nonlinear Models
454(1)
10.2 Local and Global Optima
455(4)
Overcoming Local Optima with Excel Solver
457(2)
10.3 A Location Problem
459(2)
10.4 Markowitz Portfolio Model
461(4)
10.5 Forecasting Adoption of a New Product
465(20)
Summary
469(1)
Glossary
470(1)
Problems
470(7)
Case Problem: Portfolio Optimization with Transaction Costs
477(3)
Appendix: Solving Nonlinear Optimization Problems with Analytic Solver Platform
480(5)
Chapter 11 Monte Carlo Simulation
485(65)
Analytics in Action: Reducing Patient Infections in the ICU
486(1)
11.1 What-If Analysis
487(1)
The Sanotronics Problem
487(1)
Base-Case Scenario
487(1)
Worst-Case Scenario
488(1)
Best-Case Scenario
488(1)
11.2 Simulation Modeling with Native Excel Functions
488(10)
Use of Probability Distributions to Represent Random Variables
489(2)
Generating Values for Random Variables with Excel
491(4)
Executing Simulation Trials with Excel
495(1)
Measuring and Analyzing Simulation Output
495(3)
11.3 Simulation Modeling with Analytic Solver Platform
498(20)
The Land Shark Problem
499(1)
Spreadsheet Model for Land Shark
499(1)
Generating Values for Land Shark's Random Variables
500(3)
Tracking Output Measures for Land Shark
503(1)
Executing Simulation Trials and Analyzing Output for Land Shark
504(2)
The Zappos Problem
506(1)
Spreadsheet Model for Zappos
507(3)
Modeling Random Variables for Zappos
510(5)
Tracking Output Measures for Zappos
515(2)
Executing Simulation Trials and Analyzing Output for Zappos
517(1)
11.4 Simulation Optimization
518(6)
11.5 Simulation Considerations
524(26)
Verification and Validation
524(1)
Advantages and Disadvantages of Using Simulation
524(1)
Summary
525(1)
Glossary
526(1)
Problems
527(9)
Case Problem: Four Corners
536(1)
Appendix 11.1 Incorporating Dependence Between Random Variables
537(8)
Appendix 11.2 Probability Distributions for Random Variables
545(5)
Chapter 12 Decision Analysis
550(59)
Analytics in Action: Phytopharm's New Product Research and Development
551(1)
12.1 Problem Formulation
552(2)
Payoff Tables
553(1)
Decision Trees
553(1)
12.2 Decision Analysis Without Probabilities
554(3)
Optimistic Approach
554(1)
Conservative Approach
555(1)
Minimax Regret Approach
555(2)
12.3 Decision Analysis with Probabilities
557(4)
Expected Value Approach
557(2)
Risk Analysis
559(1)
Sensitivity Analysis
560(1)
12.4 Decision Analysis with Sample Information
561(7)
Expected Value of Sample Information
566(1)
Expected Value of Perfect Information
567(1)
12.5 Computing Branch Probabilities with Bayes' Theorem
568(3)
12.6 Utility Theory
571(38)
Utility and Decision Analysis
573(4)
Utility Functions
577(3)
Exponential Utility Function
580(1)
Summary
581(1)
Glossary
582(2)
Problems
584(11)
Case Problem: Property Purchase Strategy
595(1)
Appendix: Using Analytic Solver Platform to Create Decision Trees
596(13)
Appendix A Basics of Excel 609(12)
Appendix B Data Management and Microsoft Access 621(38)
Appendix C Answers to Even-Numbered Exercises (online)
References 659(2)
Index 661
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 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 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 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. 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 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. N/A