Business Analytics: Data Analysis & Decision Making

Höfundar: S. Christian Albright, Wayne L. Winston (Útgáfa: 8)
Business Analytics: Data Analysis & Decision Making

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Útgefandi
Cengage Learning EMEA
ISBN
9798214493497
Print ISBN
9798214050270
Format
Page Fidelity (PDF)
Útgáfa
8
Höfundar
S. Christian Albright, Wayne L. Winston
Tungumál
English
Útgefið
06/15/2024
Prent takmörkun á líftíma
100
Prent takmörkun
2
Afritunar takmörkun
2

Kaflar

  • Dedication
  • About The Authors
  • Brief Contents
  • Contents
  • Preface
  • Overview of Applications in the Book by Discipline
  • Chapter 1: Introduction to Business Analytics
  • Learning Objectives
  • Business Analytics Provides Insights and Improves Performance
  • 1-1 Introduction
  • 1-2 Overview of the Book
  • 1-3 Introduction to Spreadsheet Modeling
  • 1-4 Conclusion
  • Summary of Key Terms
  • Problems
  • Part 1: Data Analysis
  • Chapter 2: Describing the Distribution of a Variable
  • Learning Objectives
  • Recent Presidential Elections
  • 2-1 Introduction
  • 2-2 Basic Concepts
  • 2-3 Summarizing Categorical Variables
  • 2-4 Summarizing Numeric Variables
  • 2-5 Time Series Data
  • 2-6 Outliers and Missing Values
  • 2-7 Excel Tables for Filtering, Sorting, and Summarizing
  • 2-8 Conclusion
  • Summary of Key Terms
  • Problems
  • Appendix: Introduction to StatTools
  • Appendix: Summary Stats with DADM_Tools
  • Chapter 3: Finding Relationships
  • Learning Objectives
  • Data Analytics at NYPD
  • 3-1 Introduction
  • 3-2 Relationships Among Categorical Variables
  • 3-3 Relationships Among Categorical Variables and a Numeric Variable
  • 3-4 Relationships Among Numeric Variables
  • 3-5 Pivot Tables
  • 3-6 Conclusion
  • Summary of Key Terms
  • Problems
  • Appendix: Using StatTools to Find Relationships
  • Appendix: Using DADM_Tools to Summarize Data
  • Chapter 4: Business Intelligence (BI) Tools
  • Learning Objectives
  • Power BI at Heathrow Airport
  • 4-1 Introduction
  • 4-2 Introduction to Relational Databases
  • 4-3 Storing Data in a Data Model
  • 4-4 Using Power Query in Excel
  • 4-5 Using Power Query in Power BI Desktop
  • 4-6 Conclusion
  • Summary of Key Terms
  • Problems
  • Chapter 5: Business Intelligence (BI) Tools for Data Analysis: Power Pivot
  • Learning Objectives
  • Power BI at T-Mobile
  • 5-1 Introduction
  • 5-2 Basing Pivot Tables on a Data Model
  • 5-3 Using Power Pivot in Excel
  • 5-4 Creating KPIs in Power Pivot
  • 5-5 Creating Visualizations in Power BI Desktop
  • 5-6 Conclusion
  • Summary of Key Terms
  • Problems
  • Appendix: Writing Complex DAX Formulas
  • Part 2: Probability and Decision Making under Uncertainty
  • Chapter 6: Probability and Probability Distributions
  • Learning Objectives
  • Judging Probabilities
  • 6-1 Introduction
  • 6-2 Probability Essentials
  • 6-3 Probability Distribution of a Random Variable
  • 6-4 The Normal Distribution
  • 6-5 The Binomial Distribution
  • 6-6 The Poisson and Exponential Distributions
  • 6-7 Conclusion
  • Summary of Key Terms
  • Problems
  • Chapter 7: Decision Making Under Uncertainty
  • Learning Objectives
  • Cost-Effective Hepatitis B Interventions
  • 7-1 Introduction
  • 7-2 Elements of Decision Analysis
  • 7-3 EMV and Decision Trees
  • 7-4 One-Stage Decision Problems
  • 7-5 The PrecisionTree Add-In
  • 7-6 Multistage Decision Problems
  • 7-7 The Role of Risk Aversion
  • 7-8 Conclusion
  • Summary of Key Terms
  • Problems
  • Appendix: Decision Trees with DADM_Tools
  • Part 3: Statistical Inference, Regression Analysis, and Time Series Forecasting
  • Chapter 8: Statistical Inference
  • Learning Objectives
  • Sample Size Selection in a Legal Case
  • 8-1 Introduction
  • 8-2 Populations and Sampling
  • 8-3 Sampling Distributions
  • 8-4 Concepts in Hypothesis Testing and Confidence Interval Estimation
  • 8-5 Overview of Statistical Inference Procedures
  • 8-6 Inference About a Mean
  • 8-7 Inference About the Difference Between Means
  • 8-8 Inference About a Proportion
  • 8-9 Inference About the Difference Between Proportions
  • 8-10 Sample Size Determination
  • 8-11 Conclusion
  • Summary of Key Terms
  • Problems
  • Chapter 9: Regression Analysis: Estimating Relationships
  • Learning Objectives
  • Hewlett Packard Delivering Profitable Growth for HPDirect.com
  • 9-1 Introduction
  • 9-2 Scatterplots: Graphing Relationships
  • 9-3 Correlations: Indicators of Linear Relationships
  • 9-4 Simple Linear Regression
  • 9-5 Multiple Regression
  • 9-6 Modeling Possibilities
  • 9-7 Validation of the Fit
  • 9-8 Conclusion
  • Summary of Key Terms
  • Problems
  • Appendix: Regression with DADM_Tools
  • Chapter 10: Regression Analysis: Statistical Inference
  • Learning Objectives
  • Forecasting in the Tire Industry
  • 10-1 Introduction
  • 10-2 The Statistical Model
  • 10-3 Inferences About the Regression Coefficients
  • 10-4 Multicollinearity
  • 10-5 Include/Exclude Decisions
  • 10-6 Stepwise Regression
  • 10-7 Outliers
  • 10-8 Violations of Regression Assumptions
  • 10-9 Prediction
  • 10-10 Conclusion
  • Summary of Key Terms
  • Problems
  • Chapter 11: Time Series Analysis and Forecasting
  • Learning Objectives
  • Revenue Managementat Harrah's Cherokee Casino & Hotel
  • 11-1 Introduction
  • 11-2 Forecasting Methods: An Overview
  • 11-3 Testing for Randomness
  • 11-4 Regression-Based Trend Models
  • 11-5 The Random Walk Model
  • 11-6 Moving Averages Forecasts
  • 11-7 Exponential Smoothing Forecasts
  • 11-8 Seasonal Models
  • 11-9 Conclusion
  • Summary of Key Terms
  • Problems
  • Appendix: Alternative Forecasting Software
  • Part 4: Optimization and SimulationModeling
  • Chapter 12: Introduction to Optimization Modeling
  • Learning Objectives
  • Inventory Optimization at GM
  • 12-1 Introduction
  • 12-2 Introduction to Optimization
  • 12-3 A Two-Variable Product Mix Model
  • 12-4 Sensitivity Analysis
  • 12-5 Properties of Linear Models
  • 12-6 Infeasibility and Unboundedness
  • 12-7 A Larger Product Mix Model
  • 12-8 A Multiperiod Production Model
  • 12-9 A Comparison of Algebraic and Spreadsheet Models
  • 12-10 A Decision Support System
  • 12-11 Conclusion
  • Summary of Key Terms
  • Problems
  • Appendix: Another Solver Add-In
  • Chapter 13: Optimization Models
  • Learning Objectives
  • Optimization of Work Center Locations at Verizon
  • 13-1 Introduction
  • 13-2 Employee Scheduling Models
  • 13-3 Blending Models
  • 13-4 Logistics Models
  • 13-5 Aggregate Planning Models
  • 13-6 Financial Models
  • 13-7 Integer Optimization Models
  • 13-8 Nonlinear Optimization Models
  • 13-9 Conclusion
  • Summary of Key Terms
  • Problems
  • Chapter 14: Introduction to Simulation Modeling
  • Learning Objectives
  • Real Applications of Simulation with @RISK
  • 14-1 Introduction
  • 14-2 Probability Distributions for Input Variables
  • 14-3 Simulation and the Flaw of Averages
  • 14-4 Simulation with Built-in Excel Tools
  • 14-5 Simulation with @RISK
  • 14-6 The Effects of Input Distributions on Results
  • 14-7 Conclusion
  • Summary of Key Terms
  • Problems
  • Appendix: Simulation with DADM_Tools
  • Chapter 15: Simulation Models
  • Learning Objectives
  • Effects of Merit Pay on Payroll Growth at DoD
  • 15-1 Introduction
  • 15-2 Operations Models
  • 15-3 Financial Models
  • 15-4 Marketing Models
  • 15-5 Simulating Games of Chance
  • 15-6 Conclusion
  • Summary of Key Terms
  • Problems
  • Part 5: Advanced Data Analysis
  • Chapter 16: Data Mining: Classification
  • Learning Objectives
  • Hottest New Jobs: Statistics and Mathematics
  • 16-1 Introduction
  • 16-2 Classification Methods
  • 16-3 Measures of Classification Accuracy
  • 16-4 Classification with Rare Events
  • 16-5 Conclusion
  • Summary of Key Terms
  • Problems
  • Chapter 17: Data Mining: Clustering and Market Basket Analysis
  • Learning Objectives
  • Beer and Diapers: a Myth?
  • 17-1 Introduction
  • 17-2 Clustering
  • 17-3 Market Basket Analysis
  • 17-4 Conclusion
  • Summary of Key Terms
  • Problems
  • References
  • Index