Business analytics : descriptive, predictive, prescriptive

Cover
Brief Contents
Contents
Preface
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
	1.6 Legal and Ethical Issues in the Use of Data and Analytics
	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
	2.9 Data Cleansing
	Summary
	Glossary
	Problems
	Case Problem 1: Heavenly Chocolates Web Site Transactions
	Case Problem 2: African Elephant Populations
Chapter 3: Data Visualization
	3.1: Overview of Data Visualization
	3.2: Tables
	3.3: Charts
	3.4: Advanced Data Visualization
	3.5: Data Dashboards
	Summary
	Glossary
	Problems
	Case Problem 1: Pelican stores
	Case Problem 2: Movie Theater Releases
	Appendix: Data Visualization in Tableau
Chapter 4: P robability: An Introduction to Modeling Uncertainty
	4.1 Events and Probabilities
	4.2 Some Basic Relationships of Probability
	4.3 Conditional Probability
	4.4 Random Variables
	4.5 Discrete Probability Distributions
	4.6 Continuous Probability Distributions
	Summary
	Glossary
	Problems
	Case Problem 1: Hamilton County Judges
	Case Problem 2: McNeil’s Auto Mall
	Case Problem 3: Gebhardt Electronics
Chapter 5: Descriptive Data Mining
	5.1 Cluster Analysis
	5.2 Association Rules
	5.3 Text Mining
	Summary
	Glossary
	Problems
	Case Problem 1: Big Ten Expansion
	Case Problem 2: Know Thy Customer
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
	6.6 Big Data, Statistical Inference, and Practical Significance
	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
	7.9 Big Data and Regression
	7.10 Prediction with Regression
	Summary
	Glossary
	Problems
	Case Problem 1: Alumni Giving
	Case Problem 2: Consumer Research, Inc.
	Case Problem 3: Predicting Winnings for NASCAR Drivers
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 1: Forecasting Food and Beverage Sales
	Case Problem 2: Forecasting Lost Sales
	Appendix: Using the Excel Forecast Sheet
Chapter 9: Predictive Data Mining
	9.1 Data Sampling, Preparation, and Partitioning
	9.2 Performance Measures
	9.3 Logistic Regression
	9.4 k-Nearest Neighbors
	9.5 Classification and Regression Trees
	Summary
	Glossary
	Problems
	Case Problem: Grey Code Corporation
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
	10.5 Predictive and Prescriptive Spreadsheet Models
	Summary
	Glossary
	Problems
	Case Problem: Retirement Plan
Chapter 11: Monte Carlo Simulation
	11.1 Risk Analysis for Sanotronics LLC
	11.2 Inventory Policy Analysis for Promus Corp
	11.3 Simulation Modeling for Land Shark Inc.
	11.4 Simulation with Dependent Random Variables
	11.5 Simulation Considerations
	Summary
	Glossary
	Problems
	Case Problem: Four Corners
	Appendix: Common Probability Distributions for Simulation
Chapter 12: Linear Optimization Models
	12.1 A Simple Maximization Problem
	12.2 Solving the Par, Inc. Problem
	12.3 A Simple Minimization Problem
	12.4 Special Cases of Linear Program Outcomes
	12.5 Sensitivity Analysis
	12.6 General Linear Programming Notation and More Examples
	12.7 Generating an Alternative Optimal Solution for a Linear Program
	Summary
	Glossary
	Problems
	Case Problem: Investment Strategy
Chapter 13: Integer Linear Optimization Models
	13.1 Types of Integer Linear Optimization Models
	13.2 Eastborne Realty, an Example of Integer Optimization
	13.3 Solving Integer Optimization Problems with Excel Solver
	13.4 Applications Involving Binary Variables
	13.5 Modeling Flexibility Provided by Binary Variables
	13.6 Generating Alternatives in Binary Optimization
	Summary
	Glossary
	Problems
	Case Problem: Applecore Children’s Clothing
Chapter 14: Nonlinear Optimization Models
	14.1 A Production Application: Par, Inc. Revisited
	14.2 Local and Global Optima
	14.3 A Location Problem
	14.4 Markowitz Portfolio Model
	14.5 Adoption of a New Product: The Bass Forecasting Model
	Summary
	Glossary
	Problems
	Case Problem: Portfolio Optimization with Transaction Costs
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
Multi-Chapter Case Problems
Appendix A: Basics of Excel
Appendix B Database Basics with Microsoft Access
References
Index