Business Analytics. Descriptive. Predictive. Prescriptive

✍ Scribed by Jeffrey D. Camm, Michael J. Fry, James J. Cochran, Jeffrey W. Ohlmann

• Publisher: Cengage Learning
• Year: 2024
• Tongue: English
• Leaves: 1037
• Category: Library

✦ Table of Contents

Cover
Brief Contents
Contents
About the Authors
Preface
Chapter 1: Introduction to Business Analytics
1.1 Decision Making
1.2 Business Analytics Defined
1.3 A Categorization of Analytical Methods and Models
1.4 Big Data, the Cloud, and Artificial Intelligence
1.5 Business Analytics in Practice
1.6 Legal and Ethical Issues in the Use of Data and Analytics
Summary
Glossary
Problems
Chapter 2: Descriptive Statistics
2.1 Overview of Using Data: Definitions and Goals
2.2 Types of Data
2.3 Exploring 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 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 Specialized Data Visualizations
3.5 Visualizing Geospatial Data
3.6 Data Dashboards
Summary
Glossary
Problems
Case Problem 1: Pelican Stores
Case Problem 2: Movie Theater Releases
Chapter 4: Data Wrangling: Data Management and Data Cleaning Strategies
4.1 Discovery
4.2 Structuring
4.3 Cleaning
4.4 Enriching
4.5 Validating and Publishing
Summary
Glossary
Problems
Case Problem 1: Usman Solutions
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 1: Hamilton County Judges
Case Problem 2: McNeil's Auto Mall
Case Problem 3: Gebhardt Electronics
Chapter 6: Descriptive Data Mining
6.1 Dimension Reduction
6.2 Cluster Analysis
6.3 Association Rules
6.4 Text Mining
Summary
Glossary
Problems
Case Problem 1: Big Ten Expansion
Case Problem 2: Know Thy Customer
Chapter 7: Statistical Inference
7.1 Selecting a Sample
7.2 Point Estimation
7.3 Sampling Distributions
7.4 Interval Estimation
7.5 Hypothesis Tests
7.6 Big Data, Statistical Inference, and Practical Significance
Summary
Glossary
Problems
Case Problem 1: Young Professional Magazine
Case Problem 2: Quality Associates, Inc.
Chapter 8: Linear Regression
8.1 Simple Linear Regression Model
8.2 Least Squares Method
8.3 Assessing the Fit of the Simple Linear Regression Model
8.4 The Multiple Linear Regression Model
8.5 Inference and Linear Regression
8.6 Categorical Independent Variables
8.7 Modeling Nonlinear Relationships
8.8 Model Fitting
8.9 Big Data and Linear Regression
8.10 Prediction with Linear Regression
Summary
Glossary
Problems
Case Problem 1: Alumni Giving
Case Problem 2: Consumer Research, Inc.
Case Problem 3: Predicting Winnings for NASCAR Drivers
Chapter 9: Time Series Analysis and Forecasting
9.1 Time Series Patterns
9.2 Forecast Accuracy
9.3 Moving Averages and Exponential Smoothing
9.4 Using Linear Regression Analysis for Forecasting
9.5 Determining the Best Forecasting Model to Use
Summary
Glossary
Problems
Case Problem 1: Forecasting Food and Beverage
Case Problem 2: Forecasting Lost Sales
Appendix 9.1: Using the Excel Forecast Sheet
Chapter 10: Predictive Data Mining: Regression Tasks
10.1 Regression Performance Measures
10.2 Data Sampling, Preparation, and Partitioning
10.3 k-Nearest Neighbors Regression
10.4 Regression Trees
10.5 Neural Network Regression
10.6 Feature Selection
Summary
Glossary
Problems
Case Problem: Housing Bubble
Chapter 11: Predictive Data Mining: Classification Tasks
11.1 Data Sampling, Preparation, and Partitioning
11.2 Performance Measures for Binary Classification
11.3 Classification with Logistic Regression
11.4 k-Nearest Neighbors Classification
11.5 Classification Trees
11.6 Neural Network Classification
11.7 Feature Selection
Summary
Glossary
Problems
Case Problem: Grey Code Corporation
Chapter 12: Spreadsheet Models
12.1 Building Good Spreadsheet Models
12.2 What-If Analysis
12.3 Some Useful Excel Functions for Modeling
12.4 Auditing Spreadsheet Models
12.5 Predictive and Prescriptive Spreadsheet Models
Summary
Glossary
Problems
Case Problem: Retirement Plan
Chapter 13: Monte Carlo Simulation
13.1 Risk Analysis for Sanotronics LLC
13.2 Inventory Policy Analysis for Promus Corp
13.3 Simulation Modeling for Land Shark Inc.
13.4 Simulation with Dependent Random Variables
13.5 Simulation Considerations
Summary
Glossary
Problems
Case Problem 1: Four Corners
Case Problem 2: Ginsberg's Jewelry Snowfall Promotion
Appendix 13.1 Common Probability Distributions for Simulation
Chapter 14: Linear Optimization Models
14.1 A Simple Maximization Problem
14.2 Solving the Par, Inc. Problem
14.3 A Simple Minimization Problem
14.4 Special Cases of Linear Program Outcomes
14.5 Sensitivity Analysis
14.6 General Linear Programming Notation and More Examples
14.7 Generating an Alternative Optimal Solution for a Linear Program
Summary
Glossary
Problems
Case Problem 1: Investment Strategy
Case Problem 2: Solutions Plus
Chapter 15: Integer Linear Optimization Models
15.1 Types of Integer Linear Optimization Models
15.2 Eastborne Realty, an Example of Integer Optimization
15.3 Solving Integer Optimization Problems with Excel Solver
15.4 Applications Involving Binary Variables
15.5 Modeling Flexibility Provided by Binary Variables
15.6 Generating Alternatives in Binary Optimization
Summary
Glossary
Problems
Case Problem 1: Applecore Children's Clothing
Case Problem 2: Yeager National Bank
Chapter 16: Nonlinear Optimization Models
16.1 A Production Application: Par, Inc. Revisited
16.2 Local and Global Optima
16.3 A Location Problem
16.4 Markowitz Portfolio Model
16.5 Adoption of a New Product: The Bass Forecasting Model
16.6 Heuristic Optimization Using Excel's Evolutionary Method
Summary
Glossary
Problems
Case Problem: Portfolio Optimization with Transaction Costs
Chapter 17: Decision Analysis
17.1 Problem Formulation
17.2 Decision Analysis Without Probabilities
17.3 Decision Analysis with Probabilities
17.4 Decision Analysis with Sample Information
17.5 Computing Branch Probabilities with Bayes' Theorem
17.6 Utility Theory
Summary
Glossary
Problems
Case Problem 1: Property Purchase Strategy
Case Problem 2: Semiconductor Fabrication at Axeon Labs
Multi-Chapter Case Problems
Appendix A: Basics of Excel
Appendix B: Database Basics with Microsoft Access
Index