Business Analytics 4th edition

Access is contingent on use of this textbook in the instructor's classroom.

• 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
  • 1: Exercises
  • 1: Case Problems
  • 1: Test Bank
  
  
• 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
  • 2: Exercises
  • 2: Case Problems
  • 2: Exploring Analytics Applet Exercises
  • 2: JMP Statistics Applet Exercises
  • 2: Test Bank
  
  
• 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
  • 3: Exercises
  • 3: Case Problems
  • 3: Exploring Analytics Applet Exercises
  • 3: Test Bank
  
  
• Chapter 4: Probability: 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
  • 4: Exercises
  • 4: Case Problems
  • 4: Exploring Analytics Applet Exercises
  • 4: Test Bank
  
  
• Chapter 5: Descriptive Data Mining
  • 5.1: Cluster Analysis
  • 5.2: Association Rules
  • 5.3: Text Mining
  • 5: Exercises
  • 5: Case Problems
  • 5: Exploring Analytics Applet Exercises
  • 5: Test Bank
  
  
• 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
  • 6: Exercises
  • 6: Case Problems
  • 6: Exploring Analytics Applet Exercises
  • 6: JMP Statistics Applet Exercises
  • 6: Test Bank
  
  
• 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
  • 7: Exercises
  • 7: Case Problems
  • 7: Exploring Analytics Applet Exercises
  • 7: JMP Statistics Applet Exercises
  • 7: Test Bank
  
  
• 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
  • 8: Exercises
  • 8: Case Problems
  • 8: Exploring Analytics Applet Exercises
  • 8: Test Bank
  
  
• 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
  • 9: Exercises
  • 9: Case Problems
  • 9: Exploring Analytics Applet Exercises
  • 9: Test Bank
  
  
• 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
  • 10: Exercises
  • 10: Case Problems
  • 10: Test Bank
  
  
• 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
  • 11: Exercises
  • 11: Case Problems
  • 11: Test Bank
  
  
• 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
  • 12: Exercises
  • 12: Case Problems
  • 12: Exploring Analytics Applet Exercises
  • 12: Test Bank
  
  
• 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
  • 13: Exercises
  • 13: Case Problems
  • 13: Exploring Analytics Applet Exercises
  • 13: Test Bank
  
  
• 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
  • 14: Exercises
  • 14: Case Problems
  • 14: Exploring Analytics Applet Exercises
  • 14: Test Bank
  
  
• 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
  • 15: Exercises
  • 15: Case Problems
  • 15: Test Bank
  
  
• Chapter A: Appendix
  • A: Appendix A: Basics of Excel
  • A: Appendix B: Database Basics with Microsoft Access