Business Analytics 5th edition

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• 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
  • 1: Exercises
  • 1: Extra Problems
  • 1: Test Bank
  
  
• 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
  • 2: Exercises
  • 2: Extra Problems
  • 2: R Appendix Practice Problems
  • 2: Case Problems
  • 2: Exploring Analytics Applet Exercises
  • 2: Test Bank
  
  
• Chapter 3: Data Visualization
  • 3.1: Overview of Data Visualization
  • 3.2: Tables
  • 3.3: Charts
  • 3.4: Specialized Data Visualization
  • 3.5: Visualizing Geospatial Data
  • 3.6: Data Dashboards
  • 3: Exercises
  • 3: Extra Problems
  • 3: R Appendix Practice Problems
  • 3: Case Problems
  • 3: Exploring Analytics Applet Exercises
  • 3: Test Bank
  
  
• 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
  • 4: Exercises
  • 4: Extra Problems
  • 4: Case Problems
  • 4: Exploring Analytics Applet Exercises
  • 4: Test Bank
  
  
• 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
  • 5: Exercises
  • 5: Extra Problems
  • 5: R Appendix Practice Problems
  • 5: Case Problems
  • 5: Exploring Analytics Applet Exercises
  • 5: Test Bank
  
  
• Chapter 6: Descriptive Data Mining
  • 6.1: Dimension Reduction
  • 6.2: Cluster Analysis
  • 6.3: Association Rules
  • 6.4: Text Mining
  • 6: Exercises
  • 6: Extra Problems
  • 6: R Appendix Practice Problems
  • 6: Orange Appendix Practice Problems
  • 6: Case Problems
  • 6: Exploring Analytics Applet Exercises
  • 6: Test Bank
  
  
• 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
  • 7: Exercises
  • 7: Extra Problems
  • 7: R Appendix Practice Problems
  • 7: Case Problems
  • 7: Exploring Analytics Applet Exercises
  • 7: Test Bank
  
  
• 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 Regression
  • 8.10: Prediction with Regression
  • 8: Exercises
  • 8: Extra Problems
  • 8: R Appendix Practice Problems
  • 8: Case Problems
  • 8: Exploring Analytics Applet Exercises
  • 8: Test Bank
  
  
• 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
  • 9: Exercises
  • 9: Extra Problems
  • 9: R Appendix Practice Problems
  • 9: Case Problems
  • 9: Exploring Analytics Applet Exercises
  • 9: Test Bank
  
  
• 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
  • 10: Exercises
  • 10: Extra Problems
  • 10: R Appendix Practice Problems
  • 10: Orange Appendix Practice Problems
  • 10: Case Problems
  • 10: Exploring Analytics Applet Exercises
  • 10: Test Bank
  
  
• 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
  • 11: Exercises
  • 11: Extra Problems
  • 11: R Appendix Practice Problems
  • 11: Orange Appendix Practice Problems
  • 11: Case Problems
  • 11: Exploring Analytics Applet Exercises
  • 11: Test Bank
  
  
• 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
  • 12: Exercises
  • 12: Extra Problems
  • 12: Case Problems
  • 12: Exploring Analytics Applet Exercises
  • 12: Test Bank
  
  
• 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
  • 13: Exercises
  • 13: Extra Problems
  • 13: Case Problems
  • 13: Exploring Analytics Applet Exercises
  • 13: Test Bank
  
  
• 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
  • 14: Exercises
  • 14: Extra Problems
  • 14: Case Problems
  • 14: Exploring Analytics Applet Exercises
  • 14: Test Bank
  
  
• 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
  • 15: Exercises
  • 15: Extra Problems
  • 15: Case Problems
  • 15: Exploring Analytics Applet Exercises
  • 15: Test 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
  • 16: Exercises
  • 16: Extra Problems
  • 16: Case Problems
  • 16: Exploring Analytics Applet Exercises
  • 16: Test Bank
  
  
• 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
  • 17: Exercises
  • 17: Extra Problems
  • 17: Case Problems
  • 17: Test Bank
  
  
• Chapter A: Appendix
  • A: Appendix A: Basics of Excel