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Business Analytics 6th edition

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Jeffrey D. Camm, James J. Cochran, Michael J. Fry, and Jeffrey W. Ohlmann
Publisher: Cengage Learning

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  • Camm Business Analytics 6e - Homework and Quizzes

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

  • Chapter 1: Introduction to Business Analytics
    • 1.1: Business Analytics and Decision Making
    • 1.2: Using Analytics for Improved Decision Making and Problem Solving
    • 1.3: Big Data
    • 1.4: Artificial Intelligence
    • 1.5: Business Analytics in Practice
    • 1.6: Legal and Ethical Issues in the Use of Data and Analytics
    • 1: Conceptual Problems
    • 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: Conceptual Problems
    • 2: Application Problems
    • 2: Extra Problems
    • 2: R Practice Problems
    • 2: Python Practice Problems
    • 2: Excel Online Activities
    • 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: Conceptual Problems
    • 3: Application Problems
    • 3: Extra Problems
    • 3: R Practice Problems
    • 3: Python Practice Problems
    • 3: Excel Online Activities
    • 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: Conceptual Problems
    • 4: Application Problems
    • 4: Extra Problems
    • 4: R Practice Problems
    • 4: Python Practice Problems
    • 4: Excel Online Activities
    • 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: Conceptual Problems
    • 5: Application Problems
    • 5: Extra Problems
    • 5: R Practice Problems
    • 5: Python Practice Problems
    • 5: Excel Online Activities
    • 5: Exploring Analytics Applet Exercises
    • 5: Test Bank

  • Chapter 6: Unsupervised Machine Learning
    • 6.1: Dimension Reduction
    • 6.2: Cluster Analysis
    • 6.3: Association Rules
    • 6.4: Text Analytics
    • 6: Conceptual Problems
    • 6: Extra Problems
    • 6: R Application Problems
    • 6: Orange Application Problems
    • 6: Python Application 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: Test of Independence
    • 7.7: Big Data, Statistical Inference, and Practical Significance
    • 7: Conceptual Problems
    • 7: Application Problems
    • 7: Extra Problems
    • 7: R Practice Problems
    • 7: Python Practice Problems
    • 7: Excel Online Activities
    • 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: Conceptual Problems
    • 8: Application Problems
    • 8: Extra Problems
    • 8: R Practice Problems
    • 8: Python Practice Problems
    • 8: Excel Online Activities
    • 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: Conceptual Problems
    • 9: Application Problems
    • 9: Extra Problems
    • 9: R Practice Problems
    • 9: Python Practice Problems
    • 9: Excel Online Activities
    • 9: Exploring Analytics Applet Exercises
    • 9: Test Bank

  • Chapter 10: Supervised Machine Learning: 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: Conceptual Problems
    • 10: Extra Problems
    • 10: R Application Problems
    • 10: Orange Application Problems
    • 10: Python Application Problems
    • 10: Exploring Analytics Applet Exercises
    • 10: Test Bank

  • Chapter 11: Supervised Machine Learning: 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: Conceptual Problems
    • 11: Extra Problems
    • 11: R Application Problems
    • 11: Orange Application Problems
    • 11: Python Application 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: Conceptual Problems
    • 12: Application Problems
    • 12: Extra Problems
    • 12: R Practice Problems
    • 12: Python Practice Problems
    • 12: Excel Online Activities
    • 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: Conceptual Problems
    • 13: Application Problems
    • 13: Extra Problems
    • 13: R Practice Problems
    • 13: Python Practice Problems
    • 13: Excel Online Activities
    • 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: Conceptual Problems
    • 14: Application Problems
    • 14: Extra Problems
    • 14: R Practice Problems
    • 14: Python Practice Problems
    • 14: Excel Online Activities
    • 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: Conceptual Problems
    • 15: Application Problems
    • 15: Extra Problems
    • 15: R Practice Problems
    • 15: Python Practice 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: Conceptual Problems
    • 16: Application Problems
    • 16: Extra Problems
    • 16: R Practice Problems
    • 16: Python Practice 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: Conceptual Problems
    • 17: Application Problems
    • 17: Extra Problems
    • 17: R Practice Problems
    • 17: Python Practice Problems
    • 17: Test Bank

  • Chapter 18: Artificial Intelligence
    • 18.1: What is AI?
    • 18.2: A Brief History of AI
    • 18.3: AI in Practice
    • 18.4: Large Language Models
    • 18.5: Prompt Engineering
    • 18.6: Ethical Concerns Related to AI
    • 18.7: Legal Considerations Related to AI
    • 18: Conceptual Problems
    • 18: Application Problems
    • 18: Extra Problems
    • 18: R Practice Problems
    • 18: Python Practice Problems
    • 18: Test Bank

  • Chapter A: Appendix
    • A: Appendix A: Basics of Excel

Questions Available within WebAssign

Most questions from this textbook are available in WebAssign. The online questions are identical to the textbook questions except for minor wording changes necessary for Web use. Whenever possible, variables, numbers, or words have been randomized so that each student receives a unique version of the question. This list is updated nightly.

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Group Quantity Questions
Chapter 1: Introduction to Business Analytics
1 0  
Chapter 2: Descriptive Statistics
2 0  
Chapter 3: Data Visualization
3 0  
Chapter 4: Data Wrangling: Data Management and Data Cleaning Strategies
4 0  
Chapter 5: Probability: An Introduction to Modeling Uncertainty
5 0  
Chapter 6: Unsupervised Machine Learning
6 0  
Chapter 7: Statistical Inference
7 0  
Chapter 8: Linear Regression
8 0  
Chapter 9: Time Series Analysis and Forecasting
9 0  
Chapter 10: Supervised Machine Learning: Regression Tasks
10 0  
Chapter 11: Supervised Machine Learning: Classification Tasks
11 0  
Chapter 12: Spreadsheet Models
12 0  
Chapter 13: Monte Carlo Simulation
13 0  
Chapter 14: Linear Optimization Models
14 0  
Chapter 15: Integer Linear Optimization Models
15 0  
Chapter 16: Nonlinear Optimization Models
16 0  
Chapter 17: Decision Analysis
17 0  
Chapter 18: Artificial Intelligence
18 0  
Total 0  

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