Test Bank For Business Analytics 5th Edition Jeffrey Camm

Test Bank For Business Analytics 5th Edition Jeffrey Camm

• Acknowledgments
• Chapter 1. Introduction to Business Analytics
  • 1.1. Decision Making
  • 1.2. Business Analytics Defined
  • 1.3. A Categorization of Analytical Methods and Models
    • Descriptive Analytics
    • Predictive Analytics
    • Prescriptive Analytics
  • 1.4. Big Data, the Cloud, and Artificial Intelligence
    • Volume
    • Velocity
    • Variety
    • Veracity
  • 1.5. Business Analytics in Practice
    • Accounting Analytics
    • Financial Analytics
    • Human Resource (HR) Analytics
    • Marketing Analytics
    • Health Care Analytics
    • Supply Chain Analytics
    • Analytics for Government and Nonprofits
    • Sports Analytics
    • Web Analytics
  • 1.6. Legal and Ethical Issues in the Use of Data and Analytics
  • Summary
  • Glossary
  • Problems
  • Appendix. Getting Started with R and RStudio
  • Appendix. Basic Data Manipulation with R
• Chapter 2. Descriptive Statistics
  • 2.1. Overview of Using Data: Definitions and Goals
  • 2.2. Types of Data
    • Population and Sample Data
    • Quantitative and Categorical Data
    • Cross-Sectional and Time Series Data
    • Sources of Data
  • 2.3. Exploring Data in Excel
    • Sorting and Filtering Data in Excel
    • Conditional Formatting of Data in Excel
  • 2.4. Creating Distributions from Data
    • Frequency Distributions for Categorical Data
    • Relative Frequency and Percent Frequency Distributions
    • Frequency Distributions for Quantitative Data
    • Histograms
    • Frequency Polygons
    • Cumulative Distributions
  • 2.5. Measures of Location
    • Mean (Arithmetic Mean)
    • Median
    • Mode
    • Geometric Mean
  • 2.6. Measures of Variability
    • Range
    • Variance
    • Standard Deviation
    • Coefficient of Variation
  • 2.7. Analyzing Distributions
    • Percentiles
    • Quartiles
    • z-Scores
    • Empirical Rule
    • Identifying Outliers
    • Boxplots
  • 2.8. Measures of Association Between Two Variables
    • Scatter Charts
    • Covariance
    • Correlation Coefficient
  • Summary
  • Glossary
  • Problems
  • Case Problem 1. Heavenly Chocolates Web Site Transactions
  • Case Problem 2. African Elephant Populations
  • Appendix. Descriptive Statistics with R
• Chapter 3. Data Visualization
  • 3.1. Overview of Data Visualization
    • Preattentive Attributes
    • Data-Ink Ratio
  • 3.2. Tables
    • Table Design Principles
    • Crosstabulation
    • PivotTables in Excel
  • 3.3. Charts
    • Scatter Charts
    • Recommended Charts in Excel
    • Line Charts
    • Bar Charts and Column Charts
    • A Note on Pie Charts and Three-Dimensional Charts
    • Additional Visualizations for Multiple Variables: Bubble Chart, Scatter Chart Matrix, and Table Lens
    • PivotCharts in Excel
  • 3.4. Specialized Data Visualizations
    • Heat Maps
    • Treemaps
    • Waterfall Charts
    • Stock Charts
    • Parallel-Coordinates Chart
  • 3.5. Visualizing Geospatial Data
    • Choropleth Maps
    • Cartograms
  • 3.6. Data Dashboards
    • Principles of Effective Data Dashboards
    • Applications of Data Dashboards
  • Summary
  • Glossary
  • Problems
  • Case Problem 1. Pelican stores
  • Case Problem 2. Movie Theater Releases
  • Appendix. Creating Tabular and Graphical Presentations with R
  • Appendix. Data Visualization in Tableau Appendix
• Chapter 4. Data Wrangling: Data Management and Data Cleaning Strategies
  • 4.1. Discovery
    • Accessing Data
    • The Format of the Raw Data
  • 4.2. Structuring
    • Data Formatting
    • Arrangement of Data
    • Splitting a Single Field into Multiple Fields
    • Combining Multiple Fields into a Single Field
  • 4.3. Cleaning
    • Missing Data
    • Identification of Erroneous Outliers, Other Erroneous Values, and Duplicate Records
  • 4.4. Enriching
    • Subsetting Data
    • Supplementing Data
    • Enhancing Data
  • 4.5. Validating and Publishing
    • Validating
    • Publishing
  • Summary
  • Glossary
  • Problems
  • Case Problem 1. Usman Solutions
  • Appendix. Importing Delimited Files into R
  • Appendix. Working with Records in R
  • Appendix. Working with Fields in R
  • Appendix. Unstacking and Stacking Data with R
• Chapter 5. Probability: An Introduction to Modeling Uncertainty
  • 5.1. Events and Probabilities
  • 5.2. Some Basic Relationships of Probability
    • Complement of an Event
    • Addition Law
  • 5.3. Conditional Probability
    • Independent Events
    • Multiplication Law
    • Bayes’ Theorem
  • 5.4. Random Variables
    • Discrete Random Variables
    • Continuous Random Variables
  • 5.5. Discrete Probability Distributions
    • Custom Discrete Probability Distribution
    • Expected Value and Variance
    • Discrete Uniform Probability Distribution
    • Binomial Probability Distribution
    • Poisson Probability Distribution
  • 5.6. Continuous Probability Distributions
    • Uniform Probability Distribution
    • Triangular Probability Distribution
    • Normal Probability Distribution
    • Exponential Probability Distribution
  • Summary
  • Glossary
  • Problems
  • Case Problem 1. Hamilton County Judges
  • Case Problem 2. McNeil’s Auto Mall
  • Case Problem 3. Gebhardt Electronics
  • Appendix. Discrete Probability Distributions with R
  • Appendix. Continuous Probability Distributions with R
• Chapter 6. Descriptive Data Mining
  • 6.1. Dimension Reduction
    • Geometric Interpretation of Principal Component Analysis
    • Summarizing Protein Consumption for Maillard Riposte
  • 6.2. Cluster Analysis
    • Measuring Distance Between Observations Consisting of Quantitative Variables
    • Measuring Distance Between Observations Consisting of Categorical Variables
    • k-Means Clustering
    • Hierarchical Clustering and Measuring Dissimilarity Between Clusters
    • Hierarchical Clustering versus k-Means Clustering
  • 6.3. Association Rules
    • Evaluating Association Rules
  • 6.4. Text Mining
    • Voice of the Customer at Triad Airlines
    • Preprocessing Text Data for Analysis
    • Movie Reviews
    • Computing Dissimilarity Between Documents
    • Word Clouds
  • Summary
  • Glossary
  • Problems
  • Case Problem 1. Big Ten Expansion
  • Case Problem 2. Know Thy Customer
  • Appendix. Principal Component Analysis with R
  • Appendix. k-Means Clustering with R
  • Appendix. Hierarchical Clustering with R
  • Appendix. Association Rules with R
  • Appendix. Text Mining with R
  • Appendix. Principal Component Analysis with Orange
  • Appendix. k-Means Clustering with Orange
  • Appendix. Hierarchical Clustering with Orange
  • Appendix. Association Rules with Orange
  • Appendix. Text Mining with Orange
• Chapter 7. Statistical Inference
  • 7.1. Selecting a Sample
    • Sampling from a Finite Population
    • Sampling from an Infinite Population
  • 7.2. Point Estimation
    • Practical Advice
  • 7.3. Sampling Distributions
    • Sampling Distribution of x¯
    • Sampling Distribution of p¯
  • 7.4. Interval Estimation
    • Interval Estimation of the Population Mean
    • Interval Estimation of the Population Proportion
  • 7.5. Hypothesis Tests
    • Developing Null and Alternative Hypotheses
    • Type I and Type II Errors
    • Hypothesis Test of the Population Mean
    • Hypothesis Test of the Population Proportion
  • 7.6. Big Data, Statistical Inference, and Practical Significance
    • Sampling Error
    • Nonsampling Error
    • Big Data
    • Understanding What Big Data Is
    • Big Data and Sampling Error
    • Big Data and the Precision of Confidence Intervals
    • Implications of Big Data for Confidence Intervals
    • Big Data, Hypothesis Testing, and p Values
    • Implications of Big Data in Hypothesis Testing
  • Summary
  • Glossary
  • Problems
  • Case Problem 1. Young Professional Magazine
  • Case Problem 2. Quality Associates, Inc.
  • Appendix. Random Sampling with R
  • Appendix. Interval Estimation of a Population Mean, Unknown Standard Deviation with R
  • Appendix. Interval Estimation of a Population Proportion with R
  • Appendix. Hypothesis Testing of a Population Mean, Unknown Standard Deviation with R
  • Appendix. Hypothesis Testing of a Population Proportion with R
• Chapter 8. Linear Regression
  • 8.1. Simple Linear Regression Model
    • Estimated Simple Linear Regression Equation
  • 8.2. Least Squares Method
    • Least Squares Estimates of the Simple Linear Regression Parameters
    • Using Excel’s Chart Tools to Compute the Estimated Simple Linear Regression Equation
  • 8.3. Assessing the Fit of the Simple Linear Regression Model
    • The Sums of Squares
    • The Coefficient of Determination
    • Using Excel’s Chart Tools to Compute the Coefficient of Determination
  • 8.4. The Multiple Linear Regression Model
    • Estimated Multiple Linear Regression Equation
    • Least Squares Method and Multiple Linear Regression
    • Butler Trucking Company and Multiple Linear Regression
    • Using Excel’s Regression Tool to Develop the Estimated Multiple Linear Regression Equation
  • 8.5. Inference and Linear Regression
    • Conditions Necessary for Valid Inference in the Least Squares Linear Regression Model
    • Testing Individual Linear Regression Parameters
    • Addressing Nonsignificant Independent Variables
    • Multicollinearity
  • 8.6. Categorical Independent Variables
    • Butler Trucking Company and Rush Hour
    • Interpreting the Parameters
    • More Complex Categorical Variables
  • 8.7. Modeling Nonlinear Relationships
    • Quadratic Regression Models
    • Piecewise Linear Regression Models
    • Interaction Between Independent Variables
  • 8.8. Model Fitting
    • Variable Selection Procedures
    • Overfitting
  • 8.9. Big Data and Linear Regression
    • Inference and Very Large Samples
    • Model Selection
  • 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
  • Appendix. Simple Linear Regression with R
  • Appendix. Multiple Linear Regression with R
  • Appendix. Regression Variable Selection Procedures with R
• Chapter 9. Time Series Analysis and Forecasting
  • 9.1. Time Series Patterns
    • Horizontal Pattern
    • Trend Pattern
    • Seasonal Pattern
    • Trend and Seasonal Pattern
    • Cyclical Pattern
    • Identifying Time Series Patterns
  • 9.2. Forecast Accuracy
  • 9.3. Moving Averages and Exponential Smoothing
    • Moving Averages
    • Exponential Smoothing
  • 9.4. Using Linear Regression Analysis for Forecasting
    • Linear Trend Projection
    • Seasonality Without Trend
    • Seasonality with Trend
    • Using Linear Regression Analysis as a Causal Forecasting Method
    • Combining Causal Variables with Trend and Seasonality Effects
    • Considerations in Using Linear Regression in Forecasting
  • 9.5. 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 9.1. Using the Excel Forecast Sheet
  • Appendix. Forecasting with R
• Chapter 10. Predictive Data Mining: Regression Tasks
  • 10.1. Regression Performance Measures
  • 10.2. Data Sampling, Preparation, and Partitioning
    • Static Holdout Method
    • k-Fold Cross-Validation
  • 10.3. k-Nearest Neighbors Regression
  • 10.4. Regression Trees
    • Constructing a Regression Tree
    • Generating Predictions with a Regression Tree
    • Ensemble Methods
  • 10.5. Neural Network Regression
    • Structure of a Neural Network
    • How a Neural Network Learns
  • 10.6. Feature Selection
    • Wrapper Methods
    • Filter Methods
    • Embedded Methods
  • Summary
  • Glossary
  • Problems
  • Case Problem. Housing Bubble
  • Appendix. k-Nearest Neighbors (k-NN) Regression with R
  • Appendix. Regression Trees with R
  • Appendix. Random Forest Regression with R
  • Appendix. Neural Network Regression with R
  • Appendix. Regularized Linear Regression with R
  • Appendix. k-Nearest Neighbors Regression with Orange
  • Appendix. Individual Regression Trees with Orange
  • Appendix. Random Forests of Regression Trees with Orange
  • Appendix. Neural Network Regression with Orange
  • Appendix. Regularized Linear Regression with Orange
• Chapter 11. Predictive Data Mining: Classification Tasks
  • 11.1. Data Sampling, Preparation, and Partitioning
    • Static Holdout Method
    • k-Fold Cross-Validation
    • Class Imbalanced Data
  • 11.2. Performance Measures for Binary Classification
  • 11.3. Classification with Logistic Regression
  • 11.4. k-Nearest Neighbors Classification
  • 11.5. Classification Trees
    • Constructing a Classification Tree
    • Generating Predictions with a Classification Tree
    • Ensemble Methods
  • 11.6. Neural Network Classification
    • Structure of a Neural Network
    • How a Neural Network Learns
  • 11.7. Feature Selection
    • Wrapper Methods
    • Filter Methods
    • Embedded Methods
  • Summary
  • Glossary
  • Problems
  • Case Problem. Grey Code Corporation
  • Appendix. Logistic Regression with R
  • Appendix. k-Nearest Neighbors with R
  • Appendix. Classification Trees with R
  • Appendix. Classification Forests with R
  • Appendix. Neural Network Classification with R
  • Appendix. Classification via Logistic Regression with Orange
  • Appendix. K-Nearest Neighbors Classification with Orange
  • Appendix. Individual Classification Trees with Orange
  • Appendix. Random Forests of Classification Trees with Orange
  • Appendix. Neural Network Classification with Orange
• Chapter 12. Spreadsheet Models
  • 12.1. Building Good Spreadsheet Models
    • Influence Diagrams
    • Building a Mathematical Model
    • Spreadsheet Design and Implementing the Model in a Spreadsheet
  • 12.2. What-If Analysis
    • Data Tables
    • Goal Seek
    • Scenario Manager
  • 12.3. Some Useful Excel Functions for Modeling
    • SUM and SUMPRODUCT
    • IF and COUNTIF
    • XLOOKUP
  • 12.4. Auditing Spreadsheet Models
    • Trace Precedents and Dependents
    • Show Formulas
    • Evaluate Formulas
    • Error Checking
    • Watch Window
  • 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
    • Base-Case Scenario
    • Worst-Case Scenario
    • Best-Case Scenario
    • Sanotronics Spreadsheet Model
    • Use of Probability Distributions to Represent Random Variables
    • Generating Values for Random Variables with Excel
    • Executing Simulation Trials with Excel
    • Measuring and Analyzing Simulation Output
  • 13.2. Inventory Policy Analysis for Promus Corp
    • Spreadsheet Model for Promus
    • Generating Values for Promus Corp’s Demand
    • Executing Simulation Trials and Analyzing Output
  • 13.3. Simulation Modeling for Land Shark Inc.
    • Spreadsheet Model for Land Shark
    • Generating Values for Land Shark’s Random Variables
    • Executing Simulation Trials and Analyzing Output
    • Generating Bid Amounts with Fitted Distributions
  • 13.4. Simulation with Dependent Random Variables
    • Spreadsheet Model for Press Teag Worldwide
  • 13.5. Simulation Considerations
    • Verification and Validation
    • Advantages and Disadvantages of Using Simulation
  • 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
    • Problem Formulation
    • Mathematical Model for the Par, Inc. Problem
  • 14.2. Solving the Par, Inc. Problem
    • The Geometry of the Par, Inc. Problem
    • Solving Linear Programs with Excel Solver
  • 14.3. A Simple Minimization Problem
    • Problem Formulation
    • Solution for the M&D Chemicals Problem
  • 14.4. Special Cases of Linear Program Outcomes
    • Alternative Optimal Solutions
    • Infeasibility
    • Unbounded
  • 14.5. Sensitivity Analysis
    • Interpreting Excel Solver Sensitivity Report
  • 14.6. General Linear Programming Notation and More Examples
    • Investment Portfolio Selection
    • Transportation Planning
    • Maximizing Banner Ad Revenue
    • Assigning Project Leaders to Clients
    • Diet Planning
  • 14.7. Generating an Alternative Optimal Solution for a Linear Program
  • Summary
  • Glossary
  • Problems
  • Case Problem 1. Investment Strategy
  • Case Problem 2. Solutions Plus
  • Appendix. Linear Programming with R
• Chapter 15. Integer Linear Optimization Models
  • 15.1. Types of Integer Linear Optimization Models
  • 15.2. Eastborne Realty, an Example of Integer Optimization
    • The Geometry of Linear All-Integer Optimization
  • 15.3. Solving Integer Optimization Problems with Excel Solver
    • A Cautionary Note About Sensitivity Analysis
  • 15.4. Applications Involving Binary Variables
    • Capital Budgeting
    • Fixed Cost
    • Bank Location
    • Product Design and Market Share Optimization
  • 15.5. Modeling Flexibility Provided by Binary Variables
    • Multiple-Choice and Mutually Exclusive Constraints
    • k Out of n Alternatives Constraint
    • Conditional and Corequisite Constraints
  • 15.6. Generating Alternatives in Binary Optimization
  • Summary
  • Glossary
  • Problems
  • Case Problem 1. Applecore Children’s Clothing
  • Case Problem 2. Yeager National Bank
  • Appendix. Integer Programming with R
• Chapter 16. Nonlinear Optimization Models
  • 16.1. A Production Application: Par, Inc. Revisited
    • An Unconstrained Problem
    • A Constrained Problem
    • Solving Nonlinear Optimization Models Using Excel Solver
    • Sensitivity Analysis and Shadow Prices in Nonlinear Models
  • 16.2. Local and Global Optima
    • Overcoming Local Optima with Excel Solver
  • 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
  • Appendix. Nonlinear Programming with R
• Chapter 17. Decision Analysis
  • 17.1. Problem Formulation
    • Payoff Tables
    • Decision Trees
  • 17.2. Decision Analysis Without Probabilities
    • Optimistic Approach
    • Conservative Approach
    • Minimax Regret Approach
  • 17.3. Decision Analysis with Probabilities
    • Expected Value Approach
    • Risk Analysis
    • Sensitivity Analysis
  • 17.4. Decision Analysis with Sample Information
    • Expected Value of Sample Information
    • Expected Value of Perfect Information
  • 17.5. Computing Branch Probabilities with Bayes’ Theorem
  • 17.6. Utility Theory
    • Utility and Decision Analysis
    • Utility Functions
    • Exponential Utility Function
  • Summary
  • Glossary
  • Problems
  • Case Problem 1. Property Purchase Strategy
  • Case Problem 2. Semiconductor Fabrication at Axeon Labs
• Case Problem: Capital State University Game-Day Magazines
• Case Problem: Hanover Inc.
• Appendix A. Basics of Excel
• Appendix B. Database Basics with Microsoft Access
• Appendix C. Solutions to Even-Numbered Problems
• Appendix D. Microsoft Excel Online and Tools for Statistical Analysis
• References