Description
Essentials of Business Analytics by Jeffrey D. Camm EBOOK PDF Instant Download
Table of Contents
- Contents
- About the Authors
- Preface
- Chapter 1: Introduction
- 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
- Volume
- Velocity
- Variety
- Veracity
- 1.5 Business Analytics in Practice
- Financial Analytics
- Human Resource (HR) Analytics
- Marketing Analytics
- Health Care Analytics
- Supply-Chain Analytics
- Analytics for Government and Nonprofits
- Sports Analytics
- Web Analytics
- Summary
- Glossary
- Chapter 2: Descriptive Statistics
- Analytics in Action: U.S. Census Bureau
- 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 Modifying 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
- 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
- Box Plots
- 2.8 Measures of Association Between Two Variables
- Scatter Charts
- Covariance
- Correlation Coefficient
- Summary
- Glossary
- Problems
- Case Problem: Heavenly Chocolates Web Site Transactions
- Appendix 2.1 Creating Box Plots with XLMiner
- Chapter 3: Data Visualization
- Analytics in Action: Cincinnati Zoo & Botanical Garden
- 3.1 Overview of Data Visualization
- Effective Design Techniques
- 3.2 Tables
- Table Design Principles
- Crosstabulation
- PivotTables in Excel
- Recommended 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
- Bubble Charts
- Heat Maps
- Additional Charts for Multiple Variables
- PivotCharts in Excel
- 3.4 Advanced Data Visualization
- Advanced Charts
- Geographic Information Systems Charts
- 3.5 Data Dashboards
- Principles of Effective Data Dashboards
- Applications of Data Dashboards
- Summary
- Glossary
- Problems
- Case Problem: All-Time Movie Box-Office Data
- Appendix 3.1 Creating a Scatter-Chart Matrix and a Parallel-Coordinates Plot with XLMiner
- Chapter 4: Descriptive Data Mining
- Analytics in Action: Advice from a Machine
- 4.1 Data Preparation
- Treatment of Missing Data
- Identification of Outliers and Erroneous Data
- Variable Representation
- 4.2 Cluster Analysis
- Measuring Similarity Between Observations
- Hierarchical Clustering
- k-Means Clustering
- Hierarchical Clustering Versus k-Means Clustering
- 4.3 Association Rules
- Evaluating Association Rules
- Summary
- Glossary
- Problems
- Case Problem: Know Thy Customer
- Appendix 4.1 Hierarchical Clustering with XLMiner
- Appendix 4.2 k-Means Clustering with XLMiner
- Appendix 4.3 Association Rules with XLMiner
- Chapter 5: Probability: An Introduction to Modeling Uncertainty
- Analytics in Action: National Aeronautics and Space Administration
- 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: Hamilton County Judges
- Chapter 6: Statistical Inference
- Analytics in Action: John Morrell & Company
- 6.1 Selecting a Sample
- Sampling from a Finite Population
- Sampling from an Infinite Population
- 6.2 Point Estimation
- Practical Advice
- 6.3 Sampling Distributions
- Sampling Distribution of x
- Sampling Distribution of p
- 6.4 Interval Estimation
- Interval Estimation of the Population Mean
- Interval Estimation of the Population Proportion
- 6.5 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
- Big Data, Statistical Inference, and Practical Significance
- Summary
- Glossary
- Problems
- Case Problem 1: Young Professional Magazine
- Case Problem 2: Quality Associates, Inc
- Chapter 7: Linear Regression
- Analytics in Action: Alliance Data Systems
- 7.1 Simple Linear Regression Model
- Regression Model
- Estimated Regression Equation
- 7.2 Least Squares Method
- Least Squares Estimates of the Regression Parameters
- Using Excel’s Chart Tools to Compute the Estimated Regression Equation
- 7.3 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
- 7.4 The Multiple Regression Model
- Regression Model
- Estimated Multiple Regression Equation
- Least Squares Method and Multiple Regression
- Butler Trucking Company and Multiple Regression
- Using Excel’s Regression Tool to Develop the Estimated Multiple Regression Equation
- 7.5 Inference and Regression
- Conditions Necessary for Valid Inference in the Least Squares Regression Model
- Testing Individual Regression Parameters
- Addressing Nonsignificant Independent Variables
- Multicollinearity
- Inference and Very Large Samples
- 7.6 Categorical Independent Variables
- Butler Trucking Company and Rush Hour
- Interpreting the Parameters
- More Complex Categorical Variables
- 7.7 Modeling Nonlinear Relationships
- Quadratic Regression Models
- Piecewise Linear Regression Models
- Interaction Between Independent Variables
- 7.8 Model Fitting
- Variable Selection Procedures
- Overfitting
- Summary
- Glossary
- Problems
- Case Problem: Alumni Giving
- Appendix 7.1 Regression with XLMiner
- Chapter 8: Time Series Analysis and Forecasting
- Analytics in Action: ACCO Brands
- 8.1 Time Series Patterns
- Horizontal Pattern
- Trend Pattern
- Seasonal Pattern
- Trend and Seasonal Pattern
- Cyclical Pattern
- Identifying Time Series Patterns
- 8.2 Forecast Accuracy
- 8.3 Moving Averages and Exponential Smoothing
- Moving Averages
- Forecast Accuracy
- Exponential Smoothing
- Forecast Accuracy
- 8.4 Using Regression Analysis for Forecasting
- Linear Trend Projection
- Seasonality
- Seasonality Without Trend
- Seasonality with Trend
- Using Regression Analysis as a Causal Forecasting Method
- Combining Causal Variables with Trend and Seasonality Effects
- Considerations in Using Regression in Forecasting
- 8.5 Determining the Best Forecasting Model to Use
- Summary
- Glossary
- Problems
- Case Problem: Forecasting Food and Beverage Sales
- Appendix 8.1 Using Excel Forecast Sheet
- Appendix 8.2 Forecasting with XLMiner
- Chapter 9: Predictive Data Mining
- Analytics in Action: Orbitz
- 9.1 Data Sampling
- 9.2 Data Partitioning
- 9.3 Accuracy Measures
- Evaluating the Classification of Categorical Outcomes
- Evaluating the Estimation of Continuous Outcomes
- 9.4 Logistic Regression
- 9.5 k-Nearest Neighbors
- Classifying Categorical Outcomes with k-Nearest Neighbors
- Estimating Continuous Outcomes with k-Nearest Neighbors
- 9.6 Classification and Regression Trees
- Classifying Categorical Outcomes with a Classification Tree
- Estimating Continuous Outcomes with a Regression Tree
- Ensemble Methods
- Summary
- Glossary
- Problems
- Case Problem: Grey Code Corporation
- Appendix 9.1 Data Partitioning with XLMiner
- Appendix 9.2 Logistic Regression Classification with XLMiner
- Appendix 9.3 k-Nearest Neighbor Classification and Estimation with XLMiner
- Appendix 9.4 Single Classification and Regression Trees with XLMiner
- Appendix 9.5 Random Forests of Classification or Regression Trees with XLMiner
- Chapter 10: Spreadsheet Models
- Analytics in Action: Procter & Gamble
- 10.1 Building Good Spreadsheet Models
- Influence Diagrams
- Building a Mathematical Model
- Spreadsheet Design and Implementing the Model in a Spreadsheet
- 10.2 What-If Analysis
- Data Tables
- Goal Seek
- 10.3 Some Useful Excel Functions for Modeling
- SUM and SUMPRODUCT
- IF and COUNTIF
- VLOOKUP
- 10.4 Auditing Spreadsheet Models
- Trace Precedents and Dependents
- Show Formulas
- Evaluate Formulas
- Error Checking
- Watch Window
- Summary
- Glossary
- Problems
- Case Problem: Retirement Plan
- Chapter 11: Linear Optimization Models
- Analytics in Action: MeadWestvaco Corporation
- 11.1 A Simple Maximization Problem
- Problem Formulation
- Mathematical Model for the Par, Inc. Problem
- 11.2 Solving the Par, Inc. Problem
- The Geometry of the Par, Inc. Problem
- Solving Linear Programs with Excel Solver
- 11.3 A Simple Minimization Problem
- Problem Formulation
- Solution for the M&D Chemicals Problem
- 11.4 Special Cases of Linear Program Outcomes
- Alternative Optimal Solutions
- Infeasibility
- Unbounded
- 11.5 Sensitivity Analysis
- Interpreting Excel Solver Sensitivity Report
- 11.6 General Linear Programming Notation and More Examples
- Investment Portfolio Selection
- Transportation Planning
- Advertising Campaign Planning
- 11.7 Generating an Alternative Optimal Solution for a Linear Program
- Summary
- Glossary
- Problems
- Case Problem: Investment Strategy
- Appendix 11.1 Solving Linear Optimization Models Using Analytic Solver Platform
- Chapter 12: Integer Linear Optimization Models
- Analytics in Action: Petrobras
- 12.1 Types of Integer Linear Optimization Models
- 12.2 Eastborne Realty, An Example of Integer Optimization
- The Geometry of Linear All-Integer Optimization
- 12.3 Solving Integer Optimization Problems with Excel Solver
- A Cautionary Note About Sensitivity Analysis
- 12.4 Applications Involving Binary Variables
- Capital Budgeting
- Fixed Cost
- Bank Location
- Product Design and Market Share Optimization
- 12.5 Modeling Flexibility Provided by Binary Variables
- Multiple-Choice and Mutually Exclusive Constraints
- k Out of n Alternatives Constraint
- Conditional and Corequisite Constraints
- 12.6 Generating Alternatives in Binary Optimization
- Summary
- Glossary
- Problems
- Case Problem: Applecore Children’s Clothing
- Appendix 12.1 Solving Integer Linear Optimization Problems Using Analytic Solver Platform
- Chapter 13 Nonlinear Optimization Models
- Analytics in Action: Intercontinental Hotels
- 13.1 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
- 13.2 Local and Global Optima
- Overcoming Local Optima with Excel Solver
- 13.3 A Location Problem
- 13.4 Markowitz Portfolio Model
- 13.5 Forecasting Adoption of a New Product
- Summary
- Glossary
- Problems
- Case Problem: Portfolio Optimization with Transaction Costs
- Appendix 13.1 Solving Nonlinear Optimization Problems with Analytic Solver Platform
- Chapter 14: Monte Carlo Simulation
- Analytics in Action: Cook County Hospital ICU
- 14.1 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
- 14.2 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
- 14.3 Simulation Considerations
- Verification and Validation
- Advantages and Disadvantages of Using Simulation
- Summary
- Glossary
- Problems
- Case Problem: Four Corners
- Appendix 14.1 Land Shark Inc. Simulation with Analytic Solver Platform
- Appendix 14.2 Distribution Fitting with Analytic Solver Platform
- Appendix 14.3 Simulation Optimization with Analytic Solver Platform
- Appendix 14.4 Correlating Random Variables with Analytic Solver Platform
- Appendix 14.5 Probability Distributions for Random Variables
- Chapter 15: Decision Analysis
- Analytics in Action: Phytopharm
- 15.1 Problem Formulation
- Payoff Tables
- Decision Trees
- 15.2 Decision Analysis Without Probabilities
- Optimistic Approach
- Conservative Approach
- Minimax Regret Approach
- 15.3 Decision Analysis with Probabilities
- Expected Value Approach
- Risk Analysis
- Sensitivity Analysis
- 15.4 Decision Analysis with Sample Information
- Expected Value of Sample Information
- Expected Value of Perfect Information
- 15.5 Computing Branch Probabilities with Bayes’ Theorem
- 15.6 Utility Theory
- Utility and Decision Analysis
- Utility Functions
- Exponential Utility Function
- Summary
- Glossary
- Problems
- Case Problem: Property Purchase Strategy
- Appendix 15.1 Using Analytic Solver Platform to Create Decision Trees
- APPENDIX A: Basics of Excel
- APPENDIX B: Database Basics with Microsoft Access
- References
- Index
