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Essentials of Business Analytics by Jeffrey D. Camm EBOOK PDF Instant Download

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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