Essentials of business analytics / Jeffrey D. Camm [and six others].

Machine generated contents note: ch. 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 --
Analytics in Action: Procter & Gamble Uses Business Analytics to Redesign its Supply Chain --
1.4.Big Data --
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 --
ch. 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: Heavenly Chocolates Web Site Transactions --
Appendix: Creating Box Plots in XLMiner --
ch. 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 --
3.3.Charts --
Scatter Charts --
Line Charts --
Bar Charts and Column Charts --
A Note on Pie Charts and 3-D 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: Creating a Scatter Chart Matrix and a Parallel Coordinates Plot with XLMiner --
ch. 4 Linear Regression --
Analytics in Action: Alliance Data Systems --
4.1.The Simple Linear Regression Model --
Regression Model and Regression Equation --
Estimated Regression Equation --
4.2.Least Squares Method --
Least Squares Estimates of the Regression Parameters --
Using Excel's Chart Tools to Compute the Estimated Regression Equation --
4.3.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 --
4.4.The Multiple Regression Model --
Regression Model and Regression Equation --
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 --
4.5.Inference and Regression --
Conditions Necessary for Valid Inference in the Least Squares Regression Model --
Testing for an Overall Regression Relationship --
Testing Individual Regression Parameters --
Addressing Nonsignificant Independent Variables --
Multicollinearity --
Inference and Very Large Samples --
4.6.Categorical Independent Variables --
Butler Trucking Company and Rush Hour --
Interpreting the Parameters --
More Complex Categorical Variables --
4.7.Modeling Nonlinear Relationships --
Quadratic Regression Models --
Piecewise Linear Regression Models --
Interaction Between Independent Variables --
4.8.Model Fitting --
Variable Selection Procedures --
Overfitting --
Summary --
Glossary --
Problems --
Case Problem: Alumni Giving --
Appendix: Using XLMiner for Regression --
ch. 5 Time Series Analysis and Forecasting --
Analytics in Action: Forecasting Demand for a Broad Line of Office Products --
5.1.Time Series Patterns --
Horizontal Pattern --
Trend Pattern --
Seasonal Pattern --
Trend and Seasonal Pattern --
Cyclical Pattern --
Identifying Time Series Patterns --
5.2.Forecast Accuracy --
5.3.Moving Averages and Exponential Smoothing --
Moving Averages --
Forecast Accuracy --
Exponential Smoothing --
Forecast Accuracy --
5.4.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 --
5.5.Determining the Best Forecasting Model to Use --
Summary --
Glossary --
Problems --
Case Problem: Forecasting Food and Beverage Sales --
Appendix: Using XLMiner for Forecasting --
ch. 6 Data Mining --
Analytics in Action: Online Retailers Using Predictive Analytics to Cater to Customers --
6.1.Data Sampling --
6.2.Data Preparation --
Treatment of Missing Data --
Identification of Outliers and Erroneous Data --
Variable Representation --
6.3.Unsupervised Learning --
Cluster Analysis --
Association Rules --
6.4.Supervised Learning --
Partitioning Data --
Classification Accuracy --
Prediction Accuracy --
k-Nearest Neighbors --
Classification and Regression Trees --
Logistic Regression --
Summary --
Glossary --
Problems --
Case Problem: Grey Code Corporation --
ch. 7 Spreadsheet Models --
Analytics in Action: Procter and Gamble Sets Inventory Targets Using Spreadsheet Models --
7.1.Building Good Spreadsheet Models --
Influence Diagrams --
Building a Mathematical Model --
Spreadsheet Design and Implementing the Model in a Spreadsheet --
7.2.What-If Analysis --
Data Tables --
Goal Seek --
7.3.Some Useful Excel Functions for Modeling --
Sum and Sumproduct --
If and Countif --
Vlookup --
7.4.Auditing Spreadsheet Models --
Trace Precedents and Dependents --
Show Formulas --
Evaluate Formulas --
Error Checking --
Watch Window --
Summary --
Glossary --
Problems --
Case Problem: Retirement Plan --
ch. 8 Linear Optimization Models --
Analytics in Action: Timber Harvesting Model at MeadWestvaco Corporation --
8.1.A Simple Maximization Problem --
Problem Formulation --
Mathematical Model for the Par, Inc. Problem --
8.2.Solving the Par, Inc. Problem --
The Geometry of the Par, Inc. Problem --
Solving Linear Programs with Excel Solver --
8.3.A Simple Minimization Problem --
Problem Formulation --
Solution for the M&D Chemicals Problem --
8.4.Special Cases of Linear Program Outcomes --
Alternative Optimal Solutions --
Infeasibility --
Unbounded --
8.5.Sensitivity Analysis --
Interpreting Excel Solver Sensitivity Report --
8.6.General Linear Programming Notation and More Examples --
Investment Portfolio Selection --
Transportation Planning --
Advertising Campaign Planning --
8.7.Generating an Alternative Optimal Solution for a Linear Program --
Summary --
Glossary --
Problems --
Case Problem: Investment Strategy --
Appendix: Solving Linear Optimization Models Using Analytic Solver Platform --
ch. 9 Integer Linear Optimization Models --
Analytics in Action: Optimizing the Transport of Oil Rig Crews --
9.1.Types of Integer Linear Optimization Models --
9.2.Eastborne Realty, An Example of Integer Optimization --
The Geometry of Linear All-Integer Optimization --
9.3.Solving Integer Optimization Problems with Excel Solver --
A Cautionary Note About Sensitivity Analysis --
9.4.Applications Involving Binary Variables --
Capital Budgeting --
Fixed Cost --
Bank Location --
Product Design and Market Share Optimization --
9.5.Modeling Flexibility Provided by Binary Variables --
Multiple-Choice and Mutually Exclusive Constraints --
k out of n Alternatives Constraint --
Conditional and Corequisite Constraints --
9.6.Generating Alternatives in Binary Optimization --
Summary --
Glossary --
Problems --
Case Problem: Applecore Children's Clothing --
Appendix: Solving Integer Linear Optimization Problems Using Analytic Solver Platform --
ch. 10 Nonlinear Optimization Models --
Analytics in Action: Intercontinental Hotels Optimizes Retail Pricing --
10.1.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 --
10.2.Local and Global Optima --
Overcoming Local Optima with Excel Solver --
10.3.A Location Problem --
10.4.Markowitz Portfolio Model --
10.5.Forecasting Adoption of a New Product --
Summary --
Glossary --
Problems --
Case Problem: Portfolio Optimization with Transaction Costs --
Appendix: Solving Nonlinear Optimization Problems with Analytic Solver Platform --
ch. 11 Monte Carlo Simulation --
Analytics in Action: Reducing Patient Infections in the ICU --
11.1.What-If Analysis --
The Sanotronics Problem --
Base-Case Scenario --
Worst-Case Scenario --
Best-Case Scenario --
11.2.Simulation Modeling with Native Excel Functions --
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 --
11.3.Simulation Modeling with Analytic Solver Platform --
The Land Shark Problem --
Spreadsheet Model for Land Shark --
Generating Values for Land Shark's Random Variables --
Tracking Output Measures for Land Shark --
Executing Simulation Trials and Analyzing Output for Land Shark --
The Zappos Problem --
Spreadsheet Model for Zappos Note continued: Modeling Random Variables for Zappos --
Tracking Output Measures for Zappos --
Executing Simulation Trials and Analyzing Output for Zappos --
11.4.Simulation Optimization --
11.5.Simulation Considerations --
Verification and Validation --
Advantages and Disadvantages of Using Simulation --
Summary --
Glossary --
Problems --
Case Problem: Four Corners --
Appendix 11.1 Incorporating Dependence Between Random Variables --
Appendix 11.2 Probability Distributions for Random Variables --
ch. 12 Decision Analysis --
Analytics in Action: Phytopharm's New Product Research and Development --
12.1.Problem Formulation --
Payoff Tables --
Decision Trees --
12.2.Decision Analysis Without Probabilities --
Optimistic Approach --
Conservative Approach --
Minimax Regret Approach --
12.3.Decision Analysis with Probabilities --
Expected Value Approach --
Risk Analysis --
Sensitivity Analysis --
12.4.Decision Analysis with Sample Information --
Expected Value of Sample Information --
Expected Value of Perfect Information --
12.5.Computing Branch Probabilities with Bayes' Theorem --
12.6.Utility Theory --
Utility and Decision Analysis --
Utility Functions --
Exponential Utility Function --
Summary --
Glossary --
Problems --
Case Problem: Property Purchase Strategy --
Appendix: Using Analytic Solver Platform to Create Decision Trees.

Provides coverage over the range of analytics - descriptive, predictive, prescriptive - not covered by any other single book, and includes step-by-step instructions to help students learn how to use Excel and powerful but easy to use Excel add-ons such as XL Miner for data mining and Analytic Solver Platform for optimization and simulation.

600-699