Business analytics : descriptive, predictive, prescriptive

Develop the analytical skills that are in high demand in businesses today with Camm/Cochran/Fry/Ohlmann's best-selling BUSINESS ANALYTICS, 4E. You master the full range of analytics as you strengthen descriptive, predictive and prescriptive analytic skills. Real examples and memorable visuals illustrate data and results for each topic. Step-by-step instructions guide you through using Microsoft® Excel, Tableau, R, and JMP Pro software to perform even advanced analytics concepts. Practical, relevant problems at all levels of difficulty further help you apply what you've learned. This edition assists you in becoming proficient in topics beyond the traditional quantitative concepts, such as data visualization and data mining, which are increasingly important in today's analytical problem solving. MindTap digital learning resources with an interactive eBook, algorithmic practice problems with solutions and Exploring Analytics visualizations strengthen your understanding of key concepts. Cover Brief Contents Contents Preface Chapter 1: Introduction 1.1 Decision Making 1.2 Business Analytics Defined 1.3 A Categorization of Analytical Methods and Models 1.4 Big Data 1.5 Business Analytics in Practice 1.6 Legal and Ethical Issues in the Use of Data and Analytics Summary Glossary Chapter 2: Descriptive Statistics 2.1 Overview of Using Data: Definitions and Goals 2.2 Types of Data 2.3 Modifying 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 2.9 Data Cleansing Summary Glossary Problems Case Problem 1: Heavenly Chocolates Web Site Transactions Case Problem 2: African Elephant Populations Chapter 3: Data Visualization 3.1: Overview of Data Visualization 3.2: Tables 3.3: Charts 3.4: Advanced Data Visualization 3.5: Data Dashboards Summary Glossary Problems Case Problem 1: Pelican stores Case Problem 2: Movie Theater Releases Appendix: Data Visualization in Tableau Chapter 4: P robability: An Introduction to Modeling Uncertainty 4.1 Events and Probabilities 4.2 Some Basic Relationships of Probability 4.3 Conditional Probability 4.4 Random Variables 4.5 Discrete Probability Distributions 4.6 Continuous Probability Distributions Summary Glossary Problems Case Problem 1: Hamilton County Judges Case Problem 2: McNeil’s Auto Mall Case Problem 3: Gebhardt Electronics Chapter 5: Descriptive Data Mining 5.1 Cluster Analysis 5.2 Association Rules 5.3 Text Mining Summary Glossary Problems Case Problem 1: Big Ten Expansion Case Problem 2: Know Thy Customer Chapter 6: Statistical Inference 6.1 Selecting a Sample 6.2 Point Estimation 6.3 Sampling Distributions 6.4 Interval Estimation 6.5 Hypothesis Tests 6.6 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 7.1 Simple Linear Regression Model 7.2 Least Squares Method 7.3 Assessing the Fit of the Simple Linear Regression Model 7.4 The Multiple Regression Model 7.5 Inference and Regression 7.6 Categorical Independent Variables 7.7 Modeling Nonlinear Relationships 7.8 Model Fitting 7.9 Big Data and Regression 7.10 Prediction with Regression Summary Glossary Problems Case Problem 1: Alumni Giving Case Problem 2: Consumer Research, Inc. Case Problem 3: Predicting Winnings for NASCAR Drivers Chapter 8: Time Series Analysis and Forecasting 8.1 Time Series Patterns 8.2 Forecast Accuracy 8.3 Moving Averages and Exponential Smoothing 8.4 Using Regression Analysis for Forecasting 8.5 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: Using the Excel Forecast Sheet Chapter 9: Predictive Data Mining 9.1 Data Sampling, Preparation, and Partitioning 9.2 Performance Measures 9.3 Logistic Regression 9.4 k-Nearest Neighbors 9.5 Classification and Regression Trees Summary Glossary Problems Case Problem: Grey Code Corporation Chapter 10: Spreadsheet Models 10.1 Building Good Spreadsheet Models 10.2 What-If Analysis 10.3 Some Useful Excel Functions for Modeling 10.4 Auditing Spreadsheet Models 10.5 Predictive and Prescriptive Spreadsheet Models Summary Glossary Problems Case Problem: Retirement Plan Chapter 11: Monte Carlo Simulation 11.1 Risk Analysis for Sanotronics LLC 11.2 Inventory Policy Analysis for Promus Corp 11.3 Simulation Modeling for Land Shark Inc. 11.4 Simulation with Dependent Random Variables 11.5 Simulation Considerations Summary Glossary Problems Case Problem: Four Corners Appendix: Common Probability Distributions for Simulation Chapter 12: Linear Optimization Models 12.1 A Simple Maximization Problem 12.2 Solving the Par, Inc. Problem 12.3 A Simple Minimization Problem 12.4 Special Cases of Linear Program Outcomes 12.5 Sensitivity Analysis 12.6 General Linear Programming Notation and More Examples 12.7 Generating an Alternative Optimal Solution for a Linear Program Summary Glossary Problems Case Problem: Investment Strategy Chapter 13: Integer Linear Optimization Models 13.1 Types of Integer Linear Optimization Models 13.2 Eastborne Realty, an Example of Integer Optimization 13.3 Solving Integer Optimization Problems with Excel Solver 13.4 Applications Involving Binary Variables 13.5 Modeling Flexibility Provided by Binary Variables 13.6 Generating Alternatives in Binary Optimization Summary Glossary Problems Case Problem: Applecore Children’s Clothing Chapter 14: Nonlinear Optimization Models 14.1 A Production Application: Par, Inc. Revisited 14.2 Local and Global Optima 14.3 A Location Problem 14.4 Markowitz Portfolio Model 14.5 Adoption of a New Product: The Bass Forecasting Model Summary Glossary Problems Case Problem: Portfolio Optimization with Transaction Costs Chapter 15: Decision Analysis 15.1 Problem Formulation 15.2 Decision Analysis Without Probabilities 15.3 Decision Analysis with Probabilities 15.4 Decision Analysis with Sample Information 15.5 Computing Branch Probabilities with Bayes’ Theorem 15.6 Utility Theory Summary Glossary Problems Case Problem: Property Purchase Strategy Multi-Chapter Case Problems Appendix A: Basics of Excel Appendix B Database Basics with Microsoft Access References Index