ESSENTIALS OF BUSINESS ANALYTICS, 2e provides coverage over the full range of analytics--descriptive, predictive, and prescriptive--not covered by any other single book. It 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. Extensive solutions to problems help instructors master material and grade student assignments.
*Special prices for countries of South-Asia
- DATAfiles and MODELfiles: All data sets used as examples and in student exercises are also provided online as files available for download by the student. DATAfiles are Excel files that contain data needed for the examples and problems given in the textbook. MODELfiles contain additional modeling features such as extensive use of Excel formulas or the use of Excel Solver or Analytic Solver Platform.
- Excel is completely integrated throughout the book, so students learn the latest methods for solving practical problems. It includes step-by-step instructions to help students learn how to use Excel 2016 to apply material in the book. It also includes by-hand calculation approaches to convey insights when this is appropriate.
- First Mindtap for Business Analytics. MindTap is a customizable digital course solution that includes an interactive eBook, autograded exercises from the textbook, and author-created video walkthroughs of key chapter concepts and select examples that use Analytic Solver platform. Students can complete assignments whenever and wherever they are ready to learn with course material specially customized for students by you streamlined in one proven, easy-to-use interface. MindTap gives students a roadmap to master decision-making in business analytics. With an array of resources, tools, and apps -- including videos, practice opportunities, note taking, and flashcards.
Chapter 1 Introduction
Chapter 2 Descriptive Statistics
Chapter 3 Data Visualization
Chapter 4 Descriptive Data Mining
Chapter 5 Probability: An Introduction to Modeling Uncertainty
Chapter 6 Statistical Inference
Chapter 7 Linear Regression
Chapter 8 Time Series Analysis and Forecasting
Chapter 9 Predictive Data Mining
Chapter 10 Spreadsheet Models
Chapter 11 Linear Optimization Models
Chapter 12 Integer Linear Optimization Models
Chapter 13 Nonlinear Optimization Models
Chapter 14 Monte Carlo Simulation
Chapter 15 Decision Analysis
Jeffrey D. Camm, Wake Forest University
James J. Cochran, University of Alabama
Jeffrey W. Ohlmann, University of Iowa
David R. Anderson, University of Cincinnati
Dennis J. Sweeney, University of Cincinnati
Thomas A. Williams, Rochester Institute of Technology