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eBook for Essentials of Business Analytics

Author(s): Jeffrey D. Camm | James J Cochran | Michael J. Fry | Jeffrey W. Ohlmann | David R. Anderson

ISBN: 9781337019019

2nd Edition

Copyright: 2017

India Release: 2020

₹850

Binding: eBook

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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

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