Business analytics : methods, models and decisions / James R. Evans
By: Evans, James R. (James Robert) [author]
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Material type: 
BookSeries: Always Learning: Publisher: Essex : Pearson Education, [2016]Copyright date: ©2017Edition: Second edition; Global edition.Description: 652 pages : color illustrations, graphs, tables ; 26 cm.Content type: text Media type: unmediated Carrier type: volumeISBN: 9781292095448 (paperback); 129209544X (paperback).Subject(s): Business intelligence | Business planning | Strategic planning | Industrial management -- Statistical methodsDDC classification: 658.401 | Item type | Current library | Call number | Copy number | Status | Barcode | |
|---|---|---|---|---|---|---|
| General lending | MTU Bishopstown Library Desk Loan | 658.401 (Browse shelf(Opens below)) | 1 | Available | 00162459 | |
| 3 day loan | MTU Bishopstown Library Desk Loan | 658.401 (Browse shelf(Opens below)) | 1 | Available | 00162460 |
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Enhanced descriptions from Syndetics:
For undergraduate or graduate business students.
A balanced and holistic approach to business analytics
Business Analytics, 2nd Edition teaches the fundamental concepts of the emerging field of business analytics and provides vital tools in understanding how data analysis works in today's organisations. Students will learn to apply basic business analytics principles, communicate with analytics professionals, and effectively use and interpret analytic models to make better business decisions. Included access to commercial grade analytics software gives students real-world experience and career-focused value. Author James Evans takes a balanced, holistic approach and looks at business analytics from descriptive, and predictive perspectives.
Includes index.
Part 1: Foundations of business analytics -- Introduction to business analytics -- Analytics on spreadsheets -- Part 2: Descriptive analytics -- Visualizing and exploring data -- Descriptive statistical measures -- Probability distributions and data modeling -- Sampling and estimation -- Statistical inference -- Part 3: Predictive Analytics -- Trendlines and regression analysis -- Forecasting techniques -- Introduction to data mining -- Spreadsheet modeling and analysis -- Monte Carlo simulation and risk analysis -- Part 4: Prescriptive analytics -- Linear optimization -- Applications of linear optimization -- Integer optimization -- Decision analysis.