Business analytics data analysis and decision making 5th by wayne l winston chapter 01

part.

© 2015 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in

Business Analytics:

Data Analysis and

Chapter

Decision Making

1

Introduction to Data Analysis and Decision Making


Introduction
(slide 1 of 2)

 Living in the age of technology has implications for everyone entering
the business world.

 Technology makes it possible to collect huge amounts of data.
 Technology has given more people the power and responsibility to analyze
data and make decisions.

 A large amount of data already exists and will only increase in the
future.

© 2015 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.

Introduction
(slide 2 of 2)

 One of the hottest topics in today’s business world is business
analytics.
 This term encompasses all of the types of analysis discussed in this book.
 It also typically implies the analysis of very large data sets.
 By using quantitative methods to uncover the information in these
data sets and then acting on this information, companies are able to
gain a competitive advantage.

© 2015 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.


The Methods
(slide 1 of 2)

 This book combines topics from two separate fields: statistics and
management science.
 Statistics is the study of data analysis.
 Management science is the study of model building, optimization, and
decision making.

 Three important themes run through this book:
 Data analysis—includes data description, data inference, and the search for
relationships in data.

 Decision making—includes optimization techniques for problems with no

uncertainty, decision analysis for problems with uncertainty, and structured
sensitivity analysis.

 Dealing with uncertainty—includes measuring uncertainty and modeling
uncertainty explicitly.

© 2015 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.


The Methods
(slide 2 of 2)

 The figure below shows where these themes and subthemes are discussed
in the book.

© 2015 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.


The Software
(slide 1 of 3)

 The software included in new copies of this book, together with
Microsoft Excel®, provides a powerful combination that can be used to
analyze a wide variety of business problems.
 Excel—the most heavily used spreadsheet package on the market
 The file excel_tutorial.xlsm explains many of the features of Excel.
 Solver Add-in—uses powerful algorithms to perform spreadsheet
optimization.

 SolverTable Add-in—shows how the optimal solution changes when certain

inputs change.

© 2015 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.


The Software
(slide 2 of 3)

 DecisionTools® Suite—Excel add-ins, including:
 @RISK—can run multiple replications of a spreadsheet simulation, perform a
sensitivity analysis, and generate random numbers from a variety of probability
distributions.



RISKOptimizer combines optimization with simulation.

 StatTools—generates statistical output quickly in an easily interpretable form.
 PrecisionTree—used to analyze decisions with uncertainty.
 NeuralTools—mimics the working of the human brain to find “neural networks”
that quantify complex nonlinear relationships.

© 2015 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.


The Software
(slide 3 of 3)

 The figure below illustrates how these add-ins are used throughout the
book.

© 2015 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.


Modeling and Models
 A model is an abstraction of a real problem that tries to capture the
essence and key features of the problem.
 There are different types of models, and each can be a valuable aid in
solving a real problem:
 Graphical models
 Algebraic models
 Spreadsheet models

© 2015 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.


Graphical Models
 Graphical models attempt to portray graphically how different
elements of a problem are related—what effects what.

 A very simple graphical model, called an influence diagram, is shown
below.

© 2015 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.


Algebraic Models
 Algebraic models use algebraic equations and inequalities to specify a
set of relationships in a very precise way.

 A typical example is the “product mix” model shown below.

© 2015 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.


Spreadsheet Models
(slide 1 of 2)

 Spreadsheet modeling is an alternative to algebraic modeling that
relates various quantities in a spreadsheet with cell formulas.

 Instant feedback is available from spreadsheets, so if a formula is entered
incorrectly, it is often immediately obvious.

 Developing good spreadsheet models is not easy.
 They must be correct, well designed and well documented.

© 2015 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.


Spreadsheet Models
(slide 2 of 2)

 A spreadsheet model for a specific example of the product mix problem is
shown below.

© 2015 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.


A Seven-Step Modeling Process

 This book portrays modeling as a seven-step process, but not all
problems require all seven steps.
1. Define the problem.
2. Collect and summarize data.
3. Develop a model.
4. Verify the model.
5. Select one or more suitable decisions.
6. Present the results to the organization.
7. Implement the model and update it over time

© 2015 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.