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Practical Management Science

Wayne L. Winston
Kelley School of Business, Indiana University

S. Christian Albright
Kelley School of Business, Indiana University

6th
Edition

Australia ● Brazil ● Mexico ● Singapore ● United Kingdom ● United States

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Practical Management Science,
Sixth Edition

Wayne L. Winston,
S. Christian Albright

Senior Vice President: Erin Joyner

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To Mary, my wonderful wife, best friend, and constant companion
And to our Welsh Corgi, Bryn, who still just wants to play ball    S.C.A.

To my wonderful family
Vivian, Jennifer, and Gregory    W.L.W.

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Copyright 2019 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.

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S. Christian Albright got his B.S. degree in Mathematics from
Stanford in 1968 and his Ph.D. degree in Operations Research
from Stanford in 1972. Until his retirement in 2011, he taught in
the Operations & Decision Technologies Department in the Kelley
School of Business at Indiana University. His teaching included
courses in management science, computer simulation, and statis-
tics to all levels of business students: undergraduates, MBAs, and
doctoral students. He has published over 20 articles in leading
operations research journals in the area of applied probability,
and he has authored several books, including Practical Manage-

ment Science, Data Analysis and Decision Making, Data Analysis for Managers, Spread-
sheet Modeling and Applications, and VBA for Modelers. He jointly developed StatTools,
a statistical add-in for Excel, with the Palisade Corporation. In “retirement,” he continues
to revise his books, and he has developed a commercial product, ExcelNow!, an extension
of the Excel tutorial that accompanies this book.
On the personal side, Chris has been married to his wonderful wife Mary for
46 years. They have a special family in Philadelphia: their son Sam, his wife Lindsay,
and their two sons, Teddy and Archer. Chris has many interests outside the academic
area. They include activities with his family (especially traveling with Mary), going to
cultural events, power walking, and reading. And although he earns his livelihood from
statistics and management science, his real passion is for playing classical music on the
piano.

Wayne L. Winston is Professor Emeritus of Decision
Sciences at the Kelley School of Business at Indiana University
and is now a Professor of Decision and Information Sciences
at the Bauer College at the University of Houston. Winston
received his B.S. degree in Mathematics from MIT and his
Ph.D. degree in Operations Research from Yale. He has written
the successful textbooks Operations Research: Applications
and Algorithms, Mathematical Programming: Applications
and Algorithms, Simulation Modeling with @RiSk, Practical
Management Science, Data Analysis for Managers, Spreadsheet

Modeling and Applications, Mathletics, Data Analysis and Business Modeling with
Excel 2013, Marketing Analytics, and Financial Models Using Simulation and
Optimization. Winston has published over 20 articles in leading journals and has won
more than 45 teaching awards, including the school-wide MBA award six times. His
current interest is in showing how spreadsheet models can be used to solve business
problems in all disciplines, particularly in finance, sports, and marketing.
Wayne enjoys swimming and basketball, and his passion for trivia won him an
appearance several years ago on the television game show Jeopardy, where he won two
games. He is married to the lovely and talented Vivian. They have two children, Gregory
and Jennifer.

About the Authors

06659_fm_ptg01_i-xvi.indd 6 11/09/17 7:33 PM

Copyright 2019 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.

Copyright 2019 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. WCN 02-200-203

vii

Preface  xiii

1 Introduction to Modeling 1

2 Introduction to Spreadsheet Modeling 19

3 Introduction to Optimization Modeling 71

4 Linear Programming Models 135

5 Network Models 219

6 Optimization Models with Integer Variables 277

7 Nonlinear Optimization Models 339

8 Evolutionary Solver: An Alternative Optimization Procedure 407

9 Decision Making under Uncertainty 457

10 Introduction to Simulation Modeling 515

11 Simulation Models 589

12 Queueing Models 667

13 Regression and Forecasting Models 715

14 Data Mining 771

References  809

Index  815

MindTap Chapters
15 Project Management 15-1

16 Multiobjective Decision Making 16-1

17 Inventory and Supply Chain Models 17-1

Brief Contents

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Copyright 2019 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.

Copyright 2019 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. WCN 02-200-203

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Copyright 2019 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.

Copyright 2019 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. WCN 02-200-203

ix

Preface xiii

CHAPTER 1 Introduction to Modeling 1
1.1 Introduction  3
1.2 A Capital Budgeting Example  3
1.3 Modeling versus Models  6
1.4 A Seven-Step Modeling Process  7
1.5 A Great Source for Management Science

Applications: Interfaces 13
1.6 Why Study Management Science? 13
1.7 Software Included with This Book  15
1.8 Conclusion  17

CHAPTER 2 Introduction to Spreadsheet
Modeling  19

2.1 Introduction 20
2.2 Basic Spreadsheet Modeling:

Concepts and Best  Practices 21
2.3 Cost Projections 25
2.4 Breakeven Analysis 31
2.5 Ordering with Quantity Discounts

and Demand Uncertainty 39
2.6 Estimating the Relationship between

Price and Demand 44
2.7 Decisions Involving the Time Value of

Money 54
2.8 Conclusion 59
Appendix Tips for Editing and

Documenting Spreadsheets 64
Case 2.1 Project Selection at Ewing Natural

Gas  66
Case 2.2 New Product Introduction at eTech  68

CHAPTER 3 Introduction to Optimization
Modeling 71

3.1 Introduction 72
3.2 Introduction to Optimization 73
3.3 A Two-Variable Product Mix Model 75

Contents

3.4 Sensitivity Analysis 87
3.5 Properties of Linear Models 97
3.6 Infeasibility and Unboundedness 100
3.7 A Larger Product Mix Model 103
3.8 A Multiperiod Production Model 111
3.9 A Comparison of Algebraic

and Spreadsheet Models 120
3.10 A Decision Support System 121
3.11 Conclusion 123
Appendix Information on Optimization Software 130
Case 3.1 Shelby Shelving 132

CHAPTER 4 Linear Programming Models 135
4.1 Introduction 136
4.2 Advertising Models 137
4.3 Employee Scheduling Models 147
4.4 Aggregate Planning Models 155
4.5 Blending Models 166
4.6 Production Process Models 174
4.7 Financial Models 179
4.8 Data Envelopment Analysis (DEA) 191
4.9 Conclusion 198
Case 4.1 Blending Aviation Gasoline at Jansen

Gas 214
Case 4.2 Delinquent Accounts at GE Capital 216
Case 4.3 Foreign Currency Trading 217

CHAPTER 5 Network Models 219
5.1 Introduction 220
5.2 Transportation Models 221
5.3 Assignment Models 233
5.4 Other Logistics Models 240
5.5 Shortest Path Models 249
5.6 Network Models in the Airline Industry 258
5.7 Conclusion 267
Case 5.1 Optimized Motor Carrier Selection at

Westvaco 274

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Copyright 2019 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.

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CHAPTER 9 Decision Making under
Uncertainty 457

9.1 Introduction 458
9.2 Elements of Decision Analysis 460
9.3 Single-Stage Decision Problems 467
9.4 The PrecisionTree Add-In 471
9.5 Multistage Decision Problems 474
9.6 The Role of Risk Aversion 492
9.7 Conclusion 499
Case 9.1 Jogger Shoe Company  510
Case 9.2 Westhouser Paper Company  511
Case 9.3 Electronic Timing System for

Olympics  512
Case 9.4 Developing a Helicopter Component

for the Army  513

CHAPTER 10 Introduction to Simulation
Modeling 515

10.1 Introduction 516
10.2 Probability Distributions for Input

Variables 518
10.3 Simulation and the Flaw of Averages 537
10.4 Simulation with Built-in Excel Tools 540
10.5 Introduction to @RISK 551
10.6 The Effects of Input Distributions on

Results 568
10.7 Conclusion 577
Appendix Learning More About @RISK 583
Case 10.1 Ski Iacket Production 584
Case 10.2 Ebony Bath Soap 585
Case 10.3 Advertising Effectiveness 586
Case 10.4 New Project Introduction at eTech 588

CHAPTER 11 Simulation Models 589
11.1 Introduction 591
11.2 Operations Models 591
11.3 Financial Models 607
11.4 Marketing Models 631
11.5 Simulating Games of Chance 646
11.6 Conclusion 652
Appendix Other Palisade Tools for Simulation 662

x Contents

CHAPTER 6 Optimization Models with Integer
Variables 277

6.1 Introduction 278
6.2 Overview of Optimization with Integer

Variables 279
6.3 Capital Budgeting Models 283
6.4 Fixed-Cost Models 290
6.5 Set-Covering and Location-Assignment

Models 303
6.6 Cutting Stock Models 320
6.7 Conclusion 324
Case 6.1 Giant Motor Company 334
Case 6.2 Selecting Telecommunication Carriers to

Obtain Volume Discounts 336
Case 6.3 Project Selection at Ewing Natural Gas 337

CHAPTER 7 Nonlinear Optimization Models 339
7.1 Introduction 340
7.2 Basic Ideas of Nonlinear Optimization 341
7.3 Pricing Models 347
7.4 Advertising Response and Selection Models 365
7.5 Facility Location Models 374
7.6 Models for Rating Sports Teams 378
7.7 Portfolio Optimization Models 384
7.8 Estimating the Beta of a Stock 394
7.9 Conclusion 398
Case 7.1 GMS Stock Hedging 405

CHAPTER 8 Evolutionary Solver: An Alternative
Optimization Procedure 407

8.1 Introduction 408
8.2 Introduction to Genetic Algorithms 411
8.3 Introduction to Evolutionary Solver 412
8.4 Nonlinear Pricing Models 417
8.5 Combinatorial Models 424
8.6 Fitting an S-Shaped Curve 435
8.7 Portfolio Optimization 439
8.8 Optimal Permutation Models 442
8.9 Conclusion 449
Case 8.1 Assigning MBA Students to Teams 454
Case 8.2 Project Selection at Ewing Natural Gas 455

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Copyright 2019 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.

Copyright 2019 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. WCN 02-200-203

Contents xi

Case 11.1 College Fund Investment  664
Case 11.2 Bond Investment Strategy  665
Case 11.3 Project Selection Ewing Natural Gas  666

CHAPTER 12 Queueing Models 667
12.1 Introduction 668
12.2 Elements of Queueing Models 670
12.3 The Exponential Distribution 673
12.4 Important Queueing Relationships 678
12.5 Analytic Steady-State Queueing Models 680
12.6 Queueing Simulation Models 699
12.7 Conclusion  709
Case 12.1 Catalog Company Phone Orders 713

CHAPTER 13 Regression and Forecasting Models 715
13.1 Introduction 716
13.2 Overview of Regression Models 717
13.3 Simple Regression Models 721
13.4 Multiple Regression Models 734
13.5 Overview of Time Series Models 745
13.6 Moving Averages Models 746
13.7 Exponential Smoothing Models 751
13.8 Conclusion 762
Case 13.1 Demand for French Bread at Howie’s

Bakery 768
Case 13.2 Forecasting Overhead at Wagner

Printers 769
Case 13.3 Arrivals at the Credit Union 770

CHAPTER 14 Data Mining 771
14.1 Introduction 772
14.2 Classification Methods 774
14.3 Clustering Methods 795
14.4 Conclusion 806
Case 14.1 Houston Area Survey 808

References  809

Index  815

MindTap Chapters

CHAPTER 15 Project Management 15-1
15.1 Introduction 15-2
15.2 The Basic CPM Model 15-4
15.3 Modeling Allocation of Resources 15-14
15.4 Models with Uncertain Activity Times 15-30
15.5 A Brief Look at Microsoft Project 15-35
15.6 Conclusion 15-39

CHAPTER 16 Multiobjective Decision Making 16-1
16.1 Introduction 16-2
16.2 Goal Programming 16-3
16.3 Pareto Optimality and Trade-Off Curves 16-12
16.4 The Analytic Hierarchy Process (AHP) 16-20
16.5 Conclusion 16-25

CHAPTER 17 Inventory and Supply Chain Models 17-1
17.1 Introduction 17-2
17.2 Categories of Inventory and Supply Chain

Models 17-3
17.3 Types of Costs in Inventory and Supply Chain

Models 17-5
17.4 Economic Order Quantity (EOQ) Models 17-6
17.5 Probabilistic Inventory Models 17-21
17.6 Ordering Simulation Models 17-34
17.7 Supply Chain Models 17-40
17.8 Conclusion 17-50
Case 17.1 Subway Token Hoarding 17-57

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Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.

Copyright 2019 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. WCN 02-200-203

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Copyright 2019 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.

Copyright 2019 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. WCN 02-200-203

xiii

Practical Management Science provides a spreadsheet-
based, example-driven approach to management
science. Our initial objective in writing the book was to
reverse negative attitudes about the course by making
the subject relevant to students. We intended to do this
by imparting valuable modeling skills that students can
appreciate and take with them into their careers. We are
very gratified by the success of previous editions.
The book has exceeded our initial objectives. We are
especially pleased to hear about the success of the book
at many other colleges and universities around the
world. The acceptance and excitement that has been
generated has motivated us to revise the book and make
the current edition even better.
When we wrote the first edition, management
science courses were regarded as irrelevant or
uninteresting to many business students, and the use of
spreadsheets in management science was in its early
stages of development. Much has changed since the
first edition was published in 1996, and we believe that
these changes are for the better. We have learned a lot
about the best practices of spreadsheet modeling for
clarity and communication. We have also developed
better ways of teaching the materials, and we
understand more about where students tend to
have difficulty with the concepts. Finally, we
have had the  opportunity to teach this material at
several Fortune 500 companies (including Eli Lilly,
PricewaterhouseCoopers, General Motors, Tomkins,
Microsoft, and Intel). These companies, through their
enthusiastic support, have further enhanced the
realism of the examples included in this book.
Our objective in writing the first edition was very
simple—we wanted to make management science
relevant and practical to students and professionals.
This book continues to distinguish itself in the market
in four fundamental ways:

■ Teach by Example. The best way to learn
modeling concepts is by working through
examples and solving an abundance of problems.
This active learning approach is not new, but our
text has more fully developed this approach than
any book in the field. The feedback we have
received from many of you has confirmed the
success of this pedagogical approach for
management science.

■ Integrate Modeling with Finance, Marketing,
and Operations Management. We integrate
modeling into all functional areas of business.
This is an important feature because the majority
of business students major in finance and
marketing. Almost all competing textbooks
emphasize operations management–related
examples. Although these examples are
important, and many are included in the book,
the application of modeling to problems in
finance and marketing is too important to ignore.
Throughout the book, we use real examples from
all functional areas of business to illustrate the
power of spreadsheet modeling to all of these
areas. At Indiana University, this led to the
development of two advanced MBA electives
in finance and marketing that built upon the
content in this book.

■ Teach Modeling, Not Just Models. Poor attitudes
among students in past management science
courses can be attributed to the way in which they
were taught: emphasis on algebraic formulations
and memorization of models. Students gain more
insight into the power of management science by
developing skills in modeling. Throughout the
book, we stress the logic associated with model
development, and we discuss solutions in this
context. Because real problems and real models
often include limitations or alternatives, we
include several “Modeling Issues” sections to
discuss these important matters. Finally, we
include “Modeling Problems” in most chapters to
help develop these skills.

■ Provide Numerous Problems and Cases.
Whereas all textbooks contain problem sets for
students to practice, we have carefully and
judiciously crafted the problems and cases
contained in this book. Each chapter contains
four types of problems: easier Level A Problems,
more difficult Level B Problems, Modeling
Problems, and Cases. Most of the problems
following sections of chapters ask students to
extend the examples in the preceding section.
The end-of-chapter problems then ask students
to explore new models. Selected solutions are
available to students through MindTap and are

Preface

06659_fm_ptg01_i-xvi.indd 13 11/09/17 7:33 PM

Copyright 2019 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.

Copyright 2019 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. WCN 02-200-203

xiv Preface

denoted by the second-color numbering of the
problem. Solutions for all of the problems and
cases are provided to adopting instructors. In
addition, shell files (templates) are available for
many of the problems for adopting instructors.
The shell files contain the basic structure of the
problem with the relevant formulas omitted. By
adding or omitting hints in individual solutions,
instructors can tailor these shell files to best meet
the specific needs of students.

New to the Sixth Edition

The immediate reason for the sixth edition was the
introduction of Excel 2016. Admittedly, this is not
really a game changer, but it does provide new features
that ought to be addressed. In addition, once we were
motivated by Excel 2016 to revise the book, we saw
the possibility for other changes that will hopefully
improve the book. Important changes to the sixth
edition include the following:

■ The book is now entirely geared to Excel 2016.
In particular, all screenshots are from this newest
version of Excel. However, the changes are not
dramatic, and users of Excel 2013, Excel 2010, and
even Excel 2007 should have no trouble following.
Also, the latest changes in the accompanying
@RISK, PrecisionTree, and StatTools add-ins
have been incorporated into the text.

■ Many of the problems (well over 100) have new
data. Even though these problems are basically
the same as before, the new data results in
different solutions. Similarly, the time series data
in several of the chapter examples have been
updated.

■ A new chapter on Data Mining has been added.
It covers classification problems (including a
section on neural networks) and clustering. To
keep the size of the physical book roughly the
same as before, the chapter on Inventory and
Supply Chain Models has been moved online as
Chapter 17.

■ Probably the single most important change is
that the book is now incorporated into Cengage’s
MindTap platf

Solve using formulas- excel problems-Quantitative Assignment

P696-10-Slow

Renting a copier
Inputs (for slow system)
Unit of time hour
Arrival rate 1 customers/hour
Service rate 2 customers/hour
Outputs
Direct outputs from inputs Distribution of number in system Distribution of time in queue
Mean time between arrivals 1.000 hours n (customers) P(n in system) t (in hours) P(wait > t)
Mean time per service 0.500 hours 4 0.031 2.000 0.068
Traffic intensity 0.500
Summary measures
Expected number in system 1.000 customers
Expected number in queue 0.500 customers
Expected time in system 1.000 hours
Expected time in queue 0.500 hours
Percentage who don’t wait in queue 50.0%
Cost analysis
Employee cost/hr
Rental cost/hour
Waiting cost/hour
Total Cost/hour

Problem 11.11

P696-10-Fast

Renting a copier
Inputs (for slow system)
Unit of time hour
Arrival rate 1 customers/hour
Service rate 2 customers/hour
Outputs
Direct outputs from inputs Distribution of number in system Distribution of time in queue
Mean time between arrivals 1.000 hours n (customers) P(n in system) t (in hours) P(wait > t)
Mean time per service 0.500 hours 4 0.031 2.000 0.068
Traffic intensity 0.500
Summary measures
Expected number in system 1.000 customers
Expected number in queue 0.500 customers
Expected time in system 1.000 hours
Expected time in queue 0.500 hours
Percentage who don’t wait in queue 50.0%
Cost analysis
Employee cost/hr
Rental cost/hour
Waiting cost/hour
Total Cost/hour

Problem 11.11

P764-32

Day Peak Load
Christopher J. Zappe, Ph.D.: In megawatts.
Daily High Temperature
Christopher J. Zappe, Ph.D.: In degrees F.

1 118.5 89
2 136.0 94
3 143.6 100
4 153.2 97
5 140.7 95
6 151.9 100
7 135.1 92
8 178.2 106
9 101.6 67
10 96.5 67
11 103.9 74
12 113.4 84
13 106.2 79
14 111.4 85
15 116.5 89
16 96.3 68
17 150.1 98
18 105.1 86
19 114.7 87
20 189.3 108
21 131.7 96
22 100.9 76
23 92.5 71
24 132.0 90
25 116.4 88

P764-35

Month Advertising Units Sold
1 $1,000 4,000,000
2 $2,000 4,800,000
3 $3,000 5,000,000
4 $20,000 7,500,000
5 $30,000 8,000,000
6 $50,000 9,000,000
7 $80,000 9,900,000
8 $100,000 10,200,000

Solve Using Formulas- Excel Problems-Quantitative Assignment

10-The Decision Sciences Department is trying to determine whether to rent a slow or a fast copier. The department believes that an employee’s time is worth $15 per hour. The slow copier rents for $4 per hour,
and it takes an employee an average of 10 minutes to complete copying. The fast copier rents for $15 per hour, and it takes an employee an average of six minutes to complete copying. On average, four employees per hour need to use the copying machine. (Assume the copying times and interarrival times to the copying machine
are exponentially distributed.) Which machine should the department rent to minimize expected total cost per hour?

32-A power company located in southern Alabama wants to predict the peak power load (i.e., the maximum amount of power that must be generated each day to meet demand) as a function of the daily high temperature (X). A random sample of 25 summer days is chosen, and the peak power load and the high temperature are recorded each day. The file P13_32.xlsx contains these observations.

  1. Create a scatterplot for these data. Comment on the observed relationship between Y and X.
  2. Estimate an appropriate regression equation to predict the peak power load for this power |company. Interpret the estimated regression coefficients.
  3. Analyze the estimated equation’s residuals.
    Do they suggest that the regression equation is adequate? If not, return to part b and revise your equation. Continue to revise the equation until the results are satisfactory.
  4. Use your final equation to predict the peak power load on a summer day with a high temperature of 100 degrees.

35-The file P13_35.xlsx contains the amount of money spent advertising a product (in thousands of dollars) and the number of units sold (in millions) for eight months. 

a. Assume that the only factor influencing monthly sales is advertising. Fit the following two curves to these data: linear (Y 5 a 1 bX) and power (Y 5 aXb). Which equation best fits the data?

b. Interpret the best-fitting equation.
c. Using the best-fitting equation, predict sales during a month in which $60,000 is spent on advertising.

Quantitative Assignment

  • Problem 10 – Page 696
  • Problem 32 – Page 764
  • Problem 35 – Page 764

Use of Excel in Forecasting Problems

Excel Analysis Tool Pack

The Excel Analysis Tool Pack is an Excel add-in that is typically included in the Excel screen tool bar after you select the DATA tab.  The icon will be titled: “Data Analysis” and is located on the right side of the tool bar. If the tool pack is not appearing, go to the Excel add-ins to add the tool pack to your spreadsheet toolbar. This tool pack contains a number of basic data analysis tools that you may be familiar with; such as: Correlation, Descriptive Statistics, Exponential Smoothing, Histograms, Moving Averages, and so on.

I recommended that you access the tool pack by selecting the “Data Analysis” icon and review the various analysis tools.  To get a detailed description of an analtyical tool, select the tool, select Help, and then scroll down the screen to select the function. For instance: Select “Data Analysis” icon à Select “Correlation” à Select “Help” à scroll to the later part of screen to select “Correlation” again à this will provide you with a definition of the function. 

Informational video for your review: https://www.youtube.com/watch?v=4lAvbp-yVs8

This informational video will illustrate the use of the Excel data analysis tool for various statistical functions, such as: mean, median, hypothesis, regression analysis.  The intent of this video is to ensure that you are aware of additional Excel analysis capabilities, how to access the Tool Pack and what some of the analytical capabilities are.

Hint: There are a number of interactive tutorials online…so, do not hesitate to search the web for additional information. A link for the Lynda tutorial site was provided earlier in the course.  However, here is access information if you have not use Lynda as yet: To start using Lynda, go to -> www.esc.edu/lynda   (you will need to put this link in your browser).  When prompted, log in with your ESC College credentials (email address & password).   

Creating Graphics and Including Analysis Results 

When working on the two forecasting problems in this Module, it is important that you demonstrate the correct use of Excel analytics by creating: Scattergrams, adding Trendlines, including Regression data / Regression Equation, identifying R-squared value and the use of linear, exponential, power curves for Best Fit  analysis, and on so.  Finally, graphics should be appropriately labeled (x and y axis).  All of these analysis functions can be found in the Excel tool bar after you insert the scattergram plot to your spreadsheet (based on the data that is provided). Once the basic graphic has been created, select the graphic and the tool bar will display the Chart tools: Design, Layout, Format.  Select “Layout” to obtain access to the analytical tools required to complete your chart. When asked to interpret data – use your graphic display and analytical results to facilitate the interpretation.  For instance:  Interpret the estimated regression coefficients?  Use the Regression Equation to identify the coefficients and then provide a brief discussion regarding the influence of the coeficients. Be sure it is a “Best Fit” solution. Check your R2 values. 

Video – Trend Lines and Regression Analysis in Excel (12 min):

https://www.youtube.com/watch?v=6rOlGbLeQxI

The intent of the following video is to provide you with an overview of adding a trend line to a chart along with the Excel regression analysis.  (Note: Video is actually 12 min versus a stated run time of 15 min.)

Again, There are a number of good tutorials online…so, do not hesitate to search the web for additional information. A link for the Lynda tutorial site was provided earlier in the course.