Research ProposalAbstractThe process of .docx

Research Proposal
Abstract
The process of learning especially higher learning is associated
with a number of challenges that should be addressed to
enhance learning. These include the difficulty walking through
hall ways and taking the advantages of the university’s basic
services. In fact, studying during breaks has become a major
challenge to many students including international students.
This project aims at uncovering the problem of overcrowding in
college campuses during breaks. It examines overcrowding
during short breaks of most students going to colleges and
universities in Jubail. It will illustrate the consequences of
overcrowding in college campuses during breaks. It will
examine student and institution awareness of the effects of
overcrowding in college campuses during breaks.

Chapter One
1.1 Background of the study
Many students go home during breaks to finish their work and
relax with their families and friends. They are usually excited
and enthusiastic of upcoming academic breaks. However, some
prefer to stay on campus during breaks and enjoying the
services of the library and the cafeteria. Most students prefer
staying on campus during academic breaks. However, staying
during breaks can lead to overcrowding in the college campuses.
Usually, students temporarily move to a single hall to make it
easier for the skeleton crew of housing staff to effectively
manage these students.
1.2 Problem Statement
Over the past years, overcrowding in college campuses during
breaks continues to grow with many more students learning in
Jubail University College. The level of students interested in
remaining on campuses during short breaks tends to be
increasing over time. This could be subjecting these students to
risks of suffering impacting their health and overall well-being.
Lack of awareness on the effects of overcrowding in college
campuses during breaks tends to exist among students and their
institutions.
1.3 Objectives
The main objective of this study is to assess overcrowding in
college campuses in Jubail University College during breaks.
We also intend to determine the level of awareness of the
effects of overcrowding among students and their institutions.
1.4 Research Questions
1. Are college campuses in Jubail overcrowded during breaks?
2. What is the level of awareness among students and
institutions?
1.5 Scope of Study
This study will be conducted in college campuses in Jubail
during breaks. The study will cover several colleges and

universities in the region.
1
COM 3120 Final Paper Checklist
Management Training Program
Remember that this is an organizational communication course.
Therefore, use your book as a
reference. Look at what we have covered and include what you
feel is pertinent to answering
the questions and developing a strong presentation.
For example, when you have to give background on what kind
of organization you are looking
at, are you creating a review of a domestic organization? An
international organization? How do
you know which one? Remember to cite!
Also, the pages are approximate. This means that if you need
more space to explain your
organization, then you can. If you have less and want to
dedicate more time to another part,
then you can.
Project length: minimum 10 pages and maximum 15 pages

Format: APA-style
What your Management Training Program Should Include
What is a management training: Management training is training
activity that focuses
on improving an individual's skills as a leader and manager.
There may be an emphasis
on soft skills, such as communication and empathy, which
enable better team work
and more progressive relationships with the people they
manage.
Part I
• Background on the organization (Approximately 2 to 3 pages
double-spaced)
- Explain what kind of organization it is
- A brief history of the organization. Give sufficient background
to understand the
organization.
• Explain what is the issue you are addressing. Why is this an
issue? Support
your reasoning. (Approximately 2 to 3 pages double-spaced)
- Describe the organizational environment from what you can
see and determine
based on your research

- Remember to look at the terms and concepts we have covered
in class and that
are in your text to review
�1
• Identify one key communication issue you would like to
develop into a
management training program. Based on what you identified as
an issue for
this organization, develop your management training program.
Examples
include:
- Decision-Making process for leaders
- Conflict Management for Leaders
- Organizational Diversity Processes for Leaders
- Technological processes for Leaders- i.e. the use of
technology in organizations
(business writing 2.0)
PART II
• Management Training Program includes your training module
- Learning objectives

- The tasks your audience need to perform
- Determine the training activities that will help the audience
learn to perform the
tasks
- Explain the tasks to your audience and what they need to do.
What your Employee Handbook Should Include
What is an employee handbook: An employee handbook is a
document that contains a
company's operating procedures. The document discloses legal
information, such as
the company equal employment opportunity policy, including
workplace harassment
policies, as well as expectations for safety in the workplace.
Part I
• Background on the organization (Approximately 1 to 2 pages)
to understand the
organization.
issue? Support

your reasoning. (Approximately 1 to 2 pages)
�2
see and determine
in class and that
Part II
• Identify two to three key communication issue you would like
to develop as
part of your employee handbook. Based on what you identified
as an issue
for this organization, develop the policy. Think of how these
policies will solve
the issues you have identified. Examples include:
- Equal Opportunity Policy
- Use of technology Policy
- Sexual Harassment Policy
- Handling difficult situations in the workplace

What Your Plan for Your Organizational Restructuring Should
Include
What is organizational restructuring: Restructuring is the
corporate management term
for the act of reorganizing the legal, ownership, operational, or
other structures of a
company for the purpose of making it more profitable, or better
organized for its
present needs.
Part I
• Background on the organization (Approximately 1 to 2 pages)
to understand the
organization.
issue? Support
your reasoning. (Approximately 1 to 2 pages)
�3
see and determine

in class and that
Part II
• Identify two to three key communication issues you would like
to develop as
part of your organizational restructuring. Based on what you
identified as an
issue for this organization, develop what that organization
should do
differently. Think of how the re-organizing will solve the issues
you have
identified. Examples include:
- Should there be an emphasis on diversity hiring? What would
that look like?
What would the results look like?
- Should there be a restructuring on the use of technology?
Social media? What
would that look like? What would the results look like?
What Every Project Should Include
- Cover page (not included in the page count)

- Proposal finalized (submitted separately)
- References page (not included in the page count
- Remember that you can use charts, graphs, tables, images, etc.
to enhance your
projects. Use these appropriately and as necessary.
Rubric
30% Fulfillment of the Purpose
25% Content
20% Development
15% Grammar and Spelling
10% Presentation
�4
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Probability & Statistics
for Engineers & Scientists
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Probability & Statistics for
Engineers & Scientists
N I N T H E D I T I O N
Ronald E. Walpole
Roanoke College
Raymond H. Myers
Virginia Tech
Sharon L. Myers
Radford University
Keying Ye
University of Texas at San Antonio
Prentice Hall
Editor in Chief: Deirdre Lynch
Acquisitions Editor: Christopher Cummings

Executive Content Editor: Christine O’Brien
Associate Editor: Christina Lepre
Senior Managing Editor: Karen Wernholm
Senior Production Project Manager: Tracy Patruno
Design Manager: Andrea Nix
Cover Designer: Heather Scott
Digital Assets Manager: Marianne Groth
Associate Media Producer: Vicki Dreyfus
Marketing Manager: Alex Gay
Marketing Assistant: Kathleen DeChavez
Senior Author Support/Technology Specialist: Joe Vetere
Rights and Permissions Advisor: Michael Joyce
Senior Manufacturing Buyer: Carol Melville
Production Coordination: Lifland et al. Bookmakers
Composition: Keying Ye
Cover photo: Marjory Dressler/Dressler Photo-Graphics
Many of the designations used by manufacturers and sellers to
distinguish their products are claimed as
trademarks. Where those designations appear in this book, and
Pearson was aware of a trademark claim, the
designations have been printed in initial caps or all caps.
Library of Congress Cataloging-in-Publication Data
Probability & statistics for engineers & scientists/Ronald E.
Walpole . . . [et al.] — 9th ed.
p. cm.
ISBN 978-0-321-62911-1
1. Engineering—Statistical methods. 2. Probabilities. I.
Walpole, Ronald E.
TA340.P738 2011
519.02’462–dc22
2010004857

Copyright c⃝ 2012, 2007, 2002 Pearson Education, Inc. All
rights reserved. No part of this publication may be
reproduced, stored in a retrieval system, or transmitted, in any
form or by any means, electronic, mechanical,
photocopying, recording, or otherwise, without the prior written
permission of the publisher. Printed in the
United States of America. For information on obtaining
permission for use of material in this work, please submit
a written request to Pearson Education, Inc., Rights and
Contracts Department, 501 Boylston Street, Suite 900,
Boston, MA 02116, fax your request to 617-671-3447, or e-mail
at http://www.pearsoned.com/legal/permissions.htm.
1 2 3 4 5 6 7 8 9 10—EB—14 13 12 11 10
ISBN 10: 0-321-62911-6
ISBN 13: 978-0-321-62911-1
http://www.pearsoned.com/legal/permissions.htm
www.pearsonhighered.com
This book is dedicated to
Billy and Julie
R.H.M. and S.L.M.
Limin, Carolyn and Emily
K.Y.
This page intentionally left blank

Contents
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . xv
1 Introduction to Statistics and Data Analysis . . . . . . . . . . . 1
1.1 Overview: Statistical Inference, Samples, Populations, and
the
Role of Probability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . 1
1.2 Sampling Procedures; Collection of Data . . . . . . . . . . . . . .
. . . . . . . . . . 7
1.3 Measures of Location: The Sample Mean and Median . . . . .
. . . . . . 11
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 13
1.4 Measures of Variability . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . 14
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 17
1.5 Discrete and Continuous Data. . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 17
1.6 Statistical Modeling, Scientific Inspection, and Graphical
Diag-

nostics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . 18
1.7 General Types of Statistical Studies: Designed Experiment,
Observational Study, and Retrospective Study . . . . . . . . . . . . .
. . . . . 27
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 30
2 Probability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . 35
2.1 Sample Space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . 35
2.2 Events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . 38
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 42
2.3 Counting Sample Points . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . 44
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 51
2.4 Probability of an Event . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . 52
2.5 Additive Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . 56
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 59

2.6 Conditional Probability, Independence, and the Product Rule
. . . 62
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 69
2.7 Bayes’ Rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . 72
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 76
Review Exercises. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . 77
viii Contents
2.8 Potential Misconceptions and Hazards; Relationship to
Material
in Other Chapters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . 79
3 Random Variables and Probability Distributions . . . . . . 81
3.1 Concept of a Random Variable . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 81
3.2 Discrete Probability Distributions . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 84
3.3 Continuous Probability Distributions . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 87

Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 91
3.4 Joint Probability Distributions . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 94
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 104
. . . . . . . . . 107
Material
. . . . . . . . . . . . . 109
4 Mathematical Expectation . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . 111
4.1 Mean of a Random Variable . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . 111
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 117
4.2 Variance and Covariance of Random Variables. . . . . . . . . .
. . . . . . . . . 119
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 127
4.3 Means and Variances of Linear Combinations of Random
Variables 128
4.4 Chebyshev’s Theorem . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . 135
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 137
. . . . . . . . . 139
Material
. . . . . . . . . . . . . 142
5 Some Discrete Probability Distributions . . . . . . . . . . . . . . . .
143
5.1 Introduction and Motivation . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . 143
5.2 Binomial and Multinomial Distributions . . . . . . . . . . . . . . .
. . . . . . . . . . 143
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 150
5.3 Hypergeometric Distribution . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . 152
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 157
5.4 Negative Binomial and Geometric Distributions . . . . . . . . .
. . . . . . . . 158
5.5 Poisson Distribution and the Poisson Process . . . . . . . . . . .
. . . . . . . . . 161

Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 164
. . . . . . . . . 166
Material
. . . . . . . . . . . . . 169
Contents ix
6 Some Continuous Probability Distributions . . . . . . . . . . . . .
171
6.1 Continuous Uniform Distribution . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 171
6.2 Normal Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . 172
6.3 Areas under the Normal Curve . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 176
6.4 Applications of the Normal Distribution . . . . . . . . . . . . . . .
. . . . . . . . . . 182
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 185
6.5 Normal Approximation to the Binomial . . . . . . . . . . . . . . .
. . . . . . . . . . 187

Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 193
6.6 Gamma and Exponential Distributions . . . . . . . . . . . . . . . .
. . . . . . . . . . 194
6.7 Chi-Squared Distribution. . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . 200
6.8 Beta Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . 201
6.9 Lognormal Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . 201
6.10 Weibull Distribution (Optional) . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . 203
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 206
. . . . . . . . . 207
Material
. . . . . . . . . . . . . 209
7 Functions of Random Variables (Optional). . . . . . . . . . . . . .
211
7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . 211

7.2 Transformations of Variables . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . 211
7.3 Moments and Moment-Generating Functions . . . . . . . . . . . .
. . . . . . . . 218
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 222
8 Fundamental Sampling Distributions and
Data Descriptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . 225
8.1 Random Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . 225
8.2 Some Important Statistics . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . 227
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 230
8.3 Sampling Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . 232
8.4 Sampling Distribution of Means and the Central Limit
Theorem. 233
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 241
8.5 Sampling Distribution of S2 . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . 243
8.6 t-Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . 246

8.7 F -Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . 251
8.8 Quantile and Probability Plots . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 254
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 259
. . . . . . . . . 260
Material
. . . . . . . . . . . . . 262
Sara
Sara
Sara
Sara
Sara
Sara
x Contents
9 One- and Two-Sample Estimation Problems . . . . . . . . . . . .
265

9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . 265
9.2 Statistical Inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . 265
9.3 Classical Methods of Estimation. . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 266
9.4 Single Sample: Estimating the Mean . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 269
9.5 Standard Error of a Point Estimate . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . 276
9.6 Prediction Intervals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . 277
9.7 Tolerance Limits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . 280
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 282
9.8 Two Samples: Estimating the Difference between Two
Means . . . 285
9.9 Paired Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . 291
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 294
9.10 Single Sample: Estimating a Proportion . . . . . . . . . . . . . .
. . . . . . . . . . . 296

9.11 Two Samples: Estimating the Difference between Two
Proportions 300
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 302
9.12 Single Sample: Estimating the Variance . . . . . . . . . . . . . .
. . . . . . . . . . . 303
9.13 Two Samples: Estimating the Ratio of Two Variances . . . .
. . . . . . . 305
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 307
9.14 Maximum Likelihood Estimation (Optional) . . . . . . . . . . .
. . . . . . . . . . 307
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 312
. . . . . . . . . 313
Material
. . . . . . . . . . . . . 316
10 One- and Two-Sample Tests of Hypotheses . . . . . . . . . . . . .
319
10.1 Statistical Hypotheses: General Concepts . . . . . . . . . . . . .
. . . . . . . . . . 319
10.2 Testing a Statistical Hypothesis . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . 321
10.3 The Use of P -Values for Decision Making in Testing
Hypotheses . 331
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 334
10.4 Single Sample: Tests Concerning a Single Mean . . . . . . . .
. . . . . . . . . 336
10.5 Two Samples: Tests on Two Means . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 342
10.6 Choice of Sample Size for Testing Means . . . . . . . . . . . . .
. . . . . . . . . . . 349
10.7 Graphical Methods for Comparing Means . . . . . . . . . . . . .
. . . . . . . . . . 354
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 356
10.8 One Sample: Test on a Single Proportion. . . . . . . . . . . . . .
. . . . . . . . . . 360
10.9 Two Samples: Tests on Two Proportions . . . . . . . . . . . . . .
. . . . . . . . . . 363
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 365
10.10 One- and Two-Sample Tests Concerning Variances . . . . .
. . . . . . . . . 366
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . 369
10.11 Goodness-of-Fit Test . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . 370
10.12 Test for Independence (Categorical Data) . . . . . . . . . . . .
. . . . . . . . . . . 373
Contents xi
10.13 Test for Homogeneity . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . 376
10.14 Two-Sample Case Study . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . 379
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 382
. . . . . . . . . 384
Material
. . . . . . . . . . . . . 386
11 Simple Linear Regression and Correlation . . . . . . . . . . . . . .
389
11.1 Introduction to Linear Regression . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . 389
11.2 The Simple Linear Regression Model . . . . . . . . . . . . . . . .

. . . . . . . . . . . . 390
11.3 Least Squares and the Fitted Model . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 394
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 398
11.4 Properties of the Least Squares Estimators . . . . . . . . . . . .
. . . . . . . . . . 400
11.5 Inferences Concerning the Regression Coefficients. . . . . .
. . . . . . . . . . 403
11.6 Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . 408
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 411
11.7 Choice of a Regression Model . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . 414
11.8 Analysis-of-Variance Approach . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . 414
11.9 Test for Linearity of Regression: Data with Repeated
Observations 416
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 421
11.10 Data Plots and Transformations. . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . 424
11.11 Simple Linear Regression Case Study. . . . . . . . . . . . . . .

. . . . . . . . . . . . . 428
11.12 Correlation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . 430
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 435
. . . . . . . . . 436
Material
. . . . . . . . . . . . . 442
12 Multiple Linear Regression and Certain
Nonlinear Regression Models . . . . . . . . . . . . . . . . . . . . . . . . .
. . 443
12.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . 443
12.2 Estimating the Coefficients . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . 444
12.3 Linear Regression Model Using Matrices . . . . . . . . . . . . .
. . . . . . . . . . . 447
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 450
12.4 Properties of the Least Squares Estimators . . . . . . . . . . . .
. . . . . . . . . . 453
12.5 Inferences in Multiple Linear Regression . . . . . . . . . . . . .

. . . . . . . . . . . . 455
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 461
12.6 Choice of a Fitted Model through Hypothesis Testing . . . .
. . . . . . . 462
12.7 Special Case of Orthogonality (Optional) . . . . . . . . . . . . .
. . . . . . . . . . . 467
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 471
12.8 Categorical or Indicator Variables . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . 472
xii Contents
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 476
12.9 Sequential Methods for Model Selection . . . . . . . . . . . . . .
. . . . . . . . . . . 476
12.10 Study of Residuals and Violation of Assumptions (Model
Check-
ing) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . 482
12.11 Cross Validation, Cp, and Other Criteria for Model
Selection . . . . 487

Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 494
12.12 Special Nonlinear Models for Nonideal Conditions . . . . .
. . . . . . . . . . 496
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 500
. . . . . . . . . 501
Material
. . . . . . . . . . . . . 506
13 One-Factor Experiments: General . . . . . . . . . . . . . . . . . . . .
. . . . 507
13.1 Analysis-of-Variance Technique. . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . 507
13.2 The Strategy of Experimental Design. . . . . . . . . . . . . . . . .
. . . . . . . . . . . 508
13.3 One-Way Analysis of Variance: Completely Randomized
Design
(One-Way ANOVA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . 509
13.4 Tests for the Equality of Several Variances . . . . . . . . . . . .
. . . . . . . . . . 516
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . 518
13.5 Single-Degree-of-Freedom Comparisons . . . . . . . . . . . . . .
. . . . . . . . . . . . 520
13.6 Multiple Comparisons . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . 523
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 529
13.7 Comparing a Set of Treatments in Blocks . . . . . . . . . . . . .
. . . . . . . . . . 532
13.8 Randomized Complete Block Designs. . . . . . . . . . . . . . . .
. . . . . . . . . . . . 533
13.9 Graphical Methods and Model Checking . . . . . . . . . . . . . .
. . . . . . . . . . 540
13.10 Data Transformations in Analysis of Variance . . . . . . . . .
. . . . . . . . . . 543
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 545
13.11 Random Effects Models . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . 547
13.12 Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . 551
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 553

. . . . . . . . . 555
Material
. . . . . . . . . . . . . 559
14 Factorial Experiments (Two or More Factors) . . . . . . . . . .
561
14.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . 561
14.2 Interaction in the Two-Factor Experiment . . . . . . . . . . . . .
. . . . . . . . . . 562
14.3 Two-Factor Analysis of Variance . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . 565
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 575
14.4 Three-Factor Experiments. . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . 579
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 586
Contents xiii
14.5 Factorial Experiments for Random Effects and Mixed
Models. . . . 588
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . 592
. . . . . . . . . 594
Material
. . . . . . . . . . . . . 596
15 2k Factorial Experiments and Fractions . . . . . . . . . . . . . . . .
. 597
15.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . 597
15.2 The 2k Factorial: Calculation of Effects and Analysis of
Variance 598
15.3 Nonreplicated 2k Factorial Experiment . . . . . . . . . . . . . . .
. . . . . . . . . . . 604
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 609
15.4 Factorial Experiments in a Regression Setting . . . . . . . . . .
. . . . . . . . . 612
15.5 The Orthogonal Design . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . 617
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 625
15.6 Fractional Factorial Experiments . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . 626

15.7 Analysis of Fractional Factorial Experiments . . . . . . . . . .
. . . . . . . . . . 632
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 634
15.8 Higher Fractions and Screening Designs . . . . . . . . . . . . . .
. . . . . . . . . . . 636
15.9 Construction of Resolution III and IV Designs with 8, 16,
and 32
Design Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . 637
15.10 Other Two-Level Resolution III Designs; The Plackett-
Burman
Designs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . 638
15.11 Introduction to Response Surface Methodology . . . . . . . .
. . . . . . . . . . 639
15.12 Robust Parameter Design . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . 643
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 652
. . . . . . . . . 653
Material

. . . . . . . . . . . . . 654
16 Nonparametric Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . 655
16.1 Nonparametric Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . 655
16.2 Signed-Rank Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . 660
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 663
16.3 Wilcoxon Rank-Sum Test . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . 665
16.4 Kruskal-Wallis Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . 668
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 670
16.5 Runs Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . 671
16.6 Tolerance Limits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . 674
16.7 Rank Correlation Coefficient . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . 674
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 677

. . . . . . . . . 679
xiv Contents
17 Statistical Quality Control . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . 681
17.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . 681
17.2 Nature of the Control Limits . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . 683
17.3 Purposes of the Control Chart . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . 683
17.4 Control Charts for Variables . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . 684
17.5 Control Charts for Attributes . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . 697
17.6 Cusum Control Charts . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . 705
. . . . . . . . . 706
18 Bayesian Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . 709
18.1 Bayesian Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . 709
18.2 Bayesian Inferences . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . 710
18.3 Bayes Estimates Using Decision Theory Framework . . . . .
. . . . . . . . 717
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 718
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . 721
Appendix A: Statistical Tables and Proofs . . . . . . . . . . . . . . . .
. . 725
Appendix B: Answers to Odd-Numbered Non-Review
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . 769
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . 785
Preface
General Approach and Mathematical Level
Our emphasis in creating the ninth edition is less on adding new
material and more
on providing clarity and deeper understanding. This objective
was accomplished in
part by including new end-of-chapter material that adds
connective tissue between
chapters. We affectionately call these comments at the end of
the chapter “Pot
Holes.” They are very useful to remind students of the big

picture and how each
chapter fits into that picture, and they aid the student in
learning about limitations
and pitfalls that may result if procedures are misused. A deeper
understanding
of real-world use of statistics is made available through class
projects, which were
added in several chapters. These projects provide the
opportunity for students
alone, or in groups, to gather their own experimental data and
draw inferences. In
some cases, the work involves a problem whose solution will
illustrate the meaning
of a concept or provide an empirical understanding of an
important statistical
result. Some existing examples were expanded and new ones
were introduced to
create “case studies,” in which commentary is provided to give
the student a clear
understanding of a statistical concept in the context of a
practical situation.
In this edition, we continue to emphasize a balance between
theory and appli-
cations. Calculus and other types of mathematical support (e.g.,
linear algebra)
are used at about the same level as in previous editions. The
coverage of an-
alytical tools in statistics is enhanced with the use of calculus
when discussion
centers on rules and concepts in probability. Probability
distributions and sta-
tistical inference are highlighted in Chapters 2 through 10.
Linear algebra and
matrices are very lightly applied in Chapters 11 through 15,
where linear regres-

sion and analysis of variance are covered. Students using this
text should have
had the equivalent of one semester of differential and integral
calculus. Linear
algebra is helpful but not necessary so long as the section in
Chapter 12 on mul-
tiple linear regression using matrix algebra is not covered by
the instructor. As
in previous editions, a large number of exercises that deal with
real-life scientific
and engineering applications are available to challenge the
student. The many
data sets associated with the exercises are available for
download from the website
http://www.pearsonhighered.com/datasets.
xv
http://www.pearsonhighered.com/datasets
xvi Preface
Summary of the Changes in the Ninth Edition
• Class projects were added in several chapters to provide a
deeper understand-
ing of the real-world use of statistics. Students are asked to
produce or gather
their own experimental data and draw inferences from these
data.
• More case studies were added and others expanded to help
students under-
stand the statistical methods being presented in the context of a
real-life situ-

ation. For example, the interpretation of confidence limits,
prediction limits,
and tolerance limits is given using a real-life situation.
• “Pot Holes” were added at the end of some chapters and
expanded in others.
These comments are intended to present each chapter in the
context of the
big picture and discuss how the chapters relate to one another.
They also
provide cautions about the possible misuse of statistical
techniques presented
in the chapter.
• Chapter 1 has been enhanced to include more on single-
number statistics as
well as graphical techniques. New fundamental material on
sampling and
experimental design is presented.
• Examples added to Chapter 8 on sampling distributions are
intended to moti-
vate P -values and hypothesis testing. This prepares the student
for the more
challenging material on these topics that will be presented in
Chapter 10.
• Chapter 12 contains additional development regarding the
effect of a single
regression variable in a model in which collinearity with other
variables is
severe.
• Chapter 15 now introduces material on the important topic of
response surface
methodology (RSM). The use of noise variables in RSM allows

the illustration
of mean and variance (dual response surface) modeling.
• The central composite design (CCD) is introduced in Chapter
15.
• More examples are given in Chapter 18, and the discussion of
using Bayesian
methods for statistical decision making has been enhanced.
Content and Course Planning
This text is designed for either a one- or a two-semester course.
A reasonable
plan for a one-semester course might include Chapters 1
through 10. This would
result in a curriculum that concluded with the fundamentals of
both estimation
and hypothesis testing. Instructors who desire that students be
exposed to simple
linear regression may wish to include a portion of Chapter 11.
For instructors
who desire to have analysis of variance included rather than
regression, the one-
semester course may include Chapter 13 rather than Chapters 11
and 12. Chapter
13 features one-factor analysis of variance. Another option is to
eliminate portions
of Chapters 5 and/or 6 as well as Chapter 7. With this option,
one or more of
the discrete or continuous distributions in Chapters 5 and 6 may
be eliminated.
These distributions include the negative binomial, geometric,
gamma, Weibull,
beta, and log normal distributions. Other features that one might
consider re-
moving from a one-semester curriculum include maximum

likelihood estimation,
Preface xvii
prediction, and/or tolerance limits in Chapter 9. A one-semester
curriculum has
built-in flexibility, depending on the relative interest of the
instructor in regression,
analysis of variance, experimental design, and response surface
methods (Chapter
15). There are several discrete and continuous distributions
(Chapters 5 and 6)
that have applications in a variety of engineering and scientific
areas.
Chapters 11 through 18 contain substantial material that can be
added for the
second semester of a two-semester course. The material on
simple and multiple
linear regression is in Chapters 11 and 12, respectively. Chapter
12 alone offers a
substantial amount of flexibility. Multiple linear regression
includes such “special
topics” as categorical or indicator variables, sequential methods
of model selection
such as stepwise regression, the study of residuals for the
detection of violations
of assumptions, cross validation and the use of the PRESS
statistic as well as
Cp, and logistic regression. The use of orthogonal regressors, a
precursor to the
experimental design in Chapter 15, is highlighted. Chapters 13
and 14 offer a
relatively large amount of material on analysis of variance

(ANOVA) with fixed,
random, and mixed models. Chapter 15 highlights the
application of two-level
designs in the context of full and fractional factorial
experiments (2k). Special
screening designs are illustrated. Chapter 15 also features a new
section on response
surface methodology (RSM) to illustrate the use of experimental
design for finding
optimal process conditions. The fitting of a second order model
through the use of
a central composite design is discussed. RSM is expanded to
cover the analysis of
robust parameter design type problems. Noise variables are used
to accommodate
dual response surface models. Chapters 16, 17, and 18 contain a
moderate amount
of material on nonparametric statistics, quality control, and
Bayesian inference.
Chapter 1 is an overview of statistical inference presented on a
mathematically
simple level. It has been expanded from the eighth edition to
more thoroughly
cover single-number statistics and graphical techniques. It is
designed to give
students a preliminary presentation of elementary concepts that
will allow them to
understand more involved details that follow. Elementary
concepts in sampling,
data collection, and experimental design are presented, and
rudimentary aspects
of graphical tools are introduced, as well as a sense of what is
garnered from a
data set. Stem-and-leaf plots and box-and-whisker plots have
been added. Graphs

are better organized and labeled. The discussion of uncertainty
and variation in
a system is thorough and well illustrated. There are examples of
how to sort
out the important characteristics of a scientific process or
system, and these ideas
are illustrated in practical settings such as manufacturing
processes, biomedical
studies, and studies of biological and other scientific systems. A
contrast is made
between the use of discrete and continuous data. Emphasis is
placed on the use
of models and the information concerning statistical models that
can be obtained
from graphical tools.
Chapters 2, 3, and 4 deal with basic probability as well as
discrete and contin-
uous random variables. Chapters 5 and 6 focus on specific
discrete and continuous
distributions as well as relationships among them. These
chapters also highlight
examples of applications of the distributions in real-life
scientific and engineering
studies. Examples, case studies, and a large number of exercises
edify the student
concerning the use of these distributions. Projects bring the
practical use of these
distributions to life through group work. Chapter 7 is the most
theoretical chapter
xviii Preface
in the text. It deals with transformation of random variables and

will likely not be
used unless the instructor wishes to teach a relatively
theoretical course. Chapter
8 contains graphical material, expanding on the more
elementary set of graphi-
cal tools presented and illustrated in Chapter 1. Probability
plotting is discussed
and illustrated with examples. The very important concept of
sampling distribu-
tions is presented thoroughly, and illustrations are given that
involve the central
limit theorem and the distribution of a sample variance under
normal, independent
(i.i.d.) sampling. The t and F distributions are introduced to
motivate their use
in chapters to follow. New material in Chapter 8 helps the
student to visualize the
importance of hypothesis testing, motivating the concept of a P
-value.
Chapter 9 contains material on one- and two-sample point and
interval esti-
mation. A thorough discussion with examples points out the
contrast between the
different types of intervals—confidence intervals, prediction
intervals, and toler-
ance intervals. A case study illustrates the three types of
statistical intervals in the
context of a manufacturing situation. This case study highlights
the differences
among the intervals, their sources, and the assumptions made in
their develop-
ment, as well as what type of scientific study or question
requires the use of each
one. A new approximation method has been added for the
inference concerning a

proportion. Chapter 10 begins with a basic presentation on the
pragmatic mean-
ing of hypothesis testing, with emphasis on such fundamental
concepts as null and
alternative hypotheses, the role of probability and the P -value,
and the power of
a test. Following this, illustrations are given of tests concerning
one and two sam-
ples under standard conditions. The two-sample t-test with
paired observations
is also described. A case study helps the student to develop a
clear picture of
what interaction among factors really means as well as the
dangers that can arise
when interaction between treatments and experimental units
exists. At the end of
Chapter 10 is a very important section that relates Chapters 9
and 10 (estimation
and hypothesis testing) to Chapters 11 through 16, where
statistical modeling is
prominent. It is important that the student be aware of the
strong connection.
Chapters 11 and 12 contain material on simple and multiple
linear regression,
respectively. Considerably more attention is given in this
edition to the effect that
collinearity among the regression variables plays. A situation is
presented that
shows how the role of a single regression variable can depend in
large part on what
regressors are in the model with it. The sequential model
selection procedures (for-
ward, backward, stepwise, etc.) are then revisited in regard to
this concept, and
the rationale for using certain P -values with these procedures is

provided. Chap-
ter 12 offers material on nonlinear modeling with a special
presentation of logistic
regression, which has applications in engineering and the
biological sciences. The
material on multiple regression is quite extensive and thus
provides considerable
flexibility for the instructor, as indicated earlier. At the end of
Chapter 12 is com-
mentary relating that chapter to Chapters 14 and 15. Several
features were added
that provide a better understanding of the material in general.
For example, the
end-of-chapter material deals with cautions and difficulties one
might encounter.
It is pointed out that there are types of responses that occur
naturally in practice
(e.g. proportion responses, count responses, and several others)
with which stan-
dard least squares regression should not be used because
standard assumptions do
not hold and violation of assumptions may induce serious
errors. The suggestion is
Preface xix
made that data transformation on the response may alleviate the
problem in some
cases. Flexibility is again available in Chapters 13 and 14, on
the topic of analysis
of variance. Chapter 13 covers one-factor ANOVA in the
context of a completely
randomized design. Complementary topics include tests on
variances and multiple

comparisons. Comparisons of treatments in blocks are
highlighted, along with the
topic of randomized complete blocks. Graphical methods are
extended to ANOVA
to aid the student in supplementing the formal inference with a
pictorial type of in-
ference that can aid scientists and engineers in presenting
material. A new project
is given in which students incorporate the appropriate
randomization into each
plan and use graphical techniques and P -values in reporting the
results. Chapter
14 extends the material in Chapter 13 to accommodate two or
more factors that
are in a factorial structure. The ANOVA presentation in Chapter
14 includes work
in both random and fixed effects models. Chapter 15 offers
material associated
with 2k factorial designs; examples and case studies present the
use of screening
designs and special higher fractions of the 2k. Two new and
special features are
the presentations of response surface methodology (RSM) and
robust parameter
design. These topics are linked in a case study that describes
and illustrates a
dual response surface design and analysis featuring the use of
process mean and
variance response surfaces.
Computer Software
Case studies, beginning in Chapter 8, feature computer printout
and graphical
material generated using both SAS and MINITAB. The
inclusion of the computer

reflects our belief that students should have the experience of
reading and inter-
preting computer printout and graphics, even if the software in
the text is not that
which is used by the instructor. Exposure to more than one type
of software can
broaden the experience base for the student. There is no reason
to believe that
the software used in the course will be that which the student
will be called upon
to use in practice following graduation. Examples and case
studies in the text are
supplemented, where appropriate, by various types of residual
plots, quantile plots,
normal probability plots, and other plots. Such plots are
particularly prevalent in
Chapters 11 through 15.
Supplements
Instructor’s
Solution
s Manual. This resource contains worked-out solutions to all
text exercises and is available for download from Pearson
Education’s Instructor
Resource Center.
Student

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