Page 11 - Applied statistics and probability for engineers

Preface vii

Chapters 2, 3, 4, and 5 cover the basic concepts of probability, discrete and continuous
random variables, probability distributions, expected values, joint probability distributions,
and independence. We have given a reasonably complete treatment of these topics but have
avoided many of the mathematical or more theoretical details.
Chapter 6 begins the treatment of statistical methods with random sampling; data sum-
mary and description techniques, including stem-and-leaf plots, histograms, box plots, and
probability plotting; and several types of time series plots. Chapter 7 discusses sampling dis-
tributions, the central limit theorem, and point estimation of parameters. This chapter also
introduces some of the important properties of estimators, the method of maximum likeli-
hood, the method of moments, and Bayesian estimation.
Chapter 8 discusses interval estimation for a single sample. Topics included are conidence
intervals for means, variances or standard deviations, proportions, prediction intervals, and tol-
erance intervals. Chapter 9 discusses hypothesis tests for a single sample. Chapter 10 presents
tests and conidence intervals for two samples. This material has been extensively rewritten and
reorganized. There is detailed information and examples of methods for determining appropri-
ate sample sizes. We want the student to become familiar with how these techniques are used to
solve real-world engineering problems and to get some understanding of the concepts behind
them. We give a logical, heuristic development of the procedures rather than a formal, mathe-
matical one. We have also included some material on nonparametric methods in these chapters.
Chapters 11 and 12 present simple and multiple linear regression including model ade-
quacy checking and regression model diagnostics and an introduction to logistic regression.
We use matrix algebra throughout the multiple regression material (Chapter 12) because it is
the only easy way to understand the concepts presented. Scalar arithmetic presentations of
multiple regression are awkward at best, and we have found that undergraduate engineers are
exposed to enough matrix algebra to understand the presentation of this material.
Chapters 13 and 14 deal with single- and multifactor experiments, respectively. The notions
of randomization, blocking, factorial designs, interactions, graphical data analysis, and frac-
tional factorials are emphasized. Chapter 15 introduces statistical quality control, emphasiz-
ing the control chart and the fundamentals of statistical process control.

WHAT’S NEW IN THIS EDITION
We received much feedback from users of the ifth edition of the book, and in response we
have made substantial changes in this new edition.
r #FDBVTF DPNQVUFS JOUFOTJWF NFUIPET BSF TP JNQPSUBOU JO UIF NPEFSO VTF PG TUBUJTUJDT XF
have added material on the bootstrap and its use in constructing conidence intervals.
r F IBWF JODSFBTFE UIF FNQIBTJT PO UIF VTF PG P -value in hypothesis testing. Many sections
of several chapters were rewritten to relect this.
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try to make the concepts easier to understand.
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ing, a technique widely used in the biopharmaceutical industry, but which has widespread
applications in other areas.
r $PNCJOJOH P-values when performing mutiple tests is incuded.
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tions of the results.
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