Introduction to Probability and Statistics: Principles and Applications for Engineering and the Computing Sciences, 4/e
J. Susan Milton,
Radford University - Emeritus Jesse Arnold,
Virginia Polytechnic Institute & State University
ISBN: 007246836x Copyright year: 2003
Book Preface
Interpretation of much of the research in the engineering and computing sciences increasingly depends on statistical methods. Furthermore, the practicing engineer will be expected to understand and help implement statistical quality control techniques in the workplace. For these reasons, it is essential that students in these fields be exposed to statistical reasoning early in their careers. This text is intended as a first course in probability and applied statistics for students in the engineering and computing sciences. It is hoped that this first course will occur on the undergraduate level. However, the text can be used to advantage by graduate students who have little or no prior experience with statistical methods.
This text is not a statistical cookbook, nor is it a manual for researchers. We attempt to find a middle road – to provide a text that gives the student an understanding of the logic behind statistical techniques as well as practice in using them. A one-year course in elementary calculus should provide an adequate background for understanding everything presented here.
We chose the examples and exercises specifically for the student in the engineering and computing sciences. Most data sets are simulated. However, the simulations are done with care, so that the results of the analysis are consistent with recently reported research. References to reports upon which the data are based on are given whenever possible. In this way, the student will gain some insight into the types of engineering problems that can be handled statistically. Many exercises are left open-ended in hopes of stimulating some classroom discussion.
It is assumed that the student has access to some type of calculator. Many calculators on the markethave some built-in statistical capability. The use of these calculators is encouraged, for it allows the student to concentrate on the interpretation of the analysis rather than on the arithmetic computations.
We should point out that many data sets are rather small so that the student will not be overwhelmed by the computational aspects of statistics. We do not intend to imply that very small data sets are routinely used in the engineering fields. In fact, most major research projects involve a tremendous investment in time and money and result in a large body of data. New to the fourth edition, we have added some large data sets to better reflect the reality students will encounter after graduation.
Such data lend themselves to analysis by computer. For this reason, we include some instruction on the interpretation of output from statistical packages. The packages chosen for illustrative purposes are SAS and MINITAB. This was done because of their widespread availability and ease of use. We do not intend to imply that they are superior to other well-known packages such as SPSS (Statistical Package for the Social Sciences) or BMD (Biomedical Computer Programs, University of California Press).
Each Chapter ends with a chapter summary that is intended to remind the student of major topics presented in the chapter. This chapter summary also includes a list of important terms. A set of exercises is provided for each section of each chapter. In addition, each chapter has a set of review exercises in which the problems are presented in random order. It is hoped that this will help the student develop the ability to recognize the appropriate analysis. The appendices include statistical tables and answers to selected exercises.
A number of different courses can be taught from this book. They can vary in length from one quarter to one year. It is difficult to determine exactly what material can be covered in a given time, since this is a function of class sizes, academic maturity of the students, and inclination of the instructor. However, we do offer some guidelines for the use of this text. In particular, the type of course presented can vary from one whose chief aim is to familiarize the student with the computational aspects of probability and the handling of data sets to one of a more theoretical nature. In many cases we include the proof or derivation of theorems, these proofs can be skipped easily with no loss of continuity. Starred exercises in the text are either a little more difficult or theoretical in nature. These can be included or deleted to help set the tone of the course. When they are included, the course tends to take a more theoretical feel.
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2003 McGraw-Hill Higher Education