Engineering Statistics / Edition 5

by Douglas C. Montgomery

Overview

This book helps readers understand statistical methodology and use it to solve engineering problems. It introduces the role of statistics and probability in engineering problem solving and illustrates the useful information contained in simple summary and graphical displays. Then it introduces the concepts of random variable, and the probability distribution that describes the behavior of that random variable presenting the basic tools of statistical inference; point estimation, confidence intervals, and hypothesis testing. It also explores empirical model-building, the design of engineering experiments and statistical quality control.

Product Details

ISBN-13:	2900470631477
Publication date:	12/21/2010
Pages:	544
Product dimensions:	6.50(w) x 1.50(h) x 9.50(d)

About the Author

Douglas C. Montgomery, Regents' Professor of Industrial Engineering and Statistics at Arizona State University, received his B.S., M.S., and Ph.D. degrees in engineering from Virginia Polytechnic Institute. He has been a faculty member of the School of Industrial and Systems Engineering at the Georgia Institute of Technology and a professor of mechanical engineering and director of the Program in Industrial Engineering at the University of Washington, where he held the John M. Fluke Distinguished Chair of Manufacturing Engineering. The recipient of numerous awards including the Deming Lecture Award from the American Statistical Association, Shewhart Medal of the American Society for Quality, the George Box medal from EENBIS, the Greenfield medal from the Royal Statistical Society, the Brumbaugh Award, the Lloyd S. Nelson Award, the William G. Hunter Award, and two Shewell Awards from the ASQ. He is the editor of Quality and Reliability Engineering International and a former editor of the Journal of Quality Technology.

George C. Runger, Ph.D., is a Professor of Industrial Engineering at Arizona State University. His research is on data mining, real-time monitoring and control, and other data-analysis methods with a focus on large, complex, multivariate data streams. His work is funded by grants from the National Science Foundation and corporations. In addition to academic work, he was a senior engineer at IBM. He holds degrees in industrial engineering and statistics.

Norma Faris Hubele, Professor Emeritus of Engineering and Statistics at Arizona State University, and formerly Director of Strategic Initiatives for the Ira A. Fulton School of Engineering, holds degrees in mathematics, operations research, statistics and computer and systems engineering. She is co-owner of the metallurgical processing and statistical consulting company Refrac Systems in Chandler, Arizona. She is on the editorial board of the Journal of Quality Technology and Quality Technology & Quantity Management, as a founding member. Her specializations include capability analysis, transportation safety, and statistics in litigation.

Table of Contents

1	The Role of Statistics in Engineering	1
1-1	The Engineering Method and Statistical Thinking	1
1-2	Collecting Engineering Data	10
1-3	Mechanistic and Empirical Models	12
1-4	Designing Experimental Investigations	16
1-5	Observing Processes over Time	19
2	Data Summary and Presentation	24
2-1	The Importance of Data Summary and Display	24
2-2	The Stem-and-Leaf Diagram	25
2-3	The Frequency Distribution and Histogram	30
2-4	The Box Plot	35
2-5	Time Sequence Plots	37
3	Random Variables and Probability Distributions	46
3-1	Introduction	47
3-2	Random Variables	49
3-3	Probability	51
3-4	Probability Density Function, Mean, and Variance of a Continuous Random Variable	56
3-4.1	Probability Density Function	56
3-4.2	Mean and Variance of a Continuous Random Variable	59
3-5	Normal Distribution	63
3-6	Probability Plots	75
3-7	Probability Mass Function, Mean, and Variance of a Discrete Random Variable	79
3-7.1	Probability Mass Function	80
3-7.2	Mean and Variance of a Discrete Random Variable	81
3-8	Binomial Distribution	84
3-9	Poisson Process	92
3-9.1	Poisson Distribution	92
3-9.2	Exponential Distribution	99
3-10	Normal Approximation to the Binomial and Poisson Distributions	105
3-11	Correlation and Independence	109
3-11.1	Correlation	109
3-11.2	Independence	113
3-12	Random Samples, Statistics, and the Central Limit Theorem	115
4	Decision Making for a Single Sample	131
4-1	Statistical Inference	132
4-2	Point Estimation	133
4-3	Hypothesis Testing	140
4-3.1	Statistical Hypotheses	140
4-3.2	Testing Statistical Hypotheses	142
4-3.3	One-Sided and Two-Sided Hypotheses	150
4-3.4	General Procedure for Hypothesis Testing	152
4-4	Inference on the Mean of a Population, Variance Known	154
4-4.1	Hypothesis Testing on the Mean	154
4-4.2	P-Values in Hypothesis Testing	157
4-4.3	Type II Error and Choice of Sample Size	158
4-4.4	Large-Sample Test	161
4-4.5	Some Practical Comments on Hypothesis Testing	161
4-4.6	Confidence Interval on the Mean	163
4-4.7	General Method for Deriving a Confidence Interval	169
4-5	Inference on the Mean of a Population, Variance Unknown	171
4-5.1	Hypothesis Testing on the Mean	172
4-5.2	P-Value for a t-Test	176
4-5.3	Computer Solution	177
4-5.4	Choice of Sample Size	178
4-5.5	Confidence Interval on the Mean	180
4-6	Inference on the Variance of a Normal Population	183
4-6.1	Hypothesis Testing on the Variance of a Normal Population	183
4-6.2	Confidence Interval on the Variance of a Normal Population	187
4-7	Inference on a Population Proportion	189
4-7.1	Hypothesis Testing on a Binomial Proportion	190
4-7.2	Type II Error and Choice of Sample Size	191
4-7.3	Confidence Interval on a Binomial Proportion	193
4-8	Summary Table of Inference Procedures for a Single Sample	198
4-9	Testing for Goodness of Fit	198
5	Decision Making for Two Samples	210
5-1	Introduction	211
5-2	Inference for a Difference in Means, Variances Known	211
5-2.1	Hypothesis Tests for a Difference in Means, Variances Known	212
5-2.2	Choice of Sample Size	214
5-2.3	Identifying Cause and Effect	215
5-2.4	Confidence Interval on a Difference in Means, Variances Known	216
5-3	Inference for the Difference in Means of Two Normal Distributions, Variances Unknown	221
5-3.1	Hypotheses Tests for the Difference in Means	221
5-3.2	Choice of Sample Size	227
5-3.3	Confidence Interval on the Difference in Means	228
5-3.4	Computer Solution	230
5-4	The Paired t-Test	235
5-5	Inferences on the Variances of Two Normal Populations	243
5-5.1	Tests of Hypotheses on the Ratio of Two Variances	243
5-5.2	Confidence Interval on the Ratio of Two Variances	247
5-6	Inference on Two Population Proportions	250
5-6.1	Large-Sample Test for H[subscript 0]:p[subscript 1] = p[subscript 2]	250
5-6.2	The [beta]-Error and Choice of Sample Size	252
5-6.3	A Confidence Interval for p[subscript 1] - p[subscript 2]	254
5-7	Summary Table for Inference Procedures for Two Samples	256
5-8	What If We Have More Than Two Means?	256
5-8.1	An Example	257
5-8.2	The Analysis of Variance	258
6	Building Empirical Models	275
6-1	Introduction to Empirical Models	275
6-2	Least Squares Estimation of the Parameters	283
6-2.1	Simple Linear Regression	283
6-2.2	Multiple Linear Regression	287
6-3	Properties of the Least Squares Estimators and Estimation of [sigma][superscript 2]	297
6-4	Hypothesis Testing in Linear Regression	301
6-4.1	Test for Significance of Regression	301
6-4.2	Tests on Individual Regression Coefficients	304
6-5	Confidence Intervals in Linear Regression	308
6-5.1	Confidence Intervals on Individual Regression Coefficients	308
6-5.2	Confidence Interval on the Mean Response	309
6-6	Prediction of New Observations	313
6-7	Assessing the Adequacy of the Regression Model	317
6-7.1	Residual Analysis	317
6-7.2	The Coefficient of Multiple Determination	322
6-7.3	Influential Observations	324
7	Design of Engineering Experiments	333
7-1	The Strategy of Experimentation	333
7-2	Some Applications of Experimental Design Techniques	335
7-3	Factorial Experiments	339
7-4	2[superscript k] Factorial Design	344
7-4.1	2[superscript 2] Design	344
7-4.2	Analysis	347
7-4.3	Residual Analysis and Model Checking	351
7-5	2[superscript k] Design for k [greater than or equal] 3 Factors	356
7-6	Single Replicate of a 2[superscript k] Design	364
7-7	Addition of Center Points to a 2[superscript k] Design	369
7-8	Fractional Replication of a 2[superscript k] Design	375
7-8.1	One-Half Fraction of a 2[superscript k] Design	376
7-8.2	Smaller Fractions: 2[superscript k-p] Fractional Factorial Design	383
7-9	Response Surface Methods and Designs	395
7-9.1	Method of Steepest Ascent	397
7-9.2	Analysis of a Second-Order Response Surface	400
7-10	Factorial Experiments with More Than Two Levels	410
8	Statistical Quality Control	426
8-1	Quality Improvement and Statistics	426
8-2	Statistical Quality Control	428
8-3	Statistical Process Control	428
8-4	Introduction to Control Charts	429
8-4.1	Basic Principles	429
8-4.2	Design of a Control Chart	433
8-4.3	Rational Subgroups	435
8-4.4	Analysis of Patterns on Control Charts	436
8-5	X and R control Charts	439
8-6	Control Charts for Individual Measurements	447
8-7	Process Capability	453
8-8	Attribute Control Charts	459
8-8.1	P Chart (Control Chart for Proportions)	459
8-8.2	U Chart (Control Chart for Defects per Unit)	462
8-9	Control Chart Performance	465
	Appendices	473
A.	Statistical Tables and Charts
B.	Bibliography
C.	Answers to Selected Problems
	Index

Editorial Reviews

The authors have made a selection and revision from Montgomery and Runger's more substantial to produce the present text, which is designed to serve a one-semester course for undergraduate engineering students who have had at least one semester of calculus. Meant for an applied course, the text is designed to be used in conjunction with model- building and the computer (by using Minitab, Statgraphics and Excel). All three authors teach at Arizona State University. Annotation c. Book News, Inc., Portland, OR (booknews.com)

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