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Engineering Statistics / Edition 3

Engineering Statistics / Edition 3


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The same statistical tools that professional engineers depend on

With a strong emphasis on the statistical techniques most often used in engineering practice, Montgomery, Runger, and Hubele’s ENGINEERING STATISTICS , presents all the key material that engineers need to know in a concise framework. All major aspects of engineering statistics are covered including descriptive statistics, probability and probability distributions, statistical tests and confidence intervals for one and two samples, building regression models, designing and analyzing engineering experiments, and statistical process control.

Revised and enhanced, the Third Edition presents an even better integration of probability and statistics into the overall engineering problem-solving process, including discussion and illustration of retrospective studies, observational studies, and designed experiments.

Highlight of the Third Edition

  • Presents expanded coverage of functions of random variables, transmission of error, and measurement systems capability analysis—important topics for all engineers.

  • Coverage of data display and analysis features expanded use of graphics, including multivariate plots.

  • Thoroughly revised coverage of regression, with an increased emphasis on Minitab, eliminates the need for matrix algebra.

  • All examples and exercises, including many new to this edition, are based on real-world applications of statistics in engineering. Many feature real data from published sources.

  • Provides unusually thorough, yet concise, coverage of regression modeling, design of engineering experiments, and statistical process control.

  • Minitab is well integrated into the text and used for many example solutions.

All data sets are available in electronic form.


Product Details

ISBN-13: 9780471448549
Publisher: Wiley
Publication date: 07/11/2003
Edition description: Older Edition
Pages: 480
Product dimensions: 8.25(w) x 10.22(h) x 0.92(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 from Virginia Polytechnic Institute, all in engineering. From 1969 to 1984, he was a faculty member of the School of Industrial & Systems Engineering at the Georgia Institute of Technology; from 1984 to 1988, he was at the University of Washington, where he held the John M. Fluke Distinguished chair of Manufacturing Engineering, was Professor of Mechanical Engineering, and Director of the Program in industrial Engineering. He has authored and coauthored many technical papers as well as twelve other books. Dr. Montgomery is a Stewart Medalist of the American Society for Quality, and has also received the Brumbaugh Award, the William G. Hunter Award, and the Shewell Award(twice) from the ASQ.

Table of Contents

1The Role of Statistics in Engineering1
1-1The Engineering Method and Statistical Thinking1
1-2Collecting Engineering Data10
1-3Mechanistic and Empirical Models12
1-4Designing Experimental Investigations16
1-5Observing Processes over Time19
2Data Summary and Presentation24
2-1The Importance of Data Summary and Display24
2-2The Stem-and-Leaf Diagram25
2-3The Frequency Distribution and Histogram30
2-4The Box Plot35
2-5Time Sequence Plots37
3Random Variables and Probability Distributions46
3-2Random Variables49
3-4Probability Density Function, Mean, and Variance of a Continuous Random Variable56
3-4.1Probability Density Function56
3-4.2Mean and Variance of a Continuous Random Variable59
3-5Normal Distribution63
3-6Probability Plots75
3-7Probability Mass Function, Mean, and Variance of a Discrete Random Variable79
3-7.1Probability Mass Function80
3-7.2Mean and Variance of a Discrete Random Variable81
3-8Binomial Distribution84
3-9Poisson Process92
3-9.1Poisson Distribution92
3-9.2Exponential Distribution99
3-10Normal Approximation to the Binomial and Poisson Distributions105
3-11Correlation and Independence109
3-12Random Samples, Statistics, and the Central Limit Theorem115
4Decision Making for a Single Sample131
4-1Statistical Inference132
4-2Point Estimation133
4-3Hypothesis Testing140
4-3.1Statistical Hypotheses140
4-3.2Testing Statistical Hypotheses142
4-3.3One-Sided and Two-Sided Hypotheses150
4-3.4General Procedure for Hypothesis Testing152
4-4Inference on the Mean of a Population, Variance Known154
4-4.1Hypothesis Testing on the Mean154
4-4.2P-Values in Hypothesis Testing157
4-4.3Type II Error and Choice of Sample Size158
4-4.4Large-Sample Test161
4-4.5Some Practical Comments on Hypothesis Testing161
4-4.6Confidence Interval on the Mean163
4-4.7General Method for Deriving a Confidence Interval169
4-5Inference on the Mean of a Population, Variance Unknown171
4-5.1Hypothesis Testing on the Mean172
4-5.2P-Value for a t-Test176
4-5.3Computer Solution177
4-5.4Choice of Sample Size178
4-5.5Confidence Interval on the Mean180
4-6Inference on the Variance of a Normal Population183
4-6.1Hypothesis Testing on the Variance of a Normal Population183
4-6.2Confidence Interval on the Variance of a Normal Population187
4-7Inference on a Population Proportion189
4-7.1Hypothesis Testing on a Binomial Proportion190
4-7.2Type II Error and Choice of Sample Size191
4-7.3Confidence Interval on a Binomial Proportion193
4-8Summary Table of Inference Procedures for a Single Sample198
4-9Testing for Goodness of Fit198
5Decision Making for Two Samples210
5-2Inference for a Difference in Means, Variances Known211
5-2.1Hypothesis Tests for a Difference in Means, Variances Known212
5-2.2Choice of Sample Size214
5-2.3Identifying Cause and Effect215
5-2.4Confidence Interval on a Difference in Means, Variances Known216
5-3Inference for the Difference in Means of Two Normal Distributions, Variances Unknown221
5-3.1Hypotheses Tests for the Difference in Means221
5-3.2Choice of Sample Size227
5-3.3Confidence Interval on the Difference in Means228
5-3.4Computer Solution230
5-4The Paired t-Test235
5-5Inferences on the Variances of Two Normal Populations243
5-5.1Tests of Hypotheses on the Ratio of Two Variances243
5-5.2Confidence Interval on the Ratio of Two Variances247
5-6Inference on Two Population Proportions250
5-6.1Large-Sample Test for H[subscript 0]:p[subscript 1] = p[subscript 2]250
5-6.2The [beta]-Error and Choice of Sample Size252
5-6.3A Confidence Interval for p[subscript 1] - p[subscript 2]254
5-7Summary Table for Inference Procedures for Two Samples256
5-8What If We Have More Than Two Means?256
5-8.1An Example257
5-8.2The Analysis of Variance258
6Building Empirical Models275
6-1Introduction to Empirical Models275
6-2Least Squares Estimation of the Parameters283
6-2.1Simple Linear Regression283
6-2.2Multiple Linear Regression287
6-3Properties of the Least Squares Estimators and Estimation of [sigma][superscript 2]297
6-4Hypothesis Testing in Linear Regression301
6-4.1Test for Significance of Regression301
6-4.2Tests on Individual Regression Coefficients304
6-5Confidence Intervals in Linear Regression308
6-5.1Confidence Intervals on Individual Regression Coefficients308
6-5.2Confidence Interval on the Mean Response309
6-6Prediction of New Observations313
6-7Assessing the Adequacy of the Regression Model317
6-7.1Residual Analysis317
6-7.2The Coefficient of Multiple Determination322
6-7.3Influential Observations324
7Design of Engineering Experiments333
7-1The Strategy of Experimentation333
7-2Some Applications of Experimental Design Techniques335
7-3Factorial Experiments339
7-42[superscript k] Factorial Design344
7-4.12[superscript 2] Design344
7-4.3Residual Analysis and Model Checking351
7-52[superscript k] Design for k [greater than or equal] 3 Factors356
7-6Single Replicate of a 2[superscript k] Design364
7-7Addition of Center Points to a 2[superscript k] Design369
7-8Fractional Replication of a 2[superscript k] Design375
7-8.1One-Half Fraction of a 2[superscript k] Design376
7-8.2Smaller Fractions: 2[superscript k-p] Fractional Factorial Design383
7-9Response Surface Methods and Designs395
7-9.1Method of Steepest Ascent397
7-9.2Analysis of a Second-Order Response Surface400
7-10Factorial Experiments with More Than Two Levels410
8Statistical Quality Control426
8-1Quality Improvement and Statistics426
8-2Statistical Quality Control428
8-3Statistical Process Control428
8-4Introduction to Control Charts429
8-4.1Basic Principles429
8-4.2Design of a Control Chart433
8-4.3Rational Subgroups435
8-4.4Analysis of Patterns on Control Charts436
8-5X and R control Charts439
8-6Control Charts for Individual Measurements447
8-7Process Capability453
8-8Attribute Control Charts459
8-8.1P Chart (Control Chart for Proportions)459
8-8.2U Chart (Control Chart for Defects per Unit)462
8-9Control Chart Performance465
A.Statistical Tables and Charts
C.Answers to Selected Problems

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