Statistics in Industry, Volume 22Ravindra Khattree, Calyampudi Radhakrishna Rao This volume presents an exposition of topics in industrial statistics. It serves as a reference for researchers in industrial statistics/industrial engineering and a source of information for practicing statisticians/industrial engineers. A variety of topics in the areas of industrial process monitoring, industrial experimentation, industrial modelling and data analysis are covered and are authored by leading researchers or practitioners in the particular specialized topic. Targeting the audiences of researchers in academia as well as practitioners and consultants in industry, the book provides comprehensive accounts of the relevant topics. In addition, whenever applicable ample data analytic illustrations are provided with the help of real world data. |
Contents
Guidelines for Selecting Factors and Factor Levels for an Industrial Designed | 3 |
Dean Department of Statistics Ohio State University Columbus OH 43210 | 8 |
jreecepcisys | 9 |
Guidelines for selecting factor levels | 12 |
S N U A Kirmani Department of Mathematics University of Northern Iowa Cedar | 17 |
Guidelines for relationships between factors | 18 |
Guidelines for variables held constant or not controlled | 24 |
Summary | 30 |
Taguchis Approach to Online Control Procedure | 657 |
Improvement under random walk model without measurement error | 663 |
Online control under random walk model with measurement error | 671 |
Robustness under nonnormality | 678 |
Concluding remarks | 685 |
DeadBand Adjustment Schemes for Online Feedback Quality | 695 |
Dynamics of the adjustment system | 701 |
Exact characterization and evaluation of the AAI | 707 |
Supersaturated designs | 44 |
Computer experiments | 54 |
Dispersion effects in screening designs | 65 |
The Planning and Analysis of Industrial Selection and Screening | 75 |
Singlefactor experiments to screen treatments based on means | 85 |
Selection and screening for completely randomized experiments | 96 |
Selection and screening for factorial experiments with randomization restrictions | 103 |
Screening and selection concerning variability | 123 |
Uniform Experimental Designs and their Applications in Industry | 131 |
Uniform design in other environments | 137 |
Computer experiments | 149 |
The connections between uniformity and other designs | 158 |
Summary and discussion | 165 |
Some Illustrative Industrial | 171 |
Models for data from experiments with multiple processing steps | 180 |
Baking cookies example of a twostep process using a stripplot design | 187 |
Semiconductors example of repeated measures in a splitsplitplot design | 199 |
Discussion | 206 |
Generalized linear models | 215 |
Dealing with the design dependence problem | 223 |
A Review of Design and Modeling in Computer Experiments | 231 |
Experimental design issues | 244 |
Discussion | 255 |
Quality Improvement and Robustness via Design of Experiments | 263 |
Models for parameter design | 269 |
Choice of plans for parameter design | 283 |
Followup experiments | 289 |
Analysis for controlled noise factors | 298 |
Related topics | 304 |
Software to Support Manufacturing Experiments | 319 |
Summary of software evaluations | 333 |
Appendix Demonstrations of products evaluated | 345 |
Conclusions and acknowledgments | 456 |
Gauge studies | 471 |
Designed experiments | 483 |
Reliability | 494 |
The applications | 501 |
Statistical issues and analysis | 509 |
Conclusions | 520 |
Need for and complexity of measurements | 526 |
Latent semantic indexing LSI applied to Web transactions | 537 |
Emerging challenges for ecommerce data mining | 544 |
Control Chart Schemes for Monitoring the Mean and Variance of Processes | 553 |
Statistical measures of control chart performance | 561 |
An illustrative example of control chart plots | 567 |
Hotelling T2 Data Depth and Beyond | 573 |
Concluding remarks | 587 |
Effective Sample Sizes for T2 Control Charts | 595 |
Sample size in Phase II | 604 |
Data for analysis | 611 |
Biplots | 618 |
Quantifying the Capability of Industrial Processes | 625 |
Nonparametric and robust process capability | 645 |
Evaluation of the AAI and MSD under pure delayed dynamics | 713 |
Robustness of the DBA schemes and the EWMA forecasts | 719 |
Statistical Calibration and Measurements | 731 |
Regulation | 741 |
Multipleuse confidence regions | 747 |
Concluding remarks | 760 |
Balanced nested designs | 766 |
Negative estimates | 773 |
Interval estimation | 783 |
Concluding remarks | 791 |
Statistical models to assess repeatability and reproducibility | 798 |
Interval estimation of repeatability and reproducibility | 815 |
Tolerancing Approaches and Related Issues in Industry | 823 |
Geometric and kinematic variations | 832 |
Statistical tolerance analysis | 835 |
Measurement tolerancing and consumers risk consideration | 859 |
Goodnessoffit Tests for Univariate and Multivariate Normal Models | 869 |
illustrations | 884 |
illustrations | 899 |
Normal Theory Methods and their Simple Robust Analogs for Univariate | 907 |
univariate case | 916 |
Multivariate normal theory tests | 928 |
Robust test procedures | 939 |
Examples | 948 |
Diagnostic Methods for Univariate and Multivariate Normal Data | 957 |
Multivariate normal data | 974 |
Multivariate linear regression data | 980 |
An example | 986 |
Dimension Reduction Methods Used in Industry | 995 |
Distributional issues | 1020 |
Statistical process control | 1025 |
Conclusions | 1035 |
Multivariate data | 1040 |
Growth and Wear Curves | 1041 |
Nonparametric methods of the analysis of growth data | 1049 |
Time Series in Industry and Business | 1055 |
ARIMA model building | 1062 |
Forecasts from ARIMA models | 1070 |
Interventions outliers and missing observations | 1074 |
Statespace models and the Kalman filter | 1081 |
Other nonlinear time series models | 1090 |
Longmemory processes | 1100 |
Summary and concluding remarks | 1102 |
Stochastic Process Models for Reliability in Dynamic Environments | 1109 |
The Markov renewal the semiMarkov and the Markov additive processes | 1116 |
The nonhomogeneous and the doubly stochastic Poisson point processes | 1124 |
Bayesian Inference for the Number of Undetected Errors | 1131 |
Heterogeneity among the errors | 1137 |
Heterogeneity of both reviewers and errors | 1143 |
| 1151 | |
Contents of Previous Volumes | 1165 |
Common terms and phrases
adjustment algorithm analysis ANOVA approach approximation assumption C. R. Rao calibration central composite design columns combination confidence intervals considered control charts control factors control limits corresponding covariance covariance matrix D-optimal defined degrees of freedom denote designed experiment detection dispersion effects engineering error estimate EWMA example experimental design factor levels Fang fractional factorial designs function given graph Khattree linear models main effects matrix measure methods Minitab Mudholkar multivariate normality noise factors normal distribution observations obtained optimal option orthogonal arrays outliers p-value parameters performance plot points problem procedure process capability indices Q-Q plot Quality Technology regression replication residuals response surface runs sample screening Section selected Shewhart simulation specification Srivastava standard deviation Statist supersaturated designs Table Taguchi Technometrics temperature tolerance transformation treatment two-factor interactions uniform design univariate values variance components vector wafer