Handbook of Statistics 22: Statistics in IndustryEdited by
- R. Khattree, Oakland University, Department of Mathematics and Statistics, Rochester, MI, USA
- C.R. Rao, The Pennyslvania State University, Department of Statistics, PA, USA
This volume presents a state of the art exposition of topics in the industrial statistics. It serves as an invaluable reference for the researchers in industrial statistics/industrial engineering and an up to date 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.
Practicing industrial statisticians and industrial engineers. Various industries and their libraries. Research institutions. University/college libraries.
Handbook of Statistics
Published: June 2003
This is an excellent addition to the Handbook of Statistics series (...) the book is definitely recommended.
Thomas P. Ryan, Journal of Quality Technology, 2004
Part I. Statistics in Research and Development.
Guidelines for Selecting Factors and Factor Levels for an Industrial Designed Experiment (V. Czitrom).
Industrial Experimentation for Screening (D.K.J. Lin).
The Planing and Analysis of Industrial Selection and Screening Experiments (G. Pan, T.J. Santner, D.M. Goldman).
Uniform Experimental Designs and their Applications in Industry (K.T. Fang, D.K.J. Lin).
Mixed Models and Repeated Measures: Some Illustrative Industrial Examples (G.A. Milliken).
Current Modeling and Design Issues in Response Surface Methodology: GLMs and Models with Block Effects (A.I. Khuri).
A Review of Design and Modeling in Computer Experiments (V.C.P. Chen, K.L. Tsui, R.R. Barton, J.K. Allen).
Quality Improvement and Robustness via Design of Experiments (B.E. Ankenman, A.M. Dean).
Software to Support Manufacturing Experiments (J.C. Reece).
Statistics in Semiconductor Industry (V. Czitrom).
PREDICT: A New Approach to Product Development and Lifetime Assessment Using Information Integration Technology (J.M. Booker, T.R. Bement, M.A. Meyer, W.J. Kerscher III).
The Promise and Challenge of Mining Web Transaction Data (S.R. Dalal, D. Egan, Y. Ho, M. Rosenstein).
Part II. Statistics In On-line Industrial Processes.
Control chart schemes for monitoring the mean and variance of the processes subject to sustained shifts and drifts (Z.G. Stoumbos, M.R. Reynolds Jr., W.H. Woodall).
Multivariate Control Charts: Hotelling-T2, Data Depth and Beyond, R.Y. Liu).
Effective Sample Sizes for T2 Control Charts (R.L. Mason, Y.M. Chou, J.C. Young).
Multidimensional Scaling in Process Control (T.F. Cox).
Quantifying the Capability of Industrial Processes (A.M. Polansky, S.N.U.A. Kirmani).
Taguchi's Approach to On-line Control Procedure (M.S. Srivastava, Y. Wu).
Dead-Band Adjustment Schemes for On-line Feedback Quality Control (A. Luceño).
Part III. Measurement Processes.
Statistical Calibration and Measurements (H. Iyer).
Subsampling Designs in Industry: Statistical Inference for Variance Components (R. Khattree).
Repeatability, Reproducibility and Interlaboratory Studies (R. Khattree).
Tolerancing - Approaches and Related Issues in Industry (T.S. Arthanari).
Part IV. Statistical Inferential Techniques Useful in Industrial Applications.Part V. Software Reliability.
Goodness-of-fit Tests for Univariate and Multivariate Normal Models (D.K. Srivasatava, G.S. Mudholkar).
Normal Theory Methods and their Simple Robust Analogs for Univariate and Multivariate Linear Models (D.K. Srivastava, G.S. Mudholkar).
Diagnostic Methods for Univariate and Multivariate Normal Data (D.N. Naik).
Dimension Reduction Methods Used in Industry (G. Merola, B. Abraham).
Growth and Wear Curves (A.M. Kshirsagar).
Time Series in Industry and Business (B. Abraham, N. Balakrishna).
Stochastic Process Models for Reliability in Dynamic Environments (N.D. Singpurwala, T.A. Mazzuchi, S. Özekici, R. Soyer).
Bayesian Inference for the Number of Undetected Errors (S. Basu).