THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Economic Control of Quality of Manufactured Product.

In least-squares fitting it is important to understand the influence which a data y value will have on each fitted y value. A projection matrix known as the hat matrix contains this information and, together with the Studentized residuals, provides a means of identifying exceptional data points. This approach also simplifies the calculations involved in removing a data point, and it requires only simple modifications in the preferred numerical least-squares algorithms.

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The Hat Matrix in Regression and ANOVA

A bewilderingly large number of statistical quantities have been proposed to study outliers and influence of individual observations in regression analysis. In this article we describe the inter-relationships which exist among the proposed measures. An examination of these relationships leads us to conclude that only three of these measures along with some graphical displays can provide an analyst a complete picture of outliers (major discrepant points) and points which excessively influence the fitted regression equation. Illustrative examples based on real data are presented.

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Influential Observations, High Leverage Points, and Outliers in Linear Regression

Statistical Models in Engineering

Design of experiments and analysis of data from designed experiments are well-established methodologies in which statisticians are formally trained. Another critical and rarely taught skill is the planning that precedes designing an experiment. This article suggests a set of tools for presenting generic technical issues and experimental features found in industrial experiments. These tools are predesign experiment guide sheets to systematize the planning process and to produce organized written documentation. They also help experimenters discuss complex trade-offs between practical limitations and statistical preferences in the experiment. A case study involving the (computer numerical control) CNC-machining of jet engine impellers is included.

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A systematic approach to planning for a designed industrial experiment

Part 1 Statistical process control: development of SPC what SPC is and is not online SPC methods off-line process control SPC methodolgy other factors affecting the success of SPC. Part 2 Some basic distributions: attribute data countable data geometric distribution the Normal distribution distributions derived from the Normal distribution application of results testing for normality the Normal approximation to the binomial distribution Normal approximation to the Poisson distribution. Part 3 Process variation: reasons for process variation types of process variation some models for process variation sampling error and measurement error. Part 5 Basic Shewhart control charts for continuous variables: control charts for average level charts for control of (within group) process spread the average run length (ARL) special problems some theoretical results for Shewhart charts charts for control of process spread. Part 6 Extensions to Shewhart charts for one-at-a-time data: one-at-a-time sampling estimation of sigma for one-at-a-time data details of further control charts for control of process average level control of process spread choice of charting method practical use of Shewhart and moving average charts properties of EWMA and MA charts. Part 7 Cumulative sum techniques for continuous variables: CuSum charts - for control of average level, for control of process spread nomogram for CuSums. Part 8 Further theoretical results on control charts for continuous variables: the effect of departures from assumption on moments of x and s(2) Shewhart charts - Markov chain approach cumulative Sum charts charts for control of process spread. Part 9 The design of control charts for specification limits: single specification limits - chart for means double specification limits - high capability processes, an alternative approach. Part 10 Control of discrete data processes: Shewhart charts - for countable data (c and u), for attribute data (np and p) CuSum charts for countable data, for attribute data comparison of Shewhart and CuSum schemes. Part 11 Sampling inspection: classification of inspection plans some properties of sampling plans methods of using sampling plans for attributes. Part 12 Inspection by variables: single specification limit - sigma known, sigma unknown estimation of fraction non-conforming, single specification limit double specification limit - sigma known, sigma unknown multivariate sampling plans. Part 13 Standard sampling systems: statement of method for inspection by attributes inspection by variables International Standards for process and quality control. Part 14 Adaptive sampling plans: CSP-1 and the AOQL criterion theory of CSP-1 the AEDL criterion. (Part contents).

Statistical Process Control—Theory and Practice

Acknowledgments. Introduction. DETECTION AND QUANTIFICATION IN THE LABORATORY. Conceptual Foundations. Statistical Foundations and Review. Calibration-Based Regression Models. Single-Concentration-Based Detection Limit Methods. Single-Concentration-Based Quantification Limit Methods. Calibration-Based Detection Limit Methods. Calibration-Based Quantification Limit Methods. Significant Digits. Experimental Design of Detection and Quantification Limit Studies and Related Studies. Between-Laboratory Detection and Quantification Limit Estimators. DETECTION AND QUANTIFICATION IN THE FIELD. Comparison of a Single Measurement to a Regulatory Standard. Censored Data. Testing Distributional Assumptions. Testing for Outliers. Detecting Trend. Detection Monitoring. Assessment and Corrective Action Monitoring: Overview. Assessment and Corrective Action Monitoring: Comparison to a Standard. Assessment and Corrective Action Monitoring: Comparison to Background. Assessment and Corrective Action Monitoring: Case Studies. Review of Available Computer Software. Summary. Appendix: Land's Tables. Glossary of Measurement Terminology. Mathematical Symbols. Web References. Annotated Bibliography. Index.

Statistical Methods for Detection and Quantification of Environmental Contamination

Calculation of detection limits for a single-laboratory ion-chromatographic method to determine parts-per-trillion ions in ultrapure water

(1997) A Comparison of Statistical Process Control and Engineering Process Control Journal of Quality Technology: Vol 29, No 2, pp 128-130

David E. Coleman

Papers

A systematic approach to planning for a designed industrial experiment

Statistical Process Control—Theory and Practice

Statistical Methods for Detection and Quantification of Environmental Contamination

Calculation of detection limits for a single-laboratory ion-chromatographic method to determine parts-per-trillion ions in ultrapure water

A Comparison of Statistical Process Control and Engineering Process Control