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

According to W. Edwards Deming, American companies require nothing less than a transformation of management style and of governmental relations with industry. In Out of the Crisis, originally published in 1982, Deming offers a theory of management based on his famous 14 Points for Management. Management's failure to plan for the future, he claims, brings about loss of market, which brings about loss of jobs. Management must be judged not only by the quarterly dividend, but by innovative plans to stay in business, protect investment, ensure future dividends, and provide more jobs through improved product and service. In simple, direct language, he explains the principles of management transformation and how to apply them.

Out of the Crisis

Statistical Analysis with Missing Data

Economic Control of Quality of Manufactured Product.

http://www.epa.gov/oppfead1/labeling/lrm/ individual chapters of the Pesticide Label Review Manual, 3rd Edition Pesticide Label Review Manual, 3rd Edition. Utilized by the EPA Office of Pesticide Programs (OPP) to improve the quality and consistency of pesticide labels. This document provides guidance on how the US EPA interprets the Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA) and its specific requirements for label language and format. Chapters include: • Precautionary labeling (including Signal Word, Personal Protective Equipment, and First Aid Statements) • Physical or Chemical Hazards • Worker Protection Labeling • Directions for Use • Labeling Claims • Storage and Disposal • Identification numbers (including EPA registration number)

Appendix A: informational sources on pesticides and health.

Control chart limits are often calculated using parameter estimates from an in-control Phase I reference sample. In Phase II monitoring, statistics based on new samples are compared with the estimated control limits to monitor for departures from the in..

Effects of Parameter Estimation on Control Chart Properties; A Literature Review

﻿Profile monitoring is used when the product or process quality is best represented by a function at each time period. Often some parametric method is used and then the estimated parameters are monitored over time to determine if there have been changes..

Monitoring correlation within linear profiles using mixed models

Profile monitoring is a quality control technique best used when the process data follow a profile at each time period. Little work has been done on the monitoring of nonlinear profiles, and that work has assumed that the measurements within a profile a..

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Profile Monitoring via Nonlinear Mixed Models

In some statistical process control applications, the quality of a process or product can be characterized by a relationship between a response variable and one explanatory variable, which is referred to as profile. We give an example here of a profile that can be described using a polynomial model. This example comes from the automotive industry, where one of the most important quality characteristics of an automobile engine is the relationship between the torque produced by an engine and the engine speed in revolutions per minute. We find for this data set that a second-order polynomial works well. In addition, we show that there is autocorrelation within each profile, thus an ordinary least-square method that ignores the autocorrelation is inappropriate. We propose a linear mixed model method as an alternative approach. After the reduction of the data to a series of parameter estimates, we then conduct a step-by-step Phase I analysis of the polynomial profiles monitoring using a T2-based procedure to check the stability of the process and whether or not there are outlying profiles. The remaining profiles are used to form the estimated mean vector and variance–covariance matrix to be used in Phase II studies. Finally, a brief discussion is presented to show how one can use these parameters in Phase II. Copyright © 2009 John Wiley & Sons, Ltd.

A case study on monitoring polynomial profiles in the automotive industry

A goal of Phase I analysis of multivariate data is to identify multivariate outliers and step changes so that the Phase II estimated control limits are sufficiently accurate. High breakdown estimation methods based on the minimum volume ellipsoid (MVE) or the minimum covariance determinant (MCD) are well suited for detecting multivariate outliers in data. As a result of the inherent difficulties in their computation, many algorithms have been proposed to detect multivariate outliers. Due to their availability in standard software packages, we consider the subsampling algorithm to obtain the MVE estimators and the FAST-MCD algorithm to obtain the MCD estimators. Previous studies have not clearly determined which of these two available estimation methods is best for control chart applications. The comprehensive simulation study presented in this paper gives guidance for the correct use of each estimator. Control limits are provided. High breakdown estimation methods based on the MCD and MVE approaches can be applied to a wide variety of multivariate quality control data. Copyright © 2006 John Wiley & Sons, Ltd.

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Willis A. Jensen

Papers

Effects of Parameter Estimation on Control Chart Properties; A Literature Review

Monitoring correlation within linear profiles using mixed models

Profile Monitoring via Nonlinear Mixed Models

A case study on monitoring polynomial profiles in the automotive industry

High breakdown estimation methods for Phase I multivariate control charts