Ronald D. Snee

Bio: Ronald D. Snee is an academic researcher from DuPont. The author has contributed to research in topics: Six Sigma & Statistical thinking. The author has an hindex of 36, co-authored 119 publications receiving 5917 citations.

Validation of Regression Models: Methods and Examples

Abstract: Methods to determine the validity of regression models include comparison of model predictions and coefficients with theory, collection of new data to check model predictions. comparison of results with theoretical model calculations, and data splitting or cross-validation in which a portion of the data is used to estimate the model coefficients, and the remainder of the data is used to measure the prediction accuracy of the model. An expository review of these methods is presented. It is concluded that data splitting is an effective method of model validation when it is not practical to collect new data to test the model. The DUPLEX algorithm, developed by R. W. Kennard, is recommended for dividing the data into the estimation set and prediction set when there is no obvious variable such as time to use as a basis to split the data. Several examples are included to illustrate the various methods of model validation.

Ridge Regression in Practice

Abstract: Summary The use of biased estimation in data analysis and model building is discussed A review of the theory of ridge regression and its relation to generalized inverse regression is presented along with the results of a simulation experiment and three examples of the use of ridge regression in practice Comments on variable selection procedures, model validation, and ridge and generalized inverse regression computation procedures are included The examples studied here show that when the predictor variables are highly correlated, ridge regression produces coefficients which predict and extrapolate better than least squares and is a safe procedure for selecting variables

Lean Six Sigma – getting better all the time

Abstract: Purpose – The purpose of this paper is to assess Lean Six Sigma to identify important advances over the last ten to 15 years and discuss emerging trends that suggest how the methodology needs to evolve. The goal is to aid those who want to use the method to improve performance as well as assist those developing improvement methodologies.Design/methodology/approach – The use and development of Lean Six Sigma is reviewed including the origins of the method, the what, why and benefits of the method, how the approach is different, the integration of Lean and Six Sigma, implementation mistakes made, lessons learned and developments needed in the future.Findings – It is found that organizations have many different improvement needs that require the objectives and methods contained in the lean and Six Sigma methodologies. It is also found that deployment and sustaining improvements are major issues that can be overcome by building a sustaining infrastructure and making improvement a business process. Critical is...

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

Abstract: (1983). Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. Journal of Quality Technology: Vol. 15, No. 3, pp. 149-153.

Six–Sigma: the evolution of 100 years of business improvement methodology

Abstract: Humankind has always felt a need to improve its condition right since it first came on this planet. Agriculture and athletics are two outstanding examples of the benefits of a constant focus on improvement. Since the early 1900s, there has been a focus on improvement of business performance. Statistical thinking and methods have played a key role in business improvement as they have in agriculture and athletics. Approaches to business improvement have evolved and grown over the years and today the process–focused, statistically–based Six–Sigma methodology is being used by companies such as GE, Honeywell, Motorola, DuPont, American Express, Ford and many others ? large and small ? to improve business performance. This article traces the evolution of business improvement methodology and the use of statistical thinking and methods in improving business performance. It is shown how Six–Sigma has evolved to include the best features of the improvement methods that have come before it. A case study of reducing newspaper errors is included to illustrate the use of Six–Sigma tools and methods.

The Design and Analysis of Experiments

Abstract: 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

Out of the Crisis

Abstract: 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.

A caution regarding rules of thumb for variance inflation factors.

Abstract: The Variance Inflation Factor (VIF) and tolerance are both widely used measures of the degree of multi-collinearity of the ith independent variable with the other independent variables in a regression model. Unfortunately, several rules of thumb – most commonly the rule of 10 – associated with VIF are regarded by many practitioners as a sign of severe or serious multi-collinearity (this rule appears in both scholarly articles and advanced statistical textbooks). When VIF reaches these threshold values researchers often attempt to reduce the collinearity by eliminating one or more variables from their analysis; using Ridge Regression to analyze their data; or combining two or more independent variables into a single index. These techniques for curing problems associated with multi-collinearity can create problems more serious than those they solve. Because of this, we examine these rules of thumb and find that threshold values of the VIF (and tolerance) need to be evaluated in the context of several other factors that influence the variance of regression coefficients. Values of the VIF of 10, 20, 40, or even higher do not, by themselves, discount the results of regression analyses, call for the elimination of one or more independent variables from the analysis, suggest the use of ridge regression, or require combining of independent variable into a single index.

Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter

Abstract: Consider the ridge estimate (λ) for β in the model unknown, (λ) = (X T X + nλI)−1 X T y. We study the method of generalized cross-validation (GCV) for choosing a good value for λ from the data. The estimate is the minimizer of V(λ) given by where A(λ) = X(X T X + nλI)−1 X T . This estimate is a rotation-invariant version of Allen's PRESS, or ordinary cross-validation. This estimate behaves like a risk improvement estimator, but does not require an estimate of σ2, so can be used when n − p is small, or even if p ≥ 2 n in certain cases. The GCV method can also be used in subset selection and singular value truncation methods for regression, and even to choose from among mixtures of these methods.