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

The author's preface gives an outline: "This book is about weakconvergence methods in metric spaces, with applications sufficient to show their power and utility. The Introduction motivates the definitions and indicates how the theory will yield solutions to problems arising outside it. Chapter 1 sets out the basic general theorems, which are then specialized in Chapter 2 to the space C[0, l ] of continuous functions on the unit interval and in Chapter 3 to the space D [0, 1 ] of functions with discontinuities of the first kind. The results of the first three chapters are used in Chapter 4 to derive a variety of limit theorems for dependent sequences of random variables. " The book develops and expands on Donsker's 1951 and 1952 papers on the invariance principle and empirical distributions. The basic random variables remain real-valued although, of course, measures on C[0, l ] and D[0, l ] are vitally used. Within this framework, there are various possibilities for a different and apparently better treatment of the material. More of the general theory of weak convergence of probabilities on separable metric spaces would be useful. Metrizability of the convergence is not brought up until late in the Appendix. The close relation of the Prokhorov metric and a metric for convergence in probability is (hence) not mentioned (see V. Strassen, Ann. Math. Statist. 36 (1965), 423-439; the reviewer, ibid. 39 (1968), 1563-1572). This relation would illuminate and organize such results as Theorems 4.1, 4.2 and 4.4 which give isolated, ad hoc connections between weak convergence of measures and nearness in probability. In the middle of p. 16, it should be noted that C*(S) consists of signed measures which need only be finitely additive if 5 is not compact. On p. 239, where the author twice speaks of separable subsets having nonmeasurable cardinal, he means "discrete" rather than "separable." Theorem 1.4 is Ulam's theorem that a Borel probability on a complete separable metric space is tight. Theorem 1 of Appendix 3 weakens completeness to topological completeness. After mentioning that probabilities on the rationals are tight, the author says it is an

Convergence of probability measures

Simulation modelling involves the development of models that imitate realworld operations, and statistical analysis of their performance with a view to improving efficiency and effectiveness This nontechnical textbook is focused towards the needs of business, engineering and computer science students

Simulation: The Practice of Model Development and Use

This article reviews Kriging (also called spatial correlation modeling). It presents the basic Kriging assumptions and formulas, contrasting Kriging and classic linear regression metamodels. Furthermore, it extends Kriging to random simulation, and discusses bootstrapping to estimate the variance of the Kriging predictor. Besides classic one-shot statistical designs such as Latin Hypercube Sampling, it reviews sequentialized and customized designs. It ends with topics for future research.

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Kriging Metamodeling in Simulation: A Review

Stochastic simulation of risk factor potential effects for software development risk management

Understanding the effects of requirements volatility in software engineering by using analytical modeling and software process simulation

As the cost and complexity of constructing a semiconductor fabrication facility increases, responsive tools are needed for designing and planning its operations. Discrete-event simulation paired with design of experiments is an effective combination. This article demonstrates how simulation in combination with design of experiments is used to compare the intrabay layout of two automated material handling systems. The difference in stocker robot utilization, number of vehicle moves per hour, and average delivery time for the two intrabay layouts will be compared using a fractional factorial experimental design. The study demonstrates that the distributed storage option is preferable for maximizing manufacturing performance. The solution procedure has general applicability as a tutorial for practitioners.

/pdf/a-simulation-based-experiment-for-comparing-amhs-performance-4y8o9iyyao.pdf

A simulation-based experiment for comparing AMHS performance in a semiconductor fabrication facility

A cycle time-throughput curve quantifies the relationship of average cycle time to throughput rates in a manufacturing system. Moreover, it indicates the asymptotic capacity of a system. Such a curve is used to characterize system performance over a range of start rates. Simulation is a fundamental method for generating such curves since simulation can handle the complexity of real systems with acceptable precision and accuracy. A simulation-based cycle time-throughput curve requires a large amount of simulation output data; the precision and accuracy of a simulated curve may be poor if there is insufficient simulation data. To overcome these problems, sequential simulation experiments based on a nonlinear D-optimal design are suggested. Using the nonlinear shape of the curve, such a design pinpointsp starting design points, and then sequentially ranks the remainingn --p candidate design points, wheren is the total number of possible design points being considered. A model of a semiconductor wafer fabrication facility is used to validate the approach. The sequences of experimental runs generated can be used as references for simulation experimenters.

D-Optimal Sequential Experiments for Generating a Simulation-Based Cycle Time-Throughput Curve

The application of simulation as a performance estimation tool in automated material handling system design is well documented, as is the amount of time required to build, debug, and analyze a typical AMHS simulation model. In the rapid growth, high technology industry, it is infeasible to continually employ sufficient staff to create unique models of all of the possible scenarios that require examination. Typically, the model is not complete before the project requirements have been modified. An alternative to unique model creation is to reuse an existing generic model. Generic models are similar to a group of software tools called simulators: software packages that contain a pre-programmed model. Investigation has indicated that a special purpose reusable generic model, designed to address the set of issues faced by a specific commercial entity, is efficient and necessary for fast model turnaround. If correctly developed, the generic model could be reused, thereby reducing model building time as well as increasing simulation accuracy. The paper discusses the use of such a model and illustrates the improvement to model build cycle time.

/pdf/effective-simulation-model-reuse-a-case-study-for-amhs-3ctdx52mfz.pdf

Gerald T. Mackulak

Papers

Stochastic simulation of risk factor potential effects for software development risk management

Understanding the effects of requirements volatility in software engineering by using analytical modeling and software process simulation

A simulation-based experiment for comparing AMHS performance in a semiconductor fabrication facility

D-Optimal Sequential Experiments for Generating a Simulation-Based Cycle Time-Throughput Curve

Effective simulation model reuse: a case study for AMHS modeling