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The tools and technique used in the Design of Experiments (DOE) have been proved successful in meeting the challenge of continuous improvement over the last 15 years. However, research has shown that applications of these techniques in small and medium-sized manufacturing companies are limited due to a lack of statistical knowledge required for their effective implementation. Although many books have been written in this subject, they are mainly by statisticians, for statisticians and not appropriate for engineers.Design of Experiments for Engineers and Scientists overcomes the problem of statistics by taking a unique approach using graphical tools. The same outcomes and conclusions are reached as by those using statistical methods and readers will find the concepts in this book both familiar and easy to understand. The book treats Planning, Communication, Engineering, Teamwork and Statistical Skills in separate chapters and then combines these skills through the use of many industrial case studies. Design of Experiments forms part of the suite of tools used in Six Sigma.Key features:* Provides essential DOE techniques for process improvement initiatives* Introduces simple graphical techniques as an alternative to advanced statistical methods - reducing time taken to design and develop prototypes, reducing time to reach the market* Case studies place DOE techniques in the context of different industry sectors* An excellent resource for the Six Sigma training programThis book will be useful to engineers and scientists from all disciplines tackling all kinds of manufacturing, product and process quality problems and will be an ideal resource for students of this topic.Dr Jiju Anthony is Senior Teaching Fellow at the International Manufacturing Unit at Warwick University. He is also a trainer and consultant in DOE and has worked as such for a number of companies including Motorola, Vickers, Procter and Gamble, Nokia, Bosch and a large number of SMEs.

Purpose This paper presents economic and economic-statistical designs of the adaptive exponentially weighted moving average (AEWMA) control chart for monitoring the process mean. It also aims to compare the effect of estimated process parameters on the economic performance of three charts, which are Shewhart, exponentially weighted moving average and AEWMA control charts with economic-statistical design. Design/methodology/approach The optimal parameters of the control charts are obtained by applying the Lorenzen and Vance's (1986) cost function. Comparisons between the economic-statistical and economic designs of the AEWMA control chart in terms of expected cost and statistical measures are performed. Also, comparisons are made between the economic performance of the three competing charts in terms of the average expected cost and standard deviation of expected cost. Findings This paper concludes that taking into account the economic factors and statistical properties in designing the AEWMA control chart leads to a slight increase in cost but in return the improvement in the statistical performance is substantial. In addition, under the estimated parameters case, the comparisons reveal that from the economic point of view the AEWMA chart is the most efficient chart when detecting shifts of different sizes. Originality/value The importance of the study stems from designing the AEWMA chart from both economic and statistical points of view because it has not been tackled before. In addition, this paper contributes to the literature by studying the effect of the estimated parameters on the performance of control charts with economic-statistical design.

Research design is fundamental to all scientific endeavors, at all levels and in all institutional settings. In many social science disciplines, however, scholars working in an interpretive-qualitative tradition get little guidance on this aspect of research from the positivist-centered training they receive. This book is an authoritative examination of the concepts and processes underlying the design of an interpretive research project. Such an approach to design starts with the recognition that researchers are inevitably embedded in the intersubjective social processes of the worlds they study. In focusing on researchers' theoretical, ontological, epistemological, and methods choices in designing research projects, Schwartz-Shea and Yanow set the stage for other volumes in the Routledge Series on Interpretive Methods. They also engage some very practical issues, such as ethics reviews and the structure of research proposals. This concise guide explores where research questions come from, criteria for evaluating research designs, how interpretive researchers engage with \"world-making,\" context, systematicity and flexibility, reflexivity and positionality, and such contemporary issues as data archiving and the researcher's body in the field.

贝叶斯学派是通过综合未知参数的先验信息与样本信息，依据贝叶斯定理，求出后验分布，根据后验分布推断未知参数的统计方法。相比频率派，贝叶斯学派更加灵活、高效。肿瘤新药是全球研发的热点，但同时也存在高失败率的风险。在肿瘤新药早期研发中，高效寻找最佳剂量、优势人群、估计疗效和成功率是医药企业和研究者的共同需求。近年来，肿瘤新药研发呈现化学药物生物制品转变、单药治疗向联合治疗转变、传统设计向创新设计转变等新趋势；伴随出现的各种挑战，包括无法找到最高耐受剂量、延迟毒性、延迟反应、剂量疗效关系变化、剂量组合众多等。基于贝叶斯方法，恰当借用先验信息，能有效帮助企业在肿瘤早期研发中，实现从传统研发模式（高投入、长周期、低效率）向现代研发模式（低投入、短周期、高效率）的转变。研究还进行了贝叶斯方法在肿瘤新药早期研发的进展阐述，与频率派的理念、应用场景的比较分析，可为医药研发的所有从业人员提供宏观、系统的参考。 Bayesian statistics is an approach for learning from evidences as it accumulates, combining prior distribution with current information on a quantity of interest, in which posterior distribution and inferences are being updated each time new data become available using Bayes’ Theorem. Though frequentist approach has dominated medical studies, Bayesian approach has been more and more widely recognized by its flexibility and efficiency. Research and development (R&D) on anti-cancer new drugs have been so hot globally in recent years in spite of relatively high failure rate. It is the common demand of pharmaceutical en

ENABLES READERS TO UNDERSTAND THE METHODS OF EXPERIMENTAL DESIGN TO SUCCESSFULLY CONDUCT LIFE TESTING TO IMPROVE PRODUCT RELIABILITY This book illustrates how experimental design and life testing can be used to understand product reliability in order to enable reliability improvements.The book is divided into four sections.

Background IDeAl (Integrated designs and analysis of small population clinical trials) is an EU funded project developing new statistical design and analysis methodologies for clinical trials in small population groups. Here we provide an overview of IDeAl findings and give recommendations to applied researchers. Method The description of the findings is broken down by the nine scientific IDeAl work packages and summarizes results from the project’s more than 60 publications to date in peer reviewed journals. In addition, we applied text mining to evaluate the publications and the IDeAl work packages’ output in relation to the design and analysis terms derived from in the IRDiRC task force report on small population clinical trials. Results The results are summarized, describing the developments from an applied viewpoint. The main result presented here are 33 practical recommendations drawn from the work, giving researchers a comprehensive guidance to the improved methodology. In particular, the findings will help design and analyse efficient clinical trials in rare diseases with limited number of patients available. We developed a network representation relating the hot topics developed by the IRDiRC task force on small population clinical trials to IDeAl’s work as well as relating important methodologies by IDeAl’s definition necessary to consider in design and analysis of small-population clinical trials. These network representation establish a new perspective on design and analysis of small-population clinical trials. Conclusion IDeAl has provided a huge number of options to refine the statistical methodology for small-population clinical trials from various perspectives. A total of 33 recommendations developed and related to the work packages help the researcher to design small population clinical trial. The route to improvements is displayed in IDeAl-network representing important statistical methodological skills necessary to design and analysis of small-population clinical trials. The methods are ready for use.

This report examines what is known about the relationship between obesity and type 2 diabetes and how future research in these areas might be directed to benefit prevention, interventions, and overall patient care. An international working group of 32 experts in the pathophysiology, genetics, clinical trials, and clinical care of obesity and/or type 2 diabetes participated in a conference held on 6-7 January 2011 and cosponsored by The Endocrine Society, the American Diabetes Association, and the European Association for the Study of Diabetes. A writing group comprising eight participants subsequently prepared this summary and recommendations. Participants reviewed and discussed published literature and their own unpublished data. The writing group unanimously supported the summary and recommendations as representing the working group's majority or unanimous opinions. The major questions linking obesity to type 2 diabetes that need to be addressed by combined basic, clinical, and population-based scientific approaches include the following: 1) Why do not all patients with obesity develop type 2 diabetes? 2) Through what mechanisms do obesity and insulin resistance contribute to β-cell decompensation, and if/when obesity prevention ensues, how much reduction in type 2 diabetes incidence will follow? 3) How does the duration of type 2 diabetes relate to the benefits of weight reduction by lifestyle, weight-loss drugs, and/or bariatric surgery on β-cell function and glycemia? 4) What is necessary for regulatory approval of medications and possibly surgical approaches for preventing type 2 diabetes in patients with obesity? Improved understanding of how obesity relates to type 2 diabetes may help advance effective and cost-effective interventions for both conditions, including more tailored therapy. To expedite this process, we recommend further investigation into the pathogenesis of these coexistent conditions and innovative approaches to their pharmacological and surgical management.

Ultra-high-performance concrete (UHPC) is characterized by a dense microstructure that yields ultra-high strength and durability properties. This paper presents an innovative method to produce eco-efficient mixtures of UHPC using locally available materials based on optimization using the packing density and a statistical mixture design approach. The results showed an optimal packing density of 0.79% for a combination of all granular materials (quartz sand, quartz powder, cement, and silica fume). The study presents experimentally based models with high coefficients of correlation in predicting the workability and strength of UHPC as a function of mixture design parameters (water-binder ratio [w/b] and high-range water-reducing admixture [HRWRA] dosage). Contour diagrams to facilitate the use of the models were established. In this research, UHPC mixtures with a slump flow between 130 and 300 mm (5.1 and 11.8 in.) and compressive strength between 135 and 225 MPa (19.6 and 32.6 ksi) were produced; such concretes are required for different industrial applications. Keywords: design; packing density; statistical design approach; ultra-high-performance concrete (UHPC).

Background Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) is widely used for quantitative proteomic investigations. The typical output of such studies is a list of identified and quantified peptides. The biological and clinical interest is, however, usually focused on quantitative conclusions at the protein level. Furthermore, many investigations ask complex biological questions by studying multiple interrelated experimental conditions. Therefore, there is a need in the field for generic statistical models to quantify protein levels even in complex study designs. Results We propose a general statistical modeling approach for protein quantification in arbitrary complex experimental designs, such as time course studies, or those involving multiple experimental factors. The approach summarizes the quantitative experimental information from all the features and all the conditions that pertain to a protein. It enables both protein significance analysis between conditions, and protein quantification in individual samples or conditions. We implement the approach in an open-source R-based software package MSstats suitable for researchers with a limited statistics and programming background. Conclusions We demonstrate, using as examples two experimental investigations with complex designs, that a simultaneous statistical modeling of all the relevant features and conditions yields a higher sensitivity of protein significance analysis and a higher accuracy of protein quantification as compared to commonly employed alternatives. The software is available at http://www.stat.purdue.edu/~ovitek/Software.html .

Nanoparticles generated by recombinant technologies are receiving increased interest in several applications, particularly the use of virus like particles (VLPs) for the generation of safer vaccines. The characterization and quantification of these nanoparticles with complex structures is very relevant for a better comprehension of the production systems and should circumvent the limitations of the most conventional quantification techniques often used. Here, we applied confocal microscopy, flow virometry and nanoparticle tracking analysis (NTA) to assess the production process of Gag virus-like particles (VLPs) in the Sf9 cell/baculovirus expression vector system (BEVS). These novel techniques were implemented in an optimization workflow based on Design of Experiments (DoE) and desirability functions to determine the best production conditions. A higher level of sensitivity was observed for NTA and confocal microscopy but flow virometry proved to be more accurate. Interestingly, extracellular vesicles were detected as an important source of contamination of this system. The synergistic interplay of viable cell concentration at infection (CCI), multiplicity of infection (MOI) and time of harvest (TOH) was assessed on five objective responses: VLP assembly, baculovirus infection, VLP production, cell viability and VLP productivity. Two global optimal conditions were defined, one targeting the maximal yield of VLPs and the other providing a balance between production and assembled VLPs. In both cases, a low MOI proved to be the best condition to achieve the highest VLP production and productivity yields. Cryo-EM analysis of nanoparticles produced in these conditions showed the typical size and morphology of HIV-1 VLPs. This study presents an integrative approach based on the combination of DoE and direct nanoparticle quantification techniques to comprehensively optimize the production of VLPs and other viral-based biotherapeutics.