Statistical Design and Analysis of Experiments: With Applications to Engineering and Science by Robert L. MasonEmphasizes the strategy of experimentation, data analysis, and the interpretation of experimental results.
Features numerous examples using actual engineering and scientific studies. Presents statistics as an integral component of experimentation from the planning stage to the presentation of the conclusions. Deep and concentrated experimental design coverage, with equivalent but separate emphasis on the analysis of data from the various designs. Topics can be implemented by practitioners and do not require a high level of training in statistics. New edition includes new and updated material and computer output.
Introduction to experimental design and analysis of variance (ANOVA)
STAT 5670 - Statistical Design and Analysis of Experiments
Not a MyNAP member yet? Register for a free account to start saving and receiving special member only perks. Some of the most important contributions to the theory and practice of statistical inference in the twentieth century have been those in experimental design. Most of the early development was stimulated by applications in agriculture. The statistical principles underlying design of experiments were largely developed by R. Fisher during his pioneering work at Rothamsted Experimental Station in the s and s.
This task view collects information on R packages for experimental design and analysis of data from experiments. With a strong increase in the number of relevant packages, packages that focus on analysis only and do not make relevant contributions for design creation are no longer added to this task view. Please feel free to suggest enhancements, and please send information on new packages or major package updates if you think they belong here. Contact details are given on my Web page. Experimental design is applied in many areas, and methods have been tailored to the needs of various fields. This task view starts out with a section on the historically earliest application area, agricultural experimentation.
The term experiment is defined as the systematic procedure carried out under controlled conditions in order to discover an unknown effect, to test or establish a hypothesis, or to illustrate a known effect. When analyzing a process, experiments are often used to evaluate which process inputs have a significant impact on the process output, and what the target level of those inputs should be to achieve a desired result output. Experiments can be designed in many different ways to collect this information. Experimental design can be used at the point of greatest leverage to reduce design costs by speeding up the design process, reducing late engineering design changes, and reducing product material and labor complexity. Designed Experiments are also powerful tools to achieve manufacturing cost savings by minimizing process variation and reducing rework, scrap, and the need for inspection.
The design of experiments DOE , DOX , or experimental design is the design of any task that aims to describe or explain the variation of information under conditions that are hypothesized to reflect the variation. The term is generally associated with experiments in which the design introduces conditions that directly affect the variation, but may also refer to the design of quasi-experiments , in which natural conditions that influence the variation are selected for observation.
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Basic, time proven design principles are a key factor for successful industrial experiments today, as they were in the past. One must also take into proper considerations practical aspects and human factors, easier said than done with some characters — and some situations. Simple guidelines may help in the difficult task of selecting a compromise between abstract formulations and real world constraints, a recurrent problem with no general solution. Unable to display preview. Download preview PDF. Skip to main content.