Titlebook: Design and Analysis of Experiments; Angela Dean,Daniel Voss,Danel Draguljić Textbook 2017Latest edition Springer International Publishing

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Designs with Two Blocking Factors, as simple examples of row-column designs. The model and an overview of analysis of variance, confidence intervals, and multiple comparisons for general row-column design is provided. The analysis simplifications that occur in Latin square and Youden designs are then described. The chapter provides

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Fractional Factorial Experiments,combinations. Some main-effect and interaction contrasts cannot then be distinguished and are said to be aliased. Such fractional factorial experiments form the topic of in this chapter 15, where two methods of design construction are discussed to enable minimum aliasing between important contrasts.

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Response Surface Methodology,e surface methodology include the determination of variable settings for which the mean response is optimized and the estimation of the response surface in the vicinity of this good location. The first part this chapter discusses first-order designs and first-order models, including lack of fit and

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Random Effects and Variance Components,r are known as random effects. Random-effects models are discussed in this chapter. Of main concern is the variability of the effects of all the levels in the population rather than comparison of the effects of the few levels observed. The sample size calculation and modeling are introduced via expe

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Nested Models,hapter discusses how to recognize nesting when it occurs, how to formulate the associated models, and how to analyze the effects in these models. For fixed-effects nested models, the estimable contrasts are identified and the corresponding confidence intervals and hypothesis tests are developed. Est

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Split-Plot Designs,n a split-plot design, the experimental units are called split plots, and are nested within whole plots, which themselves may or may not be nested within blocks. The split plots within each whole plot are assigned at random to the levels of one or more of the treatment factors. The levels of other t

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https://doi.org/10.1007/978-3-319-52250-0Analysis of Experiments; Analysis of covariance; Blocking factors; Design of Experiments; Factorial expe