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Blocking statistical! method

NR, styrene-butadiene mbber (SBR), polybutadiene rubber, nitrile mbber, acrylic copolymer, ethylene-vinyl acetate (EVA) copolymer, and A-B-A type block copolymer with conjugated dienes have been used to prepare pressure-sensitive adhesives by EB radiation [116-126]. It is not necessary to heat up the sample to join the elastomeric joints. This has only been possible due to cross-linking procedure by EB irradiation [127]. Polyfunctional acrylates, tackifier resin, and other additives have also been used to improve adhesive properties. Sasaki et al. [128] have studied the EB radiation-curable pressure-sensitive adhesives from dimer acid-based polyester urethane diacrylate with various methacrylate monomers. Acrylamide has been polymerized in the intercalation space of montmorillonite using an EB. The polymerization condition has been studied using a statistical method. The product shows a good water adsorption and retention capacity [129]. [Pg.866]

The problem of finding the joint distribution of macromolecules P(ti, ti2 mi, m2) for numbers of internal, ( i, 2). and external, (mi, m2), blocks can be easily solved by the statistical method [1,3]. This is possible because the succession of blocks in a macromolecule is described by the ab-... [Pg.190]

In contrast to the above mentioned models, the similar statistical description of the products of the complex-radical copolymerization occurring through the scheme (2.5) has been carried out quite recently [37, 49, 55-60]. Within the framework of this Seiner-Litt model, both copolymer composition [37,49, 55-58] and fractions of the different triads and blocks of the monomer units in the macromolecules were calculated [57]. The probability approaches which were applied in these works, are regarded as being of limited applicability in contrast to the general statistical method [49, 59, 60], By means of the latter method, the sequence distribution and composition inhomogeneity of the copolymer were completely described [49, 60] and also thorough calculations of its microstructure with the account for the tactidty were carried out [59, 60]. [Pg.13]

Besides the descriptive values, it is also interesting to know the correlations between the two groups of variables (rXi,Yj)- The multivariate statistical methods for this data matrix are Canonical Correlation Analysis (CCA) to investigate the relationship between both sets of variables, and Multivariate Regression with a view to predicting the values of the response variables in the Y-block in function of the variables in the X-block, using a mathematical model. [Pg.706]

There are several ways to reduce both type I and type II errors available to researchers. First, one can select a more powerful statistical method that reduces the error term by blocking, for example. This is usually a major goal for researchers and a primary reason they plan the experimental phase of a study in great detail. Second, as mentioned earlier, a researcher can increase the sample size. An increase in the sample size tends to reduce type II error, when holding type I error constant that is, if the alpha error is set at 0.05, increasing the sample size generally will reduce the rate of beta error. [Pg.5]

The most promising new approach in multivariate statistical methods is the PLS (partial least squares in latent variables) method [26, 27, 38, 607 — 610]. Many, even hundreds or thousands of independent variables (the X block) can be correlated with one or several dependent variables (the Y block). PLS analysis is a principal component-like method, with the main difference that the vectors are not indepen-... [Pg.101]

The types of statistieal analytical methods required in this application are often multivariate methods. These statistical procedures are called multivariate when the property being measured, for example, the location of the food, is being related to several variables (such as the signal levels in different miz channels) in the analysis. Multivariate statistical methods can be broadly divided into two types (1) unsupervised, which means that no a priori knowledge of the samples to be classified is required and (2) supervised, which requires a priori knowledge about the samples [18]. A good example of an unsupervised method is principal component analysis (PCA) [19-27], which looks for patterns in a block of data that depend on different variables. PCA provides a useful tool to explore and visualize information, and in particular to identify patterns in complex data, and it is therefore widely used. Applications of PCA in food science will be presented later in this chapter. [Pg.227]


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Method block

Statistical methods

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