Project SCORE

TABLE 129—5. The International Prognostic Factors Project Score for Advanced Hodgkin s Lymphoma... 

The first communication satellite. Project SCORE, was launched December 18, 1958, and the first message broadcast was a holiday peace message from President Dwight Eisenhower. 

The relationships among samples are revealed by their projections (scores) on the latent variables. This information is displayed in bivariate scoreplots. Similar samples group together in the score plots. The orthonormality between latent variable Wa vectors means that the distance between samples represents a quantitative measure of relatedness. Standardized scores can be used. In most instances, however, scores weighted in accordance with the size (proportional to ta ) of the latent variables are preferred. These are the scores used in Equation 6.1. Score plots are very useful for visual detection of atypical samples, that is, outliers. 

According to Mr. Yung, a senior quantity surveyor of a government department, the PASS used in the Housing Authority consists of two scores a project score and a contractor score. The project score reflects the performance of the contractor on a particular project during the construction stage and maintenance period. The project score includes ... 

The coordinate of an object when projected onto an axis given by a principal component is called its score. Scores arc usually denoted by Tl, T2,. ... Figure 9-7 is a sketch of a score plot the points are the objects in the coordinate system... 

Each factor is evaluated on the basis of a score of 0—10. A total score of 80 points or higher statistically has resulted in eight or nine successes out of ten instances. Below 80 points, the success-to-failure ratio for new development drops off sharply. Such analyses can be made not only initially but periodically during an R D project and as the business develops. 

Recall that the projections are often called the scores. Thus, another way of expressing equation [65] is "the scores of the spectral data points are directly... 

The whole idea behind PLS is to try to restore, to the extent possible, the optimum congruence between the each spectral factor and its corresponding concentration factor. For the purposes of this concept, optimum congruence is defined as a perfectly linear relationship between the projections, or scores, of the spectral and concentration data onto the spectral and concentration factors as exemplified in Figure 73. Since the spectral noise is independent from the concentration noise, a perfectly linear relationship is no longer possible. So, the best we can do is restore optimum congruence in the least-squares sense. 

Fig. 31.3. (a,b) Reproduction of distances D and angular distances 0 in a score plot (a = 1) or loading plot (p = 1) in the common factor-space (c,d) Unipolar axis through the representation of a row or column and through the origin 0 of space. Reproduction of the data X is obtained by perpendicular projection of the column- or row-pattern upon the unipolar axis (a + P = 1). (e,0 Bipolar axis through the representation of two rows or two columns. Reproduction of differences (contrasts) in the data X is obtained by perpendicular projection of the column- or row-pattern upon the bipolar axis (a + P = 1). 

Algebraically, the reconstruction of the values of X has been defined by the matrix product of the scores S with the transpose of the loadings L (eq. (31.22)). Geometrically, one reconstructs the value Xy by perpendicular projection of the point represented by upon the axis represented by s, as shown in Fig. 31.3c ... 

By virtue of the symmetry between scores and loadings, we can also construct bipolar axes through two columns 1 and 1 - such as is shown in Fig. 31.3f. When we project a row s, upon this bipolar axis we construct a difference between two elements in X. The proof follows readily from eq. (31.22) ... 

In practice, we would prefer an algebraic way to define the boundary. For this purpose, we define line d, perpendicular to a. One can project any object or point on that line. In Fig. 33.6c this is done for point A. The location of A on d is given by its score on d. This score is given by ... 

Fig. 35.3. Scatter plot of 16 olive oils scored by two sensory panels (Dutch panel lower case British panel upper case). The combined data are shown after Procrustes matching and projection onto the principal plane of the average configuration.

The projection of the X data vectors onto the first eigenvector produces the first latent variable or pseudomeasurement set, Zx. Of all possible directions, this eigenvector explains the greatest amount of variation in X. The second eigenvector explains the largest amount of variability after removal of the first effect, and so forth. The pseudomeasurements are called the scores, Z, and are computed as the inner products of the true measurements with the matrix of loadings, a ... 

With regard to linear projection based methods, the latent variables or scores determined by linear multivariate statistical methods such as... 

Scott, R. (1978) Eng. and Proc. Econ., 3 105. Working capital and its estimation for project evaluation. Taylor, J. H. (1977) Eng. and Proc. Econ. 2, 259. The process step scoring method for making quick capital estimates. 

ISO 14042 defines normalization as calculation of the magnitude of indicator results relative to reference information . The main aim of normalizing the category indicator results is to better understand the relative importance and magnitude of these results for each product system under study. For the purpose of normalization in LCA, Wegener Sleeswijk et al. [13] have made a comprehensive inventory of extractions and emissions of substances for the world level in the year 2000. The normalization in this project is based on these reference emissions and extractions. As a result the normalized score of an impact category for the cushion vinyl floor covering case is expressed as a fraction of the world problem. 

Fig. 17.2. PLS score plot for the VolSurf Caco-2 model. Light open circles represent penetrating compounds dark open circles represent nonpenetrating compounds. Filled circles represent the projection (prediction) of compounds in Table 17.1 in the Caco-2 model.

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