Subject correlation coefficient

As Fig. 7.58 indicates, our task was to explain the ordering of the eight probe molecules in the human in vivo target, but subjecting the eight probe molecules to each of the 50 PAMPA lipid models. For each PAMPA model, the regression correlation coefficient, r2, was used to assess the appropriateness of the model. 

There are two (count them two) more very critical developments that come from this partitioning of sums of squares. First, the correlation coefficient is not just an arbitrarily chosen computation (or even concept), but as we have seen bears a close and fundamental relationship to the whole ANOVA concept, which is itself a very fundamental statistical operation that data is subject to. As we have seen here, all these quantities - standard deviation, correlation coefficient, and the whole process of decomposing a set of data into its component parts - are very closely related to each other, because they all represent various outcomes obtained from the fundamental process of partitioning the sums of squares. 

Figure 2. Elistogram of the correlation coefficients between the thresholds for 69 different odorous substances obtained from the same group of subjects (P.EI.Punter).

Statistics. Data were subjected to analysis of varlatice and regression analysis by using the general linear model procedure of the Statistical Analysis System (16). Correlation coefficients between growth parameters were determined with the same system. Equations were best fitted to the ata based on significance level of the terms of the equation and R values. 

Calibration curves for all BAs subjected to the sample work-up procedure were linear in the range 5-200 pmoles. The linear regression correlation coefficients varied from 0.991 to 0.999 and the slopes varied from 0.83 to 1.32, when the measured isotope ratios were plotted against the calculated ratios. Detection limits for all BAs investigated were 1 pg (2.5 fmol) injected onto the column. 

A wide range of bituminous coals, representing most of the major U. S. coal fields, were microscopically and chemically analyzed in this study. The samples tested, approximately 200 in all, were obtained through various coal producers. However, analytical data were not available for all samples carbonization, chemical, and by-product gas data for these subject coals were assumed to be the same as those reported previously in published sources (7, 9910,11,17,18). Only those carbonization data obtained from tests conducted in 18-inch pilot coke ovens at 900°C. are included. The remaining analytical data, approximately one-tenth of the total test results, were obtained from full scale commercial coke oven tests. Because these latter data were not sufficiently represented in the study, they were not considered in computing correlation coefficients (r). 

Fig. 12.16. Resonance Raman images of macular pigment distributions obtained for the same subject with the Raman method (a) and with a fluorescence-based imaging method (b) (c) comparison of integrated pigment densities obtained for 16 volunteer subjects with both imaging methods. A high correlation coefficient of R=0.89 is obtained for the correlation between both methods...

From the correlation analysis it is known that the correlation-coefficient value lies between -1 and +1. If an increase in the value of one response causes the other one to rise, their correlation coefficient has a positive value. The closer a correlation coefficient value is to one, the more the value of one response depends on the value of the other one, i.e. there is a linear connection between responses so that only one response may be followed on the actual research subject. It should be noted once... 

In principle, the features describing the objects can also be subjected to cluster analysis. In this case one may think immediately of the correlation coefficient, r, or the coefficient of determination, COD, as a measure of the similarity of each pair of features. Accordingly, 1 - r or 1 - COD is useful as a measure of distance. 

The large variance of the elemental depositions, also demonstrated by the very uncertain temporal courses of the elemental deposition rate (Fig. 7-2), strongly limits visual inspection of the obtained data, the interpretation can be subjective only. Otherwise practically all simple correlation coefficients are significant. Both facts show that it seems to be useful to apply advanced statistical methods to attempt recognition of possible existing data structures which may enable the characterization of pollutant loading and the possible identification of emission sources. 

The use of the icterus index, as described by Meulengracht, for the assessment of jaundice has fallen into disrepute because of the errors caused by the presence of lipochromes, carotenoids, and other yellow pigments. Josephson (J6) in his survey found that the correlation coefficient between icterus index and serum bilirubin concentration was 0.69 in 360 healthy subjects and 0.84 in 40 jaundiced subjects. In newborn infants however, bilirubin is the only yellow pigment likely to be present and the possibility of determining serum bilirubin concentrations by direct measurement has again been re-examined. Abelson and Boggs (Al) diluted serum from infants with erythroblastosis 1 in 50 and studied the absorption curves. They found that in addition to the bili-... 

ICE was developed for estimating acute toxicity of chemicals to species where data are lacking. Interspecies correlations were created for 95 aquatic and terrestrial organisms using least squares regression where both variables are random (i.e., both variables are independent and subject to measurement error Asfaw et al. 2004). The correlation coefficient (r) is used to describe the linear association amongst the... 

These starburst dendrimers have been subjected 47 to two different fractal analyses I48 49 (a )A c/2 D)/2, where A is the surface area accessible to probe spheres possessing a cross-sectional area, o, and the surface fractal dimension, D, which quantifies the degree of surface irregularity and (b) A = dD, where d is the object size. Both methods give similar results with D = 2.41 0.04 (correlation coefficient = 0.988) and 2.42 0.07 (0.998), respectively. Essentially, the dendrimers at the larger generations are porous structures with a rough surface. For additional information on dendritic fractality, see Section 2.3. 

Figure 7 compares the results obtained from the average of four subjects analyzed by GLC-MS-EI, TLC, radio-label, and electron capture GLC. Correlation coefficients are calculated in Figure 8. The results are in reasonable agreement, and in particular the GLC-MS and electron capture GLC procedures gave good agreement for most points over the whole curve.

Determination of Linearity and Range Determine the linearity of an analytical method by mathematically treating test results obtained from analysis of samples with analyte concentrations across the claimed range of the method. The treatment is normally a calculation of a regression line by the method of least squares of test results versus analyte concentrations. In some cases, to obtain proportionality between assays and sample concentrations, the test data may have to be subjected to a mathematical transformation before the regression analysis. The slope of the regression line and its variance (correlation coefficient) provide a mathematical measure of linearity the y-intercept is a measure of the potential assay bias. 

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