Statistics significant figures

Factor Loadings (Only Statistically Significant Figures are Given)... 

Hose and van den Brink (2004) demonstrated that there was no difference in the sensitivity of Australian and non-Australian fish species to endosulfan. Furthermore, although southern hemisphere (Australian region) freshwater fish are, on average, less sensitive to copper than northern hemisphere (Nearctic and Palearctic) fish, these differences are not statistically significant (Figure 7.5). However, more studies are required to assess the generality of these observations. 

Practically, one often compares the conformation of a protein under two or more conditions. In that case, the difference of the protection factors for the two or more conditions is also minimized during the model optimization. As a result, the difference in protection factors of the two conditions only becomes visible when the differences are statistically significant (Figure 7.7). Taking antistreptavidin lgG2 as an example, the regions of the antibody heavy chain that are destabilized (decreased protection factors) by reduction of interchain disulfide bonds are clearly visible in the differential protection factor plot shown in Figure 7.7b. 

These bond fluctuation data were the first to give any numerical indication of the expected second regime in the motion of the middle monomers. More recently Schulz et ed. used a combination of MD and MC for very similar model parameters as in the bond fluctuation simulations of Paul et They improved the statistics significantly. Figure... 

The integral of the Gaussian distribution function does not exist in closed form over an arbitrary interval, but it is a simple matter to calculate the value of p(z) for any value of z, hence numerical integration is appropriate. Like the test function, f x) = 100 — x, the accepted value (Young, 1962) of the definite integral (1-23) is approached rapidly by Simpson s rule. We have obtained four-place accuracy or better at millisecond run time. For many applications in applied probability and statistics, four significant figures are more than can be supported by the data. 

In this experiment students measure the length of a pestle using a wooden meter stick, a stainless-steel ruler, and a vernier caliper. The data collected in this experiment provide an opportunity to discuss significant figures and sources of error. Statistical analysis includes the Q-test, f-test, and F-test. 

In defining acute level toxicity foi the purposes of comparing different materials, the LD q itself is not sufficient but the LD q and the 95% confidence limits should be quoted as a minimum. For example, and as demonstrated in Figure 8, two materials (A and B) with different LD q values, but overlapping 95% confidence limits, ate to be considered not statistically significantly different with respect to mortahty at the 50% level this it based on the fact that there is a statistical probabiUty that the LD q of one material could He in the 95% confidence limits of the other, and vice versa. Conversely, when there is no overlap in 95% confidence limits, as shown with material C, it may be concluded that the LD q values ate statistically significantly different. 

FIGURE 10.5 Full agonist potency ratios, (a) Data fit to individual three-parameter logistic functions. Potency ratios are not independent of level of response. At 20%, PR = 2.4 at 50%, PR = 4.1 and at 80%, PR = 6.9. (b) Curves refit to logistic with common maximum asymptote and slope. PR = 4.1. The fit to common slope and maximum is not statistically significant from individual fit. 

FIGURE 11.16 Control dose-response curve and curve obtained in the presence of a low concentration of antagonist. Panel a data points. Panel b data fit to a single dose-response curve. SSqs = 0.0377. Panel c data fit to two parallel dose-response curves of common maximum. SSqc = 0.0172. Calculation of F indicates that a statistically significant improvement in the fit was obtained by using the complex model (two curves F = 4.17, df=7, 9). Therefore, the data indicate that the antagonist had an effect at this concentration. 

The obtained results allow us to advance with the basic assumption the north sector, subject to anthropogenic influence, it showed a carbon stock 23% lower than the south sector, which had less accessibility and a better state of conservation (Table 4). These differences were statistically significant (H = 11.20, p < 0.001) only for the AGB stratum, but not for the other strata studied nor for the total carbon stock. Under similar conditions of climate, soil, geomorphology, altitude, and latitude, the human influence could explain these differences, as the AGB stratum is the easiest to appropriate by humans [10,17,19, 21]. The AGB make the largest contribution in both sectors to the carbon stock (53, 55%), followed by SOC (28-31%) and finally BGB (8-10%) depending on the sector analyzed (Figure 3). 

FIGURE 20.2 The involvement of RARE on apo-lO -lycopenoic acid-transactivated RARp expression. Upper panel Diagram of the RARp reporter vector with wild type and mutated R AREs. Lower panel HeLa cells transfected with the RARp reporter vector and an internal control vector were treated with 5 pmol/I. of apo-lO -lycopenoic acid or 1 pmol/L of all-trans retinoic acid for 24h. Luciferase activities were measured by dual-luciferase reporter system. Values are means of SEM of three replicate assays., statistically significantly different, as compared with control in the same group, P < 0.05. (Adapted from Lian, F. et al., Carcinogenesis, 28, 1567, 2007. With permission.)... 

Figure 10 presents the results of assay of VE and ACL in blood plasma of rabbits treated with probucol and two other synthetic antioxidants (S-l, S-2) for 4 weeks. In addition to an improvement in antioxidative blood plasma protection, in the case of compound S-2 a statistically significant (p < 0.01) decrease in vitamin E content was detected, a finding considered physiologically unfavorable. 

Figure 6.4 displays the result of this statistical analysis for one single thunderstorm. At the location of the laser filaments (arrow head), 43% (3 out of 7) of the pulses are synchronized with the laser repetition rate, corresponding to a high statistical significance (1 — async = 0.987). The delay mismatch between the RF pulses detected on the different LMA detectors correspond to some tens of meters, typical of spatially spread events, such as a series of... 

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