Graphical representation of data

Often in scientific work it is useful to report data in the form of a graph to enable immediate visualization of general trends and relationships. Another advantage of plotting data in the form of a graph is to be able to estimate values for points between and beyond the experimental points. For example, in Fig. 12-2 the data of 

EXAMPLE 12.5. For the sample of gas described in Fig. 12-2, what pressure is required to make the volume 6.0 L Ans. It is apparent from the graph that a pressure of 1.3 atm is required. 

In this case, the longer the time spent traveling at constant speed, the greater the distance traveled. 

If 1/P is directly proportional to V, that is, /P)k = V, then P is inversely proportional to V, that is, k = PV. The straight line found by plotting l/P versus V can easily be extended to the point where F = 19 L. The l/P point is 2.4/atm therefore, 

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Pattern recognition has been applied In many forms to various types of chemical data (1,2). In this paper the use of SIMCA pattern recognition to display data and detect outliers In different types of air pollutant analytical data Is Illustrated. Pattern recognition Is used In the sense of classification of objects Into sets with emphasis on graphical representations of data. Basic assumptions which are Implied In the use of this method are that objects In a class are similar and that the data examined are somehow related to this similarity. 

Any report of the Athabasca oil sands requires some discussion of the magnitude of the deposit. The importance of the oil sands can be shown in their relation to two major Canadian resources—oil and minerals. Figure 1, a graphical representation of data from the Oil and Gas Journal (3), compares the oil sands with the conventional world petroleum reserves. Canada has less than 2% of the known reserves excluding the oils sands when the latter are included, her share of the reserves increases to 36%. This comparison is biased in that the many other oil... 

The range in EF is from zero to unity, with a racemic value of 0.5. Enantiomer fractions are preferred to ERs, as the EF range is bounded, and a deviation from the racemic value in one direction is the same as that in the other. For example, if the (—)-enantiomer is twice the concentration as its antipode, the EF is 0.333, which is the same deviation (0.167) from a racemic EF of 0.5 as the opposite case of the (+)-enantiomer at twice the concentration as the (—)-enantiomer (EF = 0.667). The respective ERs would be 0.5 and 2. The corresponding deviations of 0.5 and 1, respectively, are not the same deviation from the racemic ER of 1. Thus, ERs can produce skewed data inappropriate for statistical summaries such as sample mean and standard error [109]. As a result, EEs are more amenable compared to ERs for graphical representations of data, mathematical expressions, mass balance determination, and environmental modelling [107, 109]. Individual ER and EF measurements can be converted [107, 108] ... 

Fig. 10.6. Sensitivity of Rcelp to chloromethyl ketones and peptidyl (acyloxy)methyl ketones. Reporters based orr Ras (A) arrd a-factor (B) arrd were used to measure the impact of TPCK, TLCK, Phe-Lys AOMK (FKBK), and Phe-Ala AOMK (FABK) on the activity of yeast Rcelp as measured through fluorescence output (A) or a biological readout assay (B) DMSO is the solvent control. A schematic for each assay is shown on the left of the panel, and a graphical representation of data collected with the assay is shown on the right. Numbers on top of each bar in the graph reflect the percent activity observed relative to the DMSO-treated control. ABZ is aminobenzoic acid DNP is dinitrophenol. Data is reproduced in part with permission from Ref. [74].

The increased use of the Internet in recent years as a means of communicating information on drugs and medical devices has provided considerable benefits to the healthcare industry. The Internet offers many possibilities in terms of graphic representation of data and the ability to publish information to a wider audience at a faster speed than by the use of traditional marketing channels. The overriding compliance consideration, however, is the accurate transmission of information to the reader, whether the reader is a member of the public or a pharmaceutical or healthcare professional. 

The properties of such graphical representations of data compiled for the various combinations of A /B /P systems have justified this interpretation. 

Figure 2 Influenza of aUcyl chain length on MIC graphic representation of data from Table 1.

Differential thermal analysis curve Graphical representation of data collected by a differential thermal analyser, where the difference temperature is plotted as a function of temperature (scanning mode) or time (isothermal mode). 

Graphical representations of data related to those discussed in this chapter will be found in J. Kragten, Atlas of Metal-Ligand Equilibria in Aqueous Solution, E. Horwood, Chichester, 1978. 

Table 3. Graphical representation of data quality assessment.

FIGURE 10 Graphical representation of data array analysed with PARAFAC. 

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