Variables quantitative

Le gaz naturel est le gaz tel qu il est produit a partir du reservoir. II est constitue d un melange d hydrocarbures gazeux et peut aussi contenir des quantites variables d autres gaz (N2, CO2, H2S, He, Ar, etc.). Dans de nombreux endroits le gaz naturel est associe a la formation de petrole, en d autres lieux, il est associ a la carbonification d une matiere organique. 

First, the qualitative and quantitative variability of the amount of catechins and proanthocyanidins present in plant extracts used for different studies is probably the most significant. This might be due to the use of different procedures of extraction, quantification and structural elucidation. In most cases, even the lack of rigorous phytochemical characterization and quantification of active compoimd(s) constitutes a severe limitation on the rehabihty of the results. The lack of commercially available pure standards (particularly for some proanthocyanidins) represents an additional problem that has so far hampered the execution of rigorous SAR studies. This hmitation means that although a munber of in vitro or in vivo studies have been carried out by using more or less pure standards of catechins or with plant extracts containing both catechins and proanthocyanidins, only a handful of authors have... 

They cannot be part of a mathematical model whose purpose would be to turn the classification into a continuous quantitative variable. In particular, the example of physical factors illustrates this. Whereas for the highest degree criteria are the same as those of the NFPA code, the simple fact of wanting to add in physical factors to these calculation models forced the originators of this technique to forget about the NFPA code. 

Box GEP, Behnken DW (1960) Some new three level designs for the study of quantitative variables. Technometrics 2 445... 

Despite the quantitative variability of salts and silicate dust particles in the plants of Arid ecosystems, we can easily discern a trend towards the selective uptake of trace elements. The calculation of coefficient of biogeochemical uptake (Cb) shows the rates of exposure to heavy metals in biogeochemical food webs. One can see that the elements contained in the plant species of both Steppe and Desert ecosystems are in equal measure susceptible to the influence of environmental factors. The most extensively absorbed are Sr, Cu, Mo, and Zn. Their values of Cb are more than unit. The group of other elements, like Ti, Zr, and V, are poorly taken up, with their values of Cb often dropping below 0.1 (see Figures 4 and 5). 

The simplest case of this parameter estimation problem results if all state variables jfj(t) and their derivatives xs(t) are measured directly. Then the estimation problem involves only r algebraic equations. On the other hand, if the derivatives are not available by direct measurement, we need to use the integrated forms, which again yield a system of algebraic equations. In a study of a chemical reaction, for example, y might be the conversion and the independent variables might be the time of reaction, temperature, and pressure. In addition to quantitative variables we could also include qualitative variables as the type of catalyst. 

Yeast and fruit are input variables in the wine-making process. In the case of yeast, the amount of a given strain could be varied, or the particular type of yeast could be varied. If the variation is of extent or quantity (e.g., the use of one ounce of yeast, or two ounces of yeast, or more) the variable is said to be a quantitative variable. If the variation is of type or quality (e.g., the use of Saccharomyces cerevisiae, or Saccharomyces ellipsoideus, or some other species) the variable is said to be a qualitative variable. Thus, yeast could be a qualitative variable (if the amount added is always the same, but the type of yeast is varied) or it could be a quantitative variable (if the type of yeast added is always the same, but the amount is varied). Similarly, fruit added in the wine-making process could be a qualitative variable or a quantitative variable. In the algebraic system, x is a quantitative variable. 

In contrast, this analysis focuses on the evaluation of relationships between and among all quantitative variables simultaneously. 

Suppose that there are p quantitative variables of interest, x, x xp, and that in some region of interest the response can be approximated by the general first-order model... 

In Section 2.2 it was shown that response surface methodology can be applied to enable a researcher to model the effect of multiple quantitative variables on a response with a low-degree polynomial. Frequently, response surface techniques have focused on the mean response as the only response of interest. However, by regarding the variation in the response as an additional response of interest, the researcher can investigate how to achieve a mean response that is on target with minimum variation. In particular, if a researcher replicates each design point in an experiment, then an estimate of the standard deviation at each point can be calculated and used to model the effect of the variables on the variability of the response. 

Les frequences des maxima des bandes OH associees sont obtenues a partir de melanges ternaries constitues par des solutions a 1% du compose liydroxyle dans du tetrachlorure de carbone auxquelles on ajoute une certain quantite, variable avee Fenergie d associatioii, de Faccepteur de protons choisi. 

Scatterplots illustrate the relationship between two quantitative variables. Typically, the values of the independent variables are the x-coordinates, and the values of the dependent variables are the /-coordinates. When presented with a scatterplot, look for a trend. Is there a line that the points seem to cluster around For example ... 

Performing a qualitative or a quantitative variability and/or uncertainty analysis is often at the heart of addressing such important issues. 

When compounds are selected according to SMD, this necessitates the adequate description of their structures by means of quantitative variables, "structure descriptors". This description can then be used after the compound selection, synthesis, and biological testing to formulate quantitative models between structural variation and activity variation, so called Quantitative Structure Activity Relationships (QSARs). For extensive reviews, see references 3 and 4. With multiple structure descriptors and multiple biological activity variables (responses), these models are necessarily multivariate (M-QSAR) in their nature, making the Partial Least Squares Projections to Latent Structures (PLS) approach suitable for the data analysis. PLS is a statistical method, which relates a multivariate descriptor data set (X) to a multivariate response data set Y. PLS is well described elsewhere and will not be described any further here [42, 43]. 

To apply these multivariate techniques, we require a data matrix with the information corresponding to n observations of p quantitative variables X, X2,..Xp). We could, also, have some qualitative variables, coded numerically, to classify the observations into groups. From a geometric perspective, the n observations of the data matrix would correspond to n points of the Euclidean space of the p variables, and the Euclidean distance between observations would correspond to a measure of proximity (similarity). 

Interval - for certain quantitative variables, where numbers on an equal unit scale are related to an arbitrary zero, e.g. temperature in °C. 

Bar charts represent frequency distributions of a discrete qualitative or quantitative variable (e.g. Fig. 37.4). 

The median is the mid-point of the observations when ranked in increasing order. For odd-sized samples, the median is the middle observation for evensized samples it is the mean of the middle pair of observations. For a quantitative variable, the median may represent the location of the main body of data better than the mean when the distribution is asymmetric or when there are outliers. 

These occur when random events act to produce variability in a continuous characteristic (quantitative variable). This situation occurs frequently in chemistry, so normal distributions are very useful and much used. The bell-hke shape of normal distributions is specified by the population mean and standard deviation (Fig. 41.4) it is symmetrical and configured such that 68.27% of the data will lie within 1 standard deviation of the mean. 95.45% within 2 standard deviations of the mean, and 99.73% within 3 standard deviations of the mean. 

The most serious problem encountered with the assay system is a quantitative variability that is, a particular set of extracts assayed under identical conditions in separate assays may give figures which are quantitatively different. The reason for this is unknown. The relative cyclic AMP activity in the extracts is the same that is, when sample B contains twice as much cyclic AMP as sample A on day 1, it will also contain twice as much on day 2. However, the absolute values of cyclic AMP may vary. 

Mathematically, a correlation is a measure of the relation between two or more quantitative variables. A linear correlation is characterized by a slope and an intercept, and the closeness of the relationship is measured in terms of a coefficient of correlation. From a biopharmaceutical standpoint, correlation simply means relationships observed between parameters derived from in vitro and in vivo studies, irrespective of the mathematical definition of the term. 

Continuous (quantitative) variables, which can be adjusted to any value over their range of variation, e.g. pH, temperature, concentrations. 

To explore an experimental procedure, the experimenter chooses a range of variation for aU the experimental variables considered (a) for all continuous (quantitative) variables, the upper and lower bounds for their variation are specified (b) for the discrete (qualitative) variables, types of equipment, types of catalysts, nature of solvents etc. are specified. Assume that each experimental variable defines a coordinate axis along which the settings of the variables can be marked. Assume... 

Despite the quantitative variability of salts and silicate dust particles in the plants of Arid ecosystems, we can easily discern a trend towards the selective uptake of trace elements. The calculation of coefficient of biogeochemical uptake (Cb) shows that the elements contained in the plant species of both Steppe and Desert ecosystems are in equal measure susceptible to the influence of environmental factors. The most... 

Table S7 Distance measures between the objects s and t, described by p quantitative variables. In the last column the average distance...

These wastes of various origins (Table 8) are characterised by a huge qualitative and quantitative variability. However, all these wastes present more or less structured UV spectra as shown in Fig. 27. 

---