Analyst behavior

Some of the problem areas mentioned are sometimes overblown by many analysts. That is, they sometimes overemphasize the importance of a particular behavioral characteristic. That characteristic might be important only in one small regime of structural response, and you must know that limitation on the validity of the characteristic. The designer s job, on the other hand, is to either avoid all those problem areas or to in some way overcome them. The situation is somewhat like having a mountain in front of you, and you must get to the other side. You either climb over that mountain, in which case you definitely recognize that it is there and solve the problem, or go around it, in which case you have simply avoided the mountain. In both cases, you must recognize that the mountain exists in order to properly deal with it. 

It is well known that pMMA and pSty in THF follow ideal GPC behavior on many common GPC columns. However, many commercially important acrylate polymers contain a wide array of other monomers. In general, acrylic polymers composed of monomers that do not contain polar groups will yield well-behaved polymers, giving ideal GPC separations. Monomers that contain polar groups should prompt the analyst to carefully evaluate the possibility of adsorption of the analyte onto the column. The most common functionalities of concern are hydroxyl groups, amine groups, ethylene oxide units, and carboxylic acids. In many cases, such monomers can be tolerated. However, the acceptable level can vary considerably with even apparently minor changes in... 

An adequate description of material behavior is basic to all designing applications. Fortunately, many problems may be treated entirely within the framework of plastic s elastic material response. While even these problems may become quite complex because of geometrical and loading conditions, the linearity, reversibility, and rate independence generally applicable to elastic material description certainly eases the task of the analyst for static and dynamic loads that include conditions such as creep, fatigue, and impact. 

This information is supported by stress-strain behavior data collected in actual materials evaluations. With computers the finite element method (FEA) has greatly enhanced the capability of the structural analyst to calculate displacement, strain, and stress values in complicated plastic structures subjected to arbitrary loading conditions (Chapter 2). FEA techniques have made analyses much more precise, resulting in better and more optimum designs. 

One of the very first things that the analyst should do prior to attempting to fit a model to the data, is to inspect the data at hand. Visual inspection is a very powerful tool as the eye can often pick up an inconsistent behavior. The primary goal of this data-inspection is to spot potential outliers. 

The nature of the mathematical model that describes a physical system may dictate a range of acceptable values for the unknown parameters. Furthermore, repeated computations of the response variables for various values of the parameters and subsequent plotting of the results provides valuable experience to the analyst about the behavior of the model and its dependency on the parameters. As a result of this exercise, we often come up with fairly good initial guesses for the parameters. The only disadvantage of this approach is that it could be time consuming. This counterbalanced by the fact that one learns a lot about the structure and behavior of the model at hand. 

Now we come to the Standard Error of Estimate and the PRESS statistic, which show interesting behavior indeed. Compare the values of these statistics in Tables 25-IB and 25-1C. Note that the value in Table 25-1C is lower than the value in Table 25-1B. Thus, using either of these as a guide, an analyst would prefer the model of Table 25-1C to that of Table 25-1B. But we know a priori that the model in Table 25-1C is the wrong model. Therefore we come to the inescapable conclusion that in the presence of error in the X variable, the use of SEE, or even cross-validation as an indicator, is worse than useless, since it is actively misleading us as to the correct model to use to describe the data. 

Other, related, questions are also important Having determined this in isolation, how does the data analyst determine this in real data, where unknown amounts of several effects may be present There is a similarity here to Richard s earlier point regarding the relationship between the amount of noise and the amount of nonlinearity. Here are more fertile areas for research into the behavior of calibration models. 

The notations we discuss in this book form a specialised language for developing and discussing software and other systems behavior. It provides the benefit that we, the analysts and designers of the system, can use it to form a clear picture of what we intend to provide for the users. 

In actual practice, it is common to keep the transformation factors constant throughout the analysts. Engineering judgment is used to select the appropriate factors, depending on the predominant response mode anticipated. A trial and error approach may be used to evaluate the response mode behavior. An average of the clastic and plastic transformation factors is sometimes used. 

A comprehensive list of behavioral phenomena and physical attributes affecting the strength and stability of steel frames is compiled in White 1991. Some of the items listed include initial imperfections, residual stresses, initial strains, construction sequence, effects of simultaneous axial force, shear and moment on section capacities, P-dclta effect, local buckling and spread of inelastic zones in members. A similar list of items could be compiled for reinforced concrete and other structural materials. It is clear that a comprehensive advanced analysts can become quite... 

It is at this point that the environmental analyst has to identity the natme of the chemicals and their potential effects on the ecosystem(s) (Smith, 1999). Although petroleum itself and its various products are complex mixtures of many organic chemicals (Chapters 2 and 3), the predominance of one particular chemical or one particular class of chemicals may offer the enviromnental analyst or scientist an opportunity for the predictability of behavior of the chemical(s). 

Snrrogates chosen to monitor any of these areas should ideally bracket the range of the property. However, it should be pointed out that very few individual methods specify surrogates that provide information on all these areas, let alone bracket the property. The analyst must determine the suitability of the surrogate to reflect the properties and behavior of the analytes as well as the ability of the snrrogate to be nsed for the collection of reliable and defensible data. 

Figure 5.16 A control chart showing sporadic results starting with Day 18. This behavior would indicate the sudden introduction of random error, such as what might occur when a new analyst is assigned to the task.

A wide variety of chromatographic operation modes exists for the separation of proteins, due to the variation in their behavior. The diverse biological and biochemical functions of proteins originate largely from differences in the amino acid side chains that convey different properties onto the protein. This provides the protein analysts with a guide to select the possible operation modes. Possible techniques are ... 

J. P. Hart, M. D. Norman, and C. J. Lacey, Voltammetric behavior of vitamins D2 and D3 at a glassy carbon electrode and their determination in pharmaceutical products by using liquid chromatography with amperometric detection, Analyst, 777 1441 (1992). 

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