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a sound clinical approach to rule out more serious renal disease (Fig. 12.1). It is important to take a detailed medical history and perform a physical examination to exclude clinical evidence of renal or systemic disease. In particular, it is important to obtain a detailed exercise history including the intensity, duration, and type of exercise. Furthermore, the uid intake, environmental conditions, and previous history of proteinuria is important. Finally, it is important to

Finally, then, you need to serialize elements. Mostly, elements are fairly straightforward. You can tell whether an element is empty (and can therefore be serialized with an empty element tag) by checking whether it has any child nodes. Attribute values need to be escaped in much the same way as text nodes, but you also need to escape any quotes within the attribute value. Set up the $attr escapes variable to hold the following XML:

Service Provider Cable (Offering Voice, Broadband Internet, and Video)

Figure 3.9. Contribution of wireless data revenue to overall ARPU for major carriers. Source: Chetan Sharma Consulting, Global Wireless Data Update 2007, March 2008.

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The risk preference function plotted in Figure 1315 is an exponential risk preference function with g = 000025 Another important concept in risk preference is the risk tolerance, or the inverse of the risk aversion coef cient For the exponential risk preference function and its constant risk aversion coef cient, the risk tolerance is constant In Figure 1315, the risk tolerance is $4000 For an expected value decision maker the risk aversion coef cient is zero, making the risk tolerance in nity The exponential risk preference function has another very special property, called the delta property This property is stated as follows: An increase in all outcomes of the lottery by a constant amount, D, results in an increase of the certain equivalent by the same amount, A.

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So, for example, in the rst example above suppose that the certain equivalent for fty fty gamble of $100 and $0 was $35 Now, if each prize is increased by $100 and the certain equivalent of a fty fty gamble on $200 and $100 becomes $135, then the delta property is satis ed for at least this one case The exponential risk preference function is the only function that can satisfy this property One very important implication of the delta property is that the buying and selling prices of a lottery are the same For example, the maximum that a decision maker was willing to pay, B for a lottery is the amount that when subtracted by every outcome made us indifferent to having the lottery and not having it, or a value of $0.

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Similarly, the minimum that the decision maker would sell the lottery for, S, is its certain equivalent; also see Figure 1316 If the risk preference function is exponential, it can be proven that B = S through the use of the delta property For other risk preference functions the buying and selling prices of a lottery are not necessarily equal There is a quick and dirty method for assessing a decision maker s risk aversion coef cient for an exponential utility function The value of R for which the decision maker is indifferent to accepting the lottery in Figure 1317.

Figure 3.8 is a data model for storing estimated product costs. Many cost components figure into the overall costs of a product. Therefore, either a PRODUCT or a PRODUCT FEATURE may be costed by many ESTIMATED COST COMPONENTS. The ESTIMATED PRODUCT COST entity maintains information on each product and its many costs. The COST COMPONENT TYPE entity specifies what type of cost it is. COST COMPONENT TYPEs include ESTIMATED MATERIALS COSTs, ESTIMATED LABOR COSTs, and ESTIMATED OTHER COSTs such as manufacturing costs (i.e., the use of machinery and equipment), shrinkage costs (i.e., theft or perishable good losses), shipping costs, costs in selling

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