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When we developed the LSRL, we referred to y (the actual value the predicted value) y as an error in prediction. The formal name for y is the residual. Note that the order is y always actual predicted so that a positive residual means that the prediction was too small and a negative residual means that the prediction was too large. example: In the previous example, a criminal earning $1560/month paid restitution of $800/month. The predicted restitution for this amount would be = 56.22 + 0.46(1560) = $661.38. Thus, the residual for this case is y $800 $ 661.38 = $138.62. Calculator Tip: The TI-83/84 will generate a complete set of residuals when you perform a LinReg. They are stored in a list called RESID which can be found in the LIST menu. RESID stores only the current set of residuals. That is, a new set of residuals is stored in RESID each time you perform a new regression. Residuals can be useful to us in determining the extent to which a linear model is appropriate for a dataset. If a line is an appropriate model, we would expect to find the residuals more or less randomly scattered about the average residual (which is, of course, 0). In fact, we expect to find them approximately normally distributed about 0. A pattern of residuals that does not appear to be more or less randomly distributed about 0 (that is, there is a systematic nature to the graph of the residuals) is evidence that a line is not a good model for the data. If the residuals are small, the line may predict well even though it isn t a good theoretical model for the data. The usual method of determining if a line is a good model is to examine visually a plot of the residuals plotted against the explanatory variable. Calculator Tip: In order to draw a residual plot on the TI-83/84, and assuming that your x-data are in L1 and your y-data are in L2, first do LinReg(a+bx)L1,L2. Next, you create a STAT PLOT scatterplot, where Xlist is set to L1 and Ylist is set to RESID. RESID can be retrieved from the LIST menu (remember that only the residuals for the most recently done regression are stored in RESID). ZOOM ZoomStat will then draw the residual plot for the current list of residuals. It s a good idea to turn off any equations you may have in the Y= list before doing a residual plot or you may get an unwanted line on your plot. example: The data given below show the height (in cm) at various ages (in months) for a group of children. (a) Does a line seem to be a good model for the data Explain. (b) What is the value of the residual for a child of 19 months Age 18 19 20 21 22 23 24 25 81.3 26 27 28 29
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The primary application for using XML documents is the browser. An XML browser consists of a parser and a display or processing application. The XML parser interprets the tag information and extracts it from the XML document. After the parser extracts the data, it then passes it to the application, such as the browser. The XML parser reads the schema or DTD of an XML document and then verifies that the document is valid or well formed. Most XML applications include a built-in parser to verify XML documents. Many XML parsers are available for free throughout the Web. An XML parser is simpler than an SGML parser because the XML specification requires that all elements have complete start and end tags. The parser also validates the XML document by comparing elements with the schemas or DTDs. Because of these more rigorous requirements, an XML parser can be simpler and smaller since it does not need as much built-in intelligence as the corresponding SGML parser. Just like an XML parser, an XML processor also checks whether an XML document is well formed and whether it conforms to all the rules. However, a processor does not check an XML document against the DTD for validity. A processor may apply XML data in a variety of ways, such as editing, printing, or transmitting. Although a well-formed document uses correct structures and syntax, it may not conform to its schema or DTD. However, a valid document is a well-formed document that also conforms strictly to its DTD, schema, or meta-data. Also, an HTML can be well-formed, if it follows the syntax rules of XML. If a document is not well-formed, then an XML parser and some
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Using these settings, you can configure policies for the browser interface, URLs, connections, security, and programs. Some of these settings are serious and substantial others just provide ways to customize Internet Explorer cosmetically, such as giving Internet Explorer customized title bars and logos. Experiment with these settings and see if you want to apply any of them to your users. Remember to give your users the most freedom possible so they can get the most from their Internet experience.
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7. On the Local Network Adapter Configuration page, confirm or change the default settings for the internal network adapter card and then click Next. 8. During the remaining parts of Setup, the computer will reboot several times. To save time logging on, provide your password on the Logon Information page. This saves your password only until Setup is completed. 9. Follow the screen prompts as the operating system is configured, the system reboots, and components are loaded. This process will take several minutes (see Figure 4-6).
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As I mentioned earlier in the chapter, rules are collected together in a RuleSet. Rules within a RuleSet can be assigned priorities, and you can specify whether or not they re active at a particular point in time (akin to an enabled property). When more than one rule is present, the rules are processed in the following manner: 1. The list of active rules is derived. 2. The highest priority rule (or set of rules) is found. 3. The rule (or rules) is evaluated, and its then or else actions are executed as necessary. 4. If a rule updates a workflow field or property used by a previous, higher-priority rule in the list, that previous rule is reevaluated and its actions are re-executed as necessary. 5. The process continues until all rules in the RuleSet have been evaluated, or reevaluated, as required. Rules can be forward-chained as a result of three situations: implicit chaining, attributed chaining, or explicit chaining. That is, rules can be linked and share dependencies because the workflow runtime ascertained there was a need (implicit chaining), you applied one of the rules-based attributes to a method (attributed chaining), or you used the Update statement (explicit chaining). Let s look briefly at each.
Key Areas to Highlight
We choose to organize the control of sodium and water excretion around the topic of blood pressure for 2 reasons. First, because pressures in various parts of the vascular system have such a powerful influence on renal function, and second, because renal actions so strongly affect blood pressures. In doing so, we will encounter many important concepts and components. We briefly outline them here and expand them in the ensuing discussion. First is the concept of a set-point, which is the value that blood pressure should be at any moment (similar to the setting for temperature on the thermostat in your house). Second are detectors of blood pressure ( pressure gauges ), which assess the level of blood pressure at any moment. Third are signals generated in response to changes in blood pressure sensed by the detectors that regulate the fourth component: effectors, which change what they do in response to the signals in order to raise or lower blood pressure and return it to the setpoint. The effectors of blood pressure regulation are (1) the heart, which has a variable contractility and beat rate; (2) peripheral arterioles, which determine resistance to flow in the peripheral vasculature; (3) large veins, which change their compliance to vary the capacity of the vascular system to hold blood; and (4) the kidneys, which vary their output of salt and water. We will elaborate on the renal involvement in these effectors as we go along. The various blood pressure regulatory processes occur over different time spans. There are immediate (within seconds) cardiovascular reflexes that are for the most part nonrenal in nature. Then, there are slower processes spanning time scales of minutes to days centered on renal regulation of salt and water (ie, fluid volume and osmolality). We can arbitrarily divide regulation into short-term, intermediateterm, and long-term processes, recognizing that those in one time domain overlap with those in others and thus each process can interact with the others. Despite this overlap between these systems, it still helps to conceptualize them, as we will do below, as separate (but interacting) processes. Figure 7 1 summarizes these relationships.
Defining the Controller
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