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client does not yet know its IP address, how is it going to respond to the server s ARP request when the server decides to respond Well, if the client knows its IP address (as indicated in the Ciaddr field), the BOOTREPLY can be sent as a normal IP packet, since the client will respond to ARPs. But if the client does not yet know its IP address, then the client cannot respond to ARPs sent by the server. There are two options available: If the server has the capability to manually construct an ARP entry in its table for this client, it will do so using the Chaddr and Yiaddr fields that it is responding with in its BOOTREPLY (BSD Unix has this capability). The server will then reply using IP services and skip the ARP process. If the server does not have this capability, then it simply sends the BOOTREPLY with the IP address set to Broadcast (B). This event is shown in the table. Chicken-or-the-Egg Dilemma If the client does not yet know its IP address, how does it respond to ARP requests during a server response If the client knows its IP address, this is not a problem. Two options: Simply send the reply set to broadcast The server can manually construct the ARP entry using the fields in the received request

If you work on the Windows platform and have both QuickTime and DirectX 7.0 or better installed, you can import all of the formats listed above.

Figure 5-9

119 121 123

Internet Features . . . . . . . Analysis Tools . . . . . . . . Outlines . . . . . . . . . Automatic subtotals . . Scenario management . Analysis ToolPak . . . . Pivot tables . . . . . . . Auditing . . . . . . . . . Solver . . . . . . . . . . Add-Ins . . . . . . . . . . . . . Compatibility . . . . . . . . .

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This criterion is termed ordinary least squares MR that employs this criterion is also known as ordinary least squares regression (OLS regression) Multiple regression computed using ordinary least squares produces optimal estimates of the regression weights in that the correlation between the predicted score Y and the observed criterion Y is maximized The weights also have desirable properties for use in testing theoretical hypotheses when several statistical assumptions, to be presented later in the chapter, are met Residuals play a special role in the detection of problems with the hypothesized regression model Examination of special plots and diagnostic statistics based on the residuals help identify ways in which the regression model can be modi ed and improved to better capture the relationships in the data Nonlinear Relationships and OLS Regression Although Equation 19.

products within tunnel smoke houses, which are designed to operate in a continuous system. In such tunnels, the meats to be smoked are carried through consecutive sections, in which appropriate parameters of temperature, smoke density, humidity, and ow rate are maintained. For electrostatic smoking, the smokehouse is additionally equipped with a highvoltage section, where the smoke deposition takes place within a few minutes (Sikorski 1962, Tilgner and Sikorski 1962). Because the length of treatment is so short, the density of smoke has to be kept very constant in order to assure a uniform degree of smoking of the product. Electrostatic deposition may also be applied in smoking ovens in which smoke preparations are used instead of smoke. The other phases of the process (i.e., pre-drying and cooking) proceed as in conventional smoking. Additional Equipment To reliably operate a smokehouse, several instruments are necessary for measuring and controlling the temperature, relative humidity, and ow rate of the smoke and the temperature in the meat products. Similar instruments are used for control of other processes in the food industry. The density of the smoke, a crucial parameter of smoking, can be easily determined by photoelectric measurement of the intensity of a light beam transmitted through a layer of smoke. The spent smoke and other exhaust gases, after leaving the smokehouse, should be cleaned before entering the atmosphere. Depending on the contents of the polluting components, different equipment may be used. Some installations comprise three sections: an electro lter, a brous lter, and activated charcoal. Other systems use afterburners to oxidize the components of the spent smoke at 800 1500 C. In the presence of catalyzers, the temperature may be reduced to 600 C.

1 might seem to limit multiple regression only to the study of linear relationships, this is not the case We can also replace any of the Xj terms with a variety of mathematical functions of the predictor variables in Equation 191 to represent other forms of relationships For example, a psychologist studying perceptions of the area of square objects (Y) as a function of their width (Xj) would include a b j X 2 term in the regression equation given the known physij cal relationship for the area of squares, area = (width)2 In general, any mathematical expression involving one or more predictor variables may be used X j , log (Xj), Xj X k , and so forth So long as the regression weight simply serves to multiply the mathematical expression, the regression equation is linear in the coef cients and can be estimated using ordinary least squares.

Thus, although the results would almost certainly not be theoretically meaningful, the equation 2 Y = b0 + b1 X 1 + b2 X 2 + b3 X 1 + b4 X 1 X 2 could be estimated using OLS regression In contrast, regression equations.

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