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De nition An observation that has a very large standardized residual in absolute value. Measures the strength of the linear relationship between two quantitative variables, with values ranging from 1 to 1. An observation that alters the regression parameters signi cantly based on its presence or absence in the data set. Measures the level of in uence of an observation by taking into account both the size of the residual and the amount of leverage for that observation. Represents an overall measure of the error in prediction resulting from the use of the estimated regression equation. An observation that is extreme in the predictor space, without reference to the response variable. Measures the overall improvement in prediction accuracy when using the regression as opposed to ignoring the predictor information. The vertical distance between the response predicted and the actual response. The proportion of the variability in the response that is explained by the linear relationship between the predictor and response variables.

If you want to send someone a letter, you can t just write a letter and drop it in the mailbox because the post office would not know where to deliver it. In order to make sure that the mail gets to its destination you have to put the letter in an envelope and address it. This same concept is used in networks, except that, instead of putting letters into envelopes, data is given protocol headers. The protocol headers act just like an envelope to identify the data s destination. The protocol that every computer on the Internet uses to communicate with anyone in the world is the TCP/IP protocol suite, which was developed in the 1970s as part of the original research that led to the Internet. The TCP/IP protocol suite actually contains four main communication protocols: IP, TCP, UDP, and ICMP. These protocols run on both the sending computer and the receiving computer to standardize communications. The TCP protocol on the sender s computer communicates with the TCP protocol on the receiver s computer, and the IP protocol on the sender s computer communicates with the IP protocol on the receiver s computer. Protocol headers were created so that the sender can provide key information about the communication to the receiver. Because these headers were created 30 years ago, some of the information is not needed today or can be modified without having an effect on the traffic. This makes protocol headerbased stego possible. Every packet that goes across the Internet must contain these headers, and you can easily embed data in the unneeded portion of them.

1. www.ecommercetimes.com/story/Alternative-Payments-More -Ways-to-Close-the-Sale-65954.html wlc=1235049585. 2. www.entrepreneur.com/ebusiness/networksolutions/ article159682.html. 3. www.javelinstrategy.com/2009/02/05/alternative-payments-the -economy-and- s-payment-strategy.

treatment A; a different and usually much smaller error variance, MSRESIDUAL, is used to test treatment B and the A B interaction. As a result, the power of the tests for B and the A B interaction is greater than that for A. Hence, a split-plot factorial design is a good design choice if a researcher is more interested in treatment B and the A B interaction than in treatment A. When both treatments and the A B interaction are of equal interest, a randomized block factorial design is a better choice if the larger block size is acceptable. If a large block size is not acceptable and the researcher is primarily interested in treatments A and B, an alternative design choice is the confounded factorial design. This design, which is described later, achieves a reduction in block size by confounding groups of blocks with the A B interaction. As a result, tests of treatments A and B are more powerful than the test of the A B interaction. Earlier, an RBF-32 design was used for the weight-loss experiment because the researcher was interested in tests of treatments A and B and the A B interaction. For purposes of comparison, I analyze the same weight-loss data as if an SPF-3 2 design had been used even though, as we will see, this is not a good design choice. But rst I describe the classical model equation for a two-treatment split-plot factorial design. The classical model equation for the weight-loss experiment is Yi jk = +

There is some evidence from studies involving populations of great reed warblers Acrocephalus arundinaceus and indigo buntings Passerina cyanea that suggests that female birds do behave in a way that supports the model. In both cases monogamous females did only marginally better in terms of chicks produced per season than did polygamous females.

constant or partialed out. For the two contrast codes, the regression equation is as follows: Y = b0 + bY 1.2 C1 + bY 2.1 C2 . (19.17)

Figure 7.6 Multiple samples improve on the synchronization error. (a) Every point represents a sample, that is a local time hi of node Ni and an estimated local time hj of node Nj. Using interpolation techniques improves on the synchronization error. The solid line results from a linear regression on the samples, the dashed line is the result of a phaselocked loop. (b) The same idea can be used for lower (5) and upper (4) bounds on the local time of Nj. Also here, interpolation can considerably improve on the synchronization error (i.e., on the uncertainty in this case). The solid lines are determined by the convexhull approach, the dashed lines according to ref. [14].