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4. Scroll through your images to nd one you d like to trim and press the Function/Set button again. The Trimming screen appears with a yellow box around the area being trimmed. 5. Use the Zoom lever and 4-Way Controller to resize and move the yellow box to adjust the trimming area. If there are faces in the photo you want to crop, turn the Control dial to jump to the face. 6. Press the Display button to change the orientation of the trimming box from horizontal to vertical if desired. 7. When you are pleased with the trimmed image, press the Function/Set button to save it as a new image. 8. Select OK to save as a new image le.

Suddenly she came upon a little three-legged table, all made o f solid glass: there was nothing on it but a tiny golden key.. . - Alice in Wonderland

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is preferred It requires judgment to assess the abnormality of such plots (Refer to the discussion of the fat pencil method in Section 6-7) We may also standardize the residuals by computing di ei 2 2, i 1, 2 p , n If the errors are normally distributed, approximately 95% of the standardized residuals should fall in the interval ( 2, 2) Residuals that are far outside this interval may indicate the presence of an outlier, that is, an observation that is not typical of the rest of the data Various rules have been proposed for discarding outliers However, outliers sometimes provide important information about unusual circumstances of interest to experimenters and should not be automatically discarded For further discussion of outliers, see Montgomery, Peck and Vining (2001).

It is frequently helpful to plot the residuals (1) in time sequence (if known), (2), against the yi, and (3) against the independent variable x These graphs will usually look like one of the four general patterns shown in Fig 11-9 Pattern (a) in Fig 11-9 represents the ideal situation, while patterns (b), (c), and (d ) represent anomalies If the residuals appear as in (b), the variance of the observations may be increasing with time or with the magnitude of yi or xi Data transformation on the response y is often used to eliminate this problem Widely used variance-stabilizing transformations include the use of 1y, ln y, or 1 y as the response See Montgomery, Peck, and Vining (2001) for more details regarding methods for selecting an appropriate transformation.

RC4 was invented by Ron Rivest in 1987. The RC is reputedly for Ron s Code, although officially it is Rivest Cipher. RC4 is without doubt the most widely used stream cipher in the world today. It is used, for example, in the Secure Socket Layer (SSL), which is the de facto standard for secure transactions over the Internet, and in Wired Equivalent Privacy (WEP), a widely deployed networking protocol that purports to semi-secure a wireless local area network (LAN). The RC4 algorithm is considered secure, if used properly. However, WEP--the Swiss cheese of security protocols--somehow managed to implement nearly all of its security functions insecurely, including RC4. As a result, there is a feasible attack on RC4 encryption as used in WEP. But before we discuss this cryptanalytic attack, we briefly mention a few of the many other security issues with WEP. Perhaps the most egregious security problem with WEP is that it uses a cyclic redundancy check (CRC) for integrity protection. The primary purpose of integrity protection is to detect malicious tampering with the data-not just to detect transmission errors. While a CRC is an excellent error detection method, it is useless for cryptographic integrity, since an intelligent adversary can alter the data and, simultaneously, the CRC value so that the integrity check is passed. This is precisely the attack that a true cryptographic integrity check, such as a MAC or HMAC, will prevent [142]. Furthermore, since RC4 is a stream cipher, WEP encryption is linear, which allows changes to be made directly to the ciphertext--by an attacker who

If a plot of the residuals against time has the appearance of (b), the variance of the observations is increasing with time Plots of residuals against yi and xi that look like (c) also indicate inequality of variance Residual plots that look like (d) indicate model inadequacy; that is, higher order terms should be added to the model, a transformation on the x-variable or the y-variable (or both) should be considered, or other regressors should be considered EXAMPLE 11-7.

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