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1 (mod 3) 17: = -1 (mod 3 ) z = -4 (mod 11) and z = 1 (mod 3 ) IC = -4 (mod 11) and z = -1 (mod 3 )

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The LCD monitor displays the status of the following shooting functions in Quick Shot mode: Shutter Speed Aperture Value AE Lock/FE Lock ISO Speed Exposure Compensation My Colors Flash Exposure Compensation Flash mode White balance Image quality Drive mode Self-Timer Histogram Camera orientation Image Stabilization Battery charge indicator Recordable shots Depending which of these functions you have turned on, the Quick Shot screen will also indicate the status of the following: i-Contrast Red-Eye Correction Date Stamp

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I installed some free barcode font , and created a template on Excel (just some simple formulas to create consecutive values) and printed normally. Then I sticked ...

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6-61. Construct and interpret either a digidot plot or a stemand-leaf plot of these data. The 100 annual Wolfer sunspot numbers from 1770 to 1869 follow. (For an interesting analysis and interpretation of these numbers, see the book by Box, Jenkins, and Reinsel referenced in Exercise 6-60. Their analysis requires some advanced knowledge of statistics and statistical model building.) (read down, then left to right) (a) Construct a time series plot of these data. (b) Construct and interpret either a digidot plot or a stem-andleaf plot of these data. 101 82 66 35 31 7 20 92 154 125 85 68 38 23 10 24 83 132 131 118 90 67 60 47

41 21 16 6 4 7 14 34 45 43 48 42 28

3= 2.1+ 1

10 8 2 0 1 5 12 14 35 46 41 30 24

16 7 4 2 8 17 36 50 62 67 71 48 28

2=1.2+0.

8 13 57 122 138 103 86 63 37 24 11 15 40

62 98 124 96 66 64 54 39 21 7 4 23 55

94 96 77 59 44 47 30 16 7 37 74

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By back-substituting, we find 11(-1) 3(4) = 1. The Chinese Remainder Theorem provides the unique solution (mod p q ) for the system

6-62. In their book Forecasting and Time Series Analysis, 2nd edition (McGraw-Hill, 1990), D. C. Montgomery, L. A. Johnson, and J. S. Gardiner analyze the data in Table 6-5, which are the monthly total passenger airline miles own in the United Kingdom, 1964 1970 (in millions of miles). (a) Draw a time series plot of the data and comment on any features of the data that are apparent. (b) Construct and interpret either a digidot plot or a stem-andleaf plot of these data.

How do we know if a particular probability distribution is a reasonable model for data Sometimes, this is an important question because many of the statistical techniques presented in subsequent chapters are based on an assumption that the population distribution is of a speci c type. Thus, we can think of determining whether data come from a speci c

IC = a,

probability distribution as verifying assumptions In other cases, the form of the distribution can give insight into the underlying physical mechanism generating the data For example, in reliability engineering, verifying that time-to-failure data come from an exponential distribution identi es the failure mechanism in the sense that the failure rate is constant with respect to time Some of the visual displays we have used earlier, such as the histogram, can provide insight about the form of the underlying distribution However, histograms are usually not really reliable indicators of the distribution form unless the sample size is very large Probability plotting is a graphical method for determining whether sample data conform to a hypothesized distribution based on a subjective visual examination of the data The general procedure is very simple and can be performed quickly It is also more reliable than the histogram for small to moderate size samples.

1/30

Probability plotting typically uses special graph paper, known as probability paper, that has been designed for the hypothesized distribution Probability paper is widely available for the normal, lognormal, Weibull, and various chi-square and gamma distributions We focus primarily on normal probability plots because many statistical techniques are appropriate only when the population is (at least approximately) normal To construct a probability plot, the observations in the sample are rst ranked from smallest to largest That is, the sample x1, x2, p , xn is arranged as x112, x122, p , x1n2, where x112 is the smallest observation, x(2) is the second smallest observation, and so forth, with x(n) the largest The ordered observations x( j) are then plotted against their observed cumulative frequency ( j 05) n on the appropriate probability paper.

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