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The box plot is an analyst s tool used only for interval and ratio variables that graphically displays a ve-number summary of a batch of numbers To illustrate the box plot, we use a data set, shown in Table 25, of the population of the 21 largest metropolitan statistical areas of the United States in 2006 It is taken from a spreadsheet downloaded from the US Census Bureau In Fig 227, an annotated box plot, shown in the middle of the gure, summarizes the population of the largest metropolitan areas Above the box plot is a list of the values ordered from smallest to largest In the list of numbers at the top of the graphic, the important ve values, highlighted in bold, are labeled and indexed to mark the corresponding vertical bars in the box plot.

Below the plot is part of the horizontal scale on which the population sizes have been plotted Ordinarily the horizontal scale would lie empty below the box plot, but here the plotted values help to understand how the shape of the box, the position of the vertical bars, and the length of its extensions give the analyst a feel for the features of the data The ve vertical bars mark the location on the horizontal scale of key markers in the ordered set of values for the variable The two that lie on the outermost fringes mark the location of the smallest and the largest values The other three form part of a box that de nes the region containing the central half, or midspread, of the data This region is known as the fourth-spread because it contains the lower and upper fourths.

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where Z obj and Z bg are, respectively, the associated object and background features under the hypothesis s. In this way, a hypothesis that also matches background features or pixels is penalized accordingly. Finally, we note how two simultaneous background models can be kept for more robustness: the off-line or static model, built from prior images, and the online or dynamic model, which is kept up to date with the current images, after removing data in areas of known objects. In fact, the former may be complete and stable but not up to date, whereas the latter is more recent but less complete, since areas covered by tracked objects must be excluded, or data from nontracked objects may be included erroneously. Once again, fusion of these two models (e.g., by weighted average of residuals) can achieve the bene ts of each.

The width of the box shows the spread The crossbar of the box, or the median, is a measure of the location of the center of the distribution The position of the median relative to the lower and upper quartiles gives an indication of skewness: the symmetry or balance of the distribution curve around the center In our example, the median s position to the right indicates that those data.

For a three-phase balanced system, equation (12.9) states that p and q [i.e., the instantaneous real power (W) and instantaneous imaginary power (VA)] are given by the average (i.e., overbar) and oscillatory (i.e., overcaret) components. The oscillatory components are present because of harmonics in the system. The average " p and " are scalar values with same magnitudes as of the phasor quantities P and Q. q The voltages and currents in equation (12.9) are obtained from the power invariance of the Clarke transformation, whose terms are indicated by equations (12.10) and (12.11). va vb ia " ~ p p p 12:9 vb va ib " ~ q q q 32 3 21 2 3 0 r 2 1 v0 va p 7 26 1 6 7 7 3 76 6 va 5 1 12:10 4 2 2 54 vb 5 34 2 p 3 1 1 vb vc 2 2 2 2 3 21 32 3 1 1 r 2 v0 va 2 2 26 6 7 7 1 1 76 12:11 4 vb 5 4 1 2 2 54 va 5 p p 3 3 3 vb vc 0 2 2 Equation (12.9) gives the de nition of instantaneous real and imaginary power assuming measured voltages and currents. Therefore, to control the VSI connected to the renewable energy source, the current references in the a b frame must be imposed as de ned by equation (12.12) for given set-point values of pc and qc . Inversion of the voltage matrix in equation (12.9) yields ia 1 va vb pc 12:12 i qc b vm 2 vb va

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