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These different terms, where they refer to the same company, should be consolidated into one for analysis In addition, subject matter expertise may be needed in cleaning these variables For example, a company name may include one of the divisions of the General Electric Company and for the purpose of this speci c project it should be included as the General Electric Company It can be more challenging to clean variables measured on an interval or ratio scale since they can take any possible value within a range However, it is useful to consider outliers in the data Outliers are a single or a small number of data values that are not similar to the rest of the data set There are many reasons for outliers An outlier may be an error in the measurement.

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observations according to ranges in the descriptor variables. One potential partition of the data is shown in Figure 7.21. The prediction is then made based on the observations used to train the model that are within the speci c region, such as the most popular class or the average value (see Figure 7.22). When an observation with unknown fuel ef ciency is presented to the decision tree model, it is placed within one of the regions. The placement is based on the observation s descriptor values. Two observations (A and B) with values for Acceleration and Weight, but no value for whether the cars have good or poor fuel ef ciency, are presented to the model. These observations are shown on the scatterplot in Figure 7.23. Observation A will be predicted to have good fuel ef ciency whereas observation B will be predicted to have poor fuel ef ciency.

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A series of outlier data points could be a result of measurements made using a different calibration An outlier may also be a genuine data point Histograms, scatterplots, box plots and z-scores can be useful in identifying outliers and are discussed in more detail within the next two chapters The histogram in Figure 33 displays a variable Height where one value is eight times higher than the average of all data points There are additional methods such as clustering and regression that could also be used to identify outliers These methods are discussed later in the book Diagnosing an outlier will require subject matter expertise to determine whether it is an error (and should be removed) or a genuine observation If the value or values are correct, then the variable may need some mathematical transformation to be applied for use with data analysis and data mining techniques.

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Since so few applications make use of bits 3 through 5 of the IP ToS byte, the Differentiated Services working group suggested the use of those three bits in addition to the three low-order bits to provide the ability to identify up to 64 classes of service, known as Diffserv Code Points (DSCP) (see Figure 9.6).

This will be discussed later in the chapter Another common problem with continuous variables is where they include nonnumeric terms Any term described using text may appear in the variable, such as above 50 or out of range Any numeric analysis would not be able to interpret a value that is not an explicit number, and hence, these terms should be converted to a number, based on subject matter expertise, or should be removed In many situations, an individual observation may have data missing for a particular variable Where there is a speci c meaning for a missing data value, the value may be replaced on the basis of the knowledge of how the data was collected..

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Now, apply Ylvisaker s Theorem 1.21 to prove that the expression above tends to zero as h 0, which proves the continuity of P{Au (Xt , t )}. Similar arguments can be applied to prove that each of the three terms on the right-hand side of (7.8) is a continuous function of u. Our next lemma is the basic technical tool to prove the fundamental Theorem 7.3. Lemma 7.7. Suppose that Z = {Z(t) : t [0, 1]} is a stochastic process that veri es H2 . De ne I Fv (u) = E v .1Au , where Au = Au (Z, ) = {Z(t) (t)u for all t [0, 1]}, ( ) is a real-valued C 2 -function de ned on [0, 1], and v = G(Z(t1 ) (t1 )v, . . . , Z(tm ) (tm )v) for some positive integer m, t1 , . . . , tm [0, 1] , v R and some C -function G : Rm R having at most polynomial growth at , that is, | G(x) | C(1+ x p ) for some positive constants C and p and all x Rm . Then, for each v R, Fv is of class C 1 and its derivative is a continuous function of the pair (u, v), which can be written in the form

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