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CAM s grade: 3 Reasoning: CAM s drawdowns are roughly in line with the small cap benchmarks and the peer group.

Property 16: Square of signal If g x; y ju x; y j2 , then G fx ; fy U fx ; fy U fx ; fy Property 17: Square of spectrum If g x; y u x; y u x; y , then G fx ; fy jU fx ; fy j2 Property 18: Rotation of axes If g x; y u x; y , then G fx ; fy U fx ; fy The important properties of the FT are summarized in Table 2-1. 2:5-19 2:5-18 2:5-17

F (X(s)) ds N (0, 2 ) in distribution as t + ,

A kNN model uses the k most similar neighbors to the observation to calculate a prediction. Where a response variable is continuous, the prediction is the mean of the nearest neighbors. Where a response variable is categorical, the prediction could be presented as a mean or a voting scheme could be used, that is, select the most common classi cation term. In the learning phase, three items should be determined: Best similarity method: As described in 6, there are many methods for determining whether two observations are similar. For example, the Euclidean or the Jaccard distance. Prior to calculating the similarity, it is important to normalize the variables to a common range so that no variables are considered to be more important. k: This is the number of similar observations that produces the best predictions. If this value is too high, then the kNN model will overgeneralize. If the value is too small, it will lead to a large variation in the prediction.

0.62 0.08 0.37 0.12 2.29 0.49 0.72 0.61 2.56 0.85 1.65

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s E(L(CS,0 )) =

We have already created the login page in 6, Working with HTML Forms. The components of the page for accepting customer input are these:

stocks, it is not surprising to see that it has the highest correlation with CAM s portfolio. The correlation coef cient can (and should) be used to nd out the degree of association between CAM s returns and those of its peer group as well as the other managers in the Emerging Leaders Fund. Covariance Covariance measures the degree to which two variables move together over time relative to their individual mean returns. It is calculated by multiplying the correlation between two variables by the standard deviation for each of the variables. The covariance is similar to the correlation coef cient in that it measures the relationship between a pair of variables. However, unlike the correlation coef cient it is not standardized (in a correlation coef cient the covariance is divided by the standard deviations of x and y). Calculating Covariance in Excel =covar(RS1,RS2) where RS1 = Return stream one. RS2 = Return stream two. Notes: See notes in the correlation section for comments on Excel Syntax. R 2 (R-squared) R2 is the square of the correlation coef cient. When calculating this statistic, you compare the return stream of the product/fund under review (known as the dependent variable) to an appropriate benchmark or other measures, such as median manager returns or the returns of another investment manager (known as the independent variables). The R2 statistic can be interpreted as the proportion of variance in the dependent variable attributable to the variation in the independent variable. Calculating R-squared in Excel =rsq(RS1,RS2) where RS1 = Return stream one (dependent variable). RS2 = Return stream two (independent variable).

The following 19 cases are of patients who were referred to me because of TMD-like symptoms; not all had TMD. This section hopefully will help readers better identify patients with non-TMD symptoms and provide readers a better feel for how I integrate the various TMD therapies. For the sake of expediency, most patients identi ed perpetuating contributing factors (e.g., excessive caffeine consumption, stomach sleeping, neck pain, or poor sleep) are not reported. It is felt these factors have been adequately discussed previously in this book and need not be revisited here. During my initial evaluation for TMD, I always document patients suspected perpetuating

4:4-16

pi , and using a Normal approximation for the con-

Figure 5.1. For the process with covariance 1 and level u = 1, representation of the three upper bounds, D, R1, and R3, and the lower bound, R2 (from top to bottom), as a function of the length T of the interval. (From Aza s and Wschebor, 2002, with permission.)