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Charting Tricks . . . . . . . . . . . . . . . . . . Printing embedded charts on a full page Creating a dead chart . . . . . . . . . . Controlling a data series by hiding data . Storing multiple charts on a chart sheet Using linked pictures in a chart . . . . . Animated charts . . . . . . . . . . . . . . Creating a hypocycloid chart . . . . . . . Creating a clock chart . . . . . . . . . . Drawing with an XY chart . . . . . . . . .

IRT not only seeks to be effective in treating disordered patterns of personality, but it also explicitly addresses links among interpersonal and intrapsychic events and associated symptoms such as depression or anxiety. Although patients, Utah graduate student trainees, experienced clinician trainees in Wisconsin, and clinicians who attend IRT workshops and see videotapes of this work are convinced of its validity, IRT has not met, and is unlikely to be able to meet, current scientific standards that would permit it to be included on the list of well-established empirically supported therapies (ESTs; Chambless & Ollendick, 2001) within the usual time frameworks for research. This situation is not unique to IRT. It is a problem that has to be faced by any approach that seeks to treat severe personality disorder as it occurs in ordinary practice. Because of the relevance of this problem in documenting effectiveness to treatment of personality in general, the methodological issues are explored in some detail in the next few sections. The discussion leads to a suggestion for an alternative research paradigm that should supplement current rules for how to establish that a treatment is effective.

For schedules S1 and S2, the only read operation is R1(X). The value that R1(X) returns to T1 is the value that was initially in the database what was written by T0 before T1, T2, and T3 started. This satis es Condition 1. Condition 2 also holds true for these two schedules since the nal value of X is written by T3. As a result, S, S1, and S2 are view equivalent. As we argued before, these three schedules are con ict serializable. It is not too dif cult to observe that every con ict serializable schedule is also view serializable.

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where both i and h run from 1 to n, and there are (1/2) n (n 1) comparisons of scores on each variable for i > h. The Pearson, Kendall, and Spearman correlations are de ned by the choice of the aih and bih comparisons in Equation 25.1. When the ith raw score is equal in value to the hth raw score on a variable, then the pair is tied. The treatment of tied values can be important, so comments regarding ties are selectively included. Pearson s Correlation Coef cient If interval variables can be assumed, then Pearson s is considered an appropriate measure of bivariate correlation. The formulas for and its sample counterpart, r, are very familiar. However, our present purposes are served better if we introduce an unnecessary complexity. Let aih and bih be differences between raw scores. We de ne aih = xi xh , (25.2)

always be added to culture medium after the addition of carrier protein (0.1% BSA or 1 10% appropriate serum).

p(X i = xi | ) p( c ) p(X i = xi | ) p( c )

The following example demonstrates how to use an Excel worksheet function in a VBA procedure. Excel s infrequently used ROMAN function converts a decimal number into a Roman numeral.