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criterion and truth telling/lying was not investigated in that particular study. As mentioned above, CBCA experts assume that all criteria occur more frequently during truth telling than fabrication (a > sign in the table), and CBCA studies often reveal this pattern. Table 8.4 shows that people believe that truth tellers stories contain more details (Criterion 3) and more descriptions of interactions (Criterion 5) than stories of liars. This is in alignment with CBCA research. For many other cues, people do not know how they actually relate to truth telling. In fact, they often express beliefs that are the direct opposite of reality. For example, people believe that liars include more contextual embeddings (Criterion 4), unusual details (Criterion 8), and super uous details (Criterion 9) in their stories, whereas, in fact, truth tellers are more likely to do this. In summary, people s beliefs about how credible statements sound differ to some extent from the views of CBCA experts. This is good news for these experts, because this makes it less likely that liars who deliberately attempt to produce convincing statements without knowing the CBCA tool will provide statements that appear credible to CBCA experts. Several other studies have focused on the relationship between perceived credibility and one particular CBCA criterion: number of details. Most studies support the nding of the CBCA surveys that inclusion of many details makes an honest impression (Bell & Loftus, 1988, 1989; Conte, Sorenson, Fogarty, & Rosa, 1991; Wells & Leippe, 1981).

come to their decision. In this version of the open-ended question method the answers given are not context-free but depend on the video fragments that the observers had to judge. For example, it is unlikely that observers will report any verbal cues if the truth tellers and liars provided short answers. Compared to the open-ended question version described in the main text, this version is therefore more restricted. Also, when we compared these two versions in our study, we found an overlap in the results (Mann, Vrij, & Bull, 2004). I will therefore not discuss this restricted version of the open-ended question method in this chapter.

0.86 0.58 0.48 0.38 0.34 0.34 0.25 0.23 0.21 0.19 0.18 0.13 0.06 0.05 0.05 0.03 0.02 0.06 0.07 0.10 0.12 0.13 0.19 0.21 0.21

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where the processes Y1 (t) and Y2 (t) are chosen among {X(t), X (t)}. It suf ces to prove that all these terms have the same limit as t + , and then 0 whatever the choice is. Applying Lemma 10.8, the expression in (10.45) is equal to

Wavenumber (cm 1)

19:19

EXHIBIT 12.4 (Continued)

In this chapter, you will learn to do the following: Identify the mechanisms that are used for session tracking in JSP Use cookies for session tracking Use session objects for session tracking Use URL rewriting for session tracking

6 cups (1500 ml) reduced-sodium chicken broth 19 oz can (540 ml) red kidney beans 6 cups (1500 ml) kale leaves, chopped

The converse inequality is somewhat more complicated. We apply Lemma 3.3 to each of the intervals J1 , . . . , Jk . We have

9

Sum of squares of error (SSE) This statistic measures the total deviation of the response from the predicted value Summary table A summary table presents a grouping of the data where each row represent a group and each column details summary information, such as counts or averages Supervised learning Methods, which use a response variable to guide the analysis Support Represents a count or proportion of observations within a particular group included in a data set Survey A collection of questions directed at an unbiased random section of the population, using nonleading questions Temporal data mining See time-series data mining Test set A set of observations that are not used in building a prediction model, but are used in testing the accuracy of a prediction model Textual data mining The process of extracting nontrivial facts, patterns, and relationships from unstructured textual documents Time-series data mining.

3.62 3.29 3.07 2.92 2.81

The patient had no pain, complained he was wearing down his teeth (Figure V.1), and said he would like to prevent further tooth wear. His range of motion was 55 mm opening and 9/9/7 (right lateral, left lateral, and protrusive). Palpation of his masticatory and cervical structures revealed no tenderness. Does this patient have TMD Since none of the masticatory structures were tender, and he does not have a history of TMJ noise, he does not have TMD.

So far this is exactly the same as working with packages in any other area of Java programming. The difference is that you call the servlet by directing your browser to the following URL:

A prediction model or other method that uses historical information to predict future events Training set A set of observations that are used in creating a prediction model Transforming (data) A process involving mathematical operations to generate new variables to be used in the analysis Two-sided hypothesis test A hypothesis test where the alternative hypothesis population parameter may lie on either side of the null hypothesis value Type I error Within a hypothesis test, a type I error is the error of incorrectly rejecting a null hypothesis when it is true Type II error Within a hypothesis test, a type II error is the error of incorrectly not rejecting a null hypothesis when it should be rejected Unsupervised learning Analysis methods that do not use any data to guide the technique operations Value mapping.

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