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t the weight w, using, for example, the algorithms proposed by [Kruskal 1956, Prim 1957] The edges of this maximum spanning tree are the edges in E With a join tree we finally have a singly connected structure A join tree for the Danish Jersey cattle example is shown on the right in Figure 418 The join graph it is constructed from has additional edges between the first and second and between the third and fourth node in the top row..

It can be shown [25] that, for t on the interval 0; 2T , f t h t E t where h t is given by ( ! ! ) 1 1 F a X kpt kpt kpt kpt h t Re F a cos Im F a sin T 2 T T T T k 1 4:4:19 and the error E t is bounded by E t M eat e2T a a 1 ! 4:4:20 4:4:18

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Salzman (1997) reports links between a particular attachment pattern and two clinically signi cant correlates, affective instability and eating disorders (anorexia, or anorexia with bulimia). Although not a patient study in the sense above, all reported cases of eating disorder had been diagnosed by family physicians, and so the ndings are included here for discussion. The interview sample was preselected by a screening questionnaire to yield a group of women, who appeared to meet the criteria for secure (n = 10), ambivalent (n = 11), or avoidant (n = 7) attachment to their mothers. All partook in the Adolescent Attachment Interview (developed separately from, but overlapping with the AAI), which was classi ed into the three categories of secure, avoidant, and ambivalent. The most striking observations were among the ambivalently attached subgroup, where 7 of 11 had experienced clinically signi cant anorexia nervosa, and 9 of the 11 reported states of distressingly intense feeling. In contrast, none of the secure or avoidant subjects suffered from diagnosed anorexia in adolescence. Daughters reported that their mothers described a similar mixture of anger, longing, and dependence in their own attachments to the subjects grandmothers. Salzman links the adolescents descriptions of attachment to their mothers to the particular type of disturbed feeding pattern pseudo-demand with impatient mothers quoted earlier in this chapter (Ainsworth & Bell, 1969; quoted in Bruch, 1974).

For one, measuring happiness is difficult. You must rely on people s selfreports of their well-being. You can ask for a general report of happiness or you can measure it during the course of daily activities, but these two methods give different results. Also, it s hard to know if one person s very happy is the same as another s very happy, so how do you reliably compare them And what if you are looking at people surveyed during different eras or from different cultures Can you really compare that Then relating reported happiness to income adds difficulty upon difficulty. Money and many other variables are intertwined with each other. For example, the more money you have, the more education you are likely to have, and more education could make you happier. The interlinked variables need to be teased apart. Still, there are some empirical results that have been seen again and again. First, in a given place at a given time, there is a relationship between income and happiness: the more income you have, the happier you are likely to be. Paradoxically, however, once a certain subsistence-level income has been achieved, happiness over time seems to have stayed the same even as countries have become much wealthier and per capita income has increased greatly. This is known as the Easterlin paradox, for economist Richard Easterlin s classic 1974 paper identifying it.* So what can you conclude about the relationship between money and happiness It s probably fair to say the following**:

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