# Download Bayesian inference for prevalence and diagnostic test by Hanson T.E., Johnson W.O. PDF

By Hanson T.E., Johnson W.O.

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Sample text

Nevertheless, it seems reasonable that the houses’ age has an influence on their price X14 . 5 reveals again that the data set might consist of two subgroups. But in this case it is not obvious that the subgroups correspond to more expensive or cheaper houses. One can furthermore observe a negative relation between X7 and X8 . This could reflect the way the Boston metropolitan area developed over time: the districts with the newer buildings are farther away from employment centres with industrial facilities.

1 Is the upper extreme always an outlier? 2 Is it possible for the mean or the median to lie outside of the fourths or even outside of the outside bars? 3 Assume that the data are normally distributed N (0, 1). What percentage of the data do you expect to lie outside the outside bars? 4 What percentage of the data do you expect to lie outside the outside bars if we assume that the data are normally distributed N (0, σ 2 ) with unknown variance σ 2 ? S. S. cities? How would the five-number summary of 15 observations of N (0, 1)-distributed data differ from that of 50 observations from the same distribution?

8 Boston Housing 51 Proportion of lower status of the population X13 Of all the variables X13 exhibits the clearest negative relation with X14 —hardly any outliers show up. Taking the square root of X13 and the logarithm of X14 transforms the relation into a linear one. 4 × X11 )/1000 X12 = X12 /100 X13 = X13 X14 = log (X14 ) Taking the logarithm or raising the variables to the power of something smaller than one helps to reduce the asymmetry. This is due to the fact that lower values move further away from each other, whereas the distance between greater values is reduced by these transformations.