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The rst two scenarios depict the impact of AmI dark situations on the individual and the family in their everyday life The impact of the AmI dark situations on the individual is at the micro-level In scenarios 3 and 4, the impact is on a larger societal scale The theft of personal data in scenario 3 affects millions of people Scenario 4 also depicts the societal impact of AmI technologies on privacy, the environment and crowd behaviour Even though the last two scenarios focus on the macro-level, the problems are still related to individuals and families everyday life Therefore, all the four scenarios are very relevant also from this book s viewpoint, although the technological approach (AmI versus mobile) is slightly different Legal analysis The SWAMI project has accomplished an extensive set of legal analyses on their scenarios.

Below, it is mainly referred to their analyses, highlighting issues that are mostly relevant from this book s perspective, but also adding some observations The SWAMI project started the analysis of the legal aspects of AmI by describing the existing relevant European law In the next step, they applied the identi ed legal framework to the SWAMI dark scenarios This exercise resulted in the identi cation of lacunae and problems in the existing European Information Society law Consequently, in a third step, they moved to the development of legal safeguards addressing key pre-identi ed threats and vulnerabilities and policy options [10] SWAMI has highlighted several particularities of the legal regulation of AmI First, the law is only one of the available sets of tools for regulating behaviour; others include social norms, market rules and the architecture of the technology.

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Linear regression, known as least squares estimation, seeks to find the smallest variance between points Linear regression can be used on anything that can be averaged (such as dollars, lifetime value, average order size) Logistic regression uses a technique known as maximum likelihood estimation, which seeks to find a model that gives you the greatest proportion of successes Use logistical regression in situations whose outcomes are characterized as a yes or no; response or nonresponse; or better, same, or worse Neural net is a technique that is good at finding patterns in the data that are nonlinear Its strength is that it can test for all the combinations of variables, not easily detectable through traditional statistical methods, to find positive and negative patterns that determine response There are a few software programs on the market that claim to do.

On the one hand, the architecture of AmI might well make certain legal rules dif cult to enforce (eg data protection obligations,.

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Here, L k are the (normalized) Legendre polynomials, ~ ~ L ~ ~ ~ = 1 for - l k l )0, that L ~ ~ all , 2 are constructed in Example A.44. It is easy to see that l k ( - 1 ) = 0, k = 2 , 3 , . . .. The orthogonality of the Legendre polynomials further yields that

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lowest scores do not differ much from the scores near them, but for Var_D the lowest score of 13 (case 12) appears disconnected from the next higher score of 32, which does not differ much from the next higher score of 34 The z score associated with the raw score of 13 is extreme [z = (13 5052)/971 = 386] Further evidence comes from the box plot in Figure 57 for Var_D where case 12 is identi ed as below the interval containing the rest of the scores In the box plot for Var_A no case has a score outside the interval The box itself is based on the interquartile range the range between the 75th and 25th percentile that contains 50% of the cases The upper and lower borders of the box are called hinges The median is the line through the box.

If the median is off center, there is some skewness to the data The lines outside the box are 15 times the interquartile range from their own hinges That is, the top line is 15 (75th percentile 25th percentile) above the 75th percentile, and the lower line is 15 (75th percentile 25th percentile) below the 25th percentile These two lines are called the upper and lower inner fences, respectively (There can be outer fences, as well, which are three times the interquartile range from their respective hinges, but only if there are very extreme data points) Any score that is above or below the inner fences, such as that for case 12, is likely to be an outlier Researchers often are reluctant to deal with outliers because they feel that the sample should be analyzed as is.

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