"... In PAGE 6: ...ight positions i =1,..., 8 and four durations j =1,2, 3, 4 ( Table2 ), a fairly typical quantity of data for a coastal cliff site. Note that for each time interval, there were some sections that showed no recession at all, indicating that there had been no landslide episodes in that time interval.... In PAGE 7: ...iques. Note from Fig. 4 that the likelihood surface has local maxima, which could prove to be problem- atic for numerical optimisation routines. The maximum likelihood parameter estimates for the data set in Table2 are listed in Table 3. The parameters of the gamma distribution correspond to a mean time between landslides of 51 years and a standard deviation of 4 years.... In PAGE 11: ...cliff falls at the site, and the frequency and severity of storms required to initiate cliff falls. Moreover, the historic data set ( Table2 ) contains no information on the size and frequency of individual cliff falls or on the associated storm conditions, but some informa- tion on individual cliff falls and storm loading will often be available from more intensive recent mon- itoring. The Bayesian approach therefore enables the sparse long-term records of cliff recession to be combined with the higher resolution short-term meas- urements of individual falls.... In PAGE 14: ... The data set reveals cycles of erosion with a periodicity of 6 or 7 years, though a much longer- period cycle is also postulated. The short-term perio- dicity would not be identified in the coarse, but also more typical, sample data shown in Table2 and analysed in this paper. A detailed data set of the type available on the Holderness coast lends itself to analysis as a two-dimensional (longshore distan- ce C2 time) random field (Vanmarcke, 1983).... ..."

"... In PAGE 36: ...0 50 100 150 200 250 300 350 400 450 Cup Clippers Head phones Mobile phone Camera products f i xat i o n d u r at i o n ( m s) non-designer designer Figure 18: Fixation duration for designers and non-designers for different products Fixation count and fixation duration (ms) for all the products during each task were also calculated ( Table3 ). Figure 19 presents the fixation count for different tasks for all the participants grouped as designers and non-designers.... ..."

"... In PAGE 7: ...In Table 1, the fourth column gives the time for manufacturing one cabinet on a single cell. These values are used to calculate the time that each machine requires to manufacture cabinets of each type (see Table3 ), according to a known pipeline workstation formula (provided that the number of operations is divisible by the number of cells). The final column contains a Boolean square matrix that specifies which cabinet types cannot be produced on the same machine.... ..."

"... In PAGE 23: ... This applies both to quits to another job and quits out of the workforce. Table9 reports our estimates of gender differences in the hazard of quitting the current job post-promotion, after controlling for a large number of characteristics. The numbers in Table 9 are hazard ratios rather than coefficients.... In PAGE 23: ...en. This applies both to quits to another job and quits out of the workforce. Table 9 reports our estimates of gender differences in the hazard of quitting the current job post-promotion, after controlling for a large number of characteristics. The numbers in Table9 are hazard ratios rather than coefficients. Therefore a value of unity to... In PAGE 24: ...interpreted as a lower female than male quit rate, while a value greater than unity represents a higher female quit rate. As with the raw data, the results in Table9 reveal that there are positive but insignificantly higher quit rates for promoted women (compared to promoted men) to another job or out of the workforce, and higher quits of unpromoted women (compared to unpromoted men) out of the workforce. In contrast to the raw data, women who have never been promoted during the sample period are less likely to quit to another job than men.... ..."

"... In PAGE 11: ... This table shows whether the measure of switching costs accrued especially to households that visited websites that they do not normally visit. Table 7 shows more results for the attack on Yahoo, and Table8 presents probabilistic differences and duration model results. The most striking result relates to the Yahoo attack.... ..."

"... In PAGE 10: ... This observation is also confirmed by Figure 18, which shows the number of paths explored during Tdown. Table2 lists the median du- rations of Tdown events originated and observed at different tiers. Events observed by the core have shortest durations, which confirms our previous observation (Figure 14).... ..."