A longitudinal study of domestic water conservation behavior

A 1988 study of a school-linked sample in a metropolitan and a regional urban area established baseline data for knowledge, attitudes, intentions, and behavior with regard to water management and conservation (Murphy, Watson, & Moore, 1991). This paper reports on a 1991 follow-up, utilising both longitudinal and cross-sectional samples of students, teachers and parents, which aimed at identifying changes within the community since the initial study. In addition, the study sought to identify factors influential in change and the extent to which the pattern of relationships between knowledge, attitudes, intentions and behaviors had remained stable over time. The results indicated that there was a move towards greater conservation as measured by the variables studied over the three year period, that media interventions and water costs were perceived as influential in this change, and that reported conserving behavior continued to be better predicted by stated intentions than by knowledge. Little difference in the pattern of intervariable relationships was observed across the time span studied.     

Household composition and optimal commodity taxes

This paper investigates conditions under which demographic variables will have no impact on commodity taxes. We allow nonlinear and nonseparable preferences, a general demographic demand procedure, and a demogrant scheme linked to the number of children. Formulae for demographic revision of tax estimates are presented in a form that can be easily applied, and the only marginal data requirement is the number of children in the household. The paper extends an earlier exercise (Ray 1988) in avoiding the need for equivalence scales, and in using a demogrant scheme that is consistent with current practice in several European countries. The study confirms the robustness of the earlier discussion to the demogrant scheme adopted.I acknowledge, with thanks, the comments of two anonymous referees, but retain responsibility for errors that may remain.     

SPC—a team effort for process improvement across four Area Control Centres

This paper describes an innovative application of statistical process control to the online remote control of the UK's gas transportation networks. The gas industry went through a number of changes in ownership, regulation, access to networks, organization and management culture in the 1990s. The application of SPC was motivated by these changes along with the desire to apply the best industrial statistics theory to practical problems. The work was initiated by a studentship, with the technology gradually being transferred to the industry. The combined efforts of control engineers and statisticians helped develop a novel SPC system. Having set up the control limits, a system was devised to automatically update and publish the control charts on a daily basis. The charts and an associated discussion forum are available to both managers and control engineers throughout the country at their desktop PCs. The paper describes methods of involving people to design first-class systems to achieve continual process improvement. It describes how the traditional benefits of SPC can be realized in a 'distal team working', and 'soft systems', context of four Area Control Centres, controlling a system delivering two thirds of the UK's energy needs.     

A Discrete Time Parametric Model for the Analysis of Failure Time Data

This paper introduces a parametric discrete failure time model which allows a variety of smooth hazard function shapes, including shapes which are not readily available with continuous failure time models. The model is easy to fit, and statistical inference is simple. Further, it is readily extended to allow for differences between subjects while retaining the ease of fit and simplicity of statistical inference. The performance of the discrete time analysis is demonstrated by application to several data sets.     

MAXIMUM LIKELIHOOD ESTIMATION OF GROSS FLOWS

A method of maximum likelihood estimation of gross flows from overlapping stratified sample data is developed. The approach taken is model-based and the EM algorithm is used to solve the estimation problem. Inference is thus based on information from the total sample at each time period. This can be contrasted with the conventional approach to gross flows estimation which only uses information from the overlapping sub-sample. An application to estimation of flows of Australian cropping and livestock industries farms into and out of an “at risk” situation over the period 1979–84 is presented, as well as a discussion of extensions to more complex sampling situations.